CN113409953A - Information processing method, information processing apparatus, storage medium, and electronic device - Google Patents
Information processing method, information processing apparatus, storage medium, and electronic device Download PDFInfo
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Abstract
The application discloses an information processing method, an information processing device, a storage medium and electronic equipment. The method comprises the following steps: obtaining AI diagnostic information; responding to the editing operation aiming at the AI diagnostic information to obtain first diagnostic information; and associating the AI diagnostic information with the first diagnostic information to obtain first associated information. The method and the device can improve rechecking efficiency.
Description
Technical Field
The present application belongs to the field of electronic technologies, and in particular, to an information processing method, an information processing apparatus, a storage medium, and an electronic device.
Background
With the rapid development of AI technology, AI technology has been gradually applied in medical lesion diagnosis. The diagnosis accuracy is always very important, especially for some difficult medical records or some ambiguous medical records. AI diagnosis is a good aid, but it is difficult to replace people who ultimately have to make a judgment on the AI diagnosis. Usually, the physician will perform a diagnostic review on the result of the AI-assisted diagnosis to ensure the accuracy of the diagnosis. In the diagnostic review process, there may be situations where multiple physicians are required to review the AI diagnostic results. However, in the related art, the result of the AI auxiliary diagnosis and the result of the AI auxiliary diagnosis checked by the previous physician are difficult to be obtained by the next physician, which is inconvenient for review and comparison and has low checking efficiency.
Disclosure of Invention
The embodiment of the application provides an information processing method, an information processing device, a storage medium and an electronic device, which can improve rechecking efficiency.
An embodiment of the present application provides an information processing method, including:
obtaining AI diagnostic information;
responding to the editing operation aiming at the AI diagnostic information to obtain first diagnostic information;
and associating the AI diagnostic information with the first diagnostic information to obtain first associated information.
An embodiment of the present application provides an information processing apparatus, including:
the acquisition module is used for acquiring AI diagnostic information;
the editing module is used for responding to the editing operation aiming at the AI diagnostic information to obtain first diagnostic information;
and the association module is used for associating the AI diagnostic information with the first diagnostic information to obtain first associated information.
The embodiment of the present application provides a storage medium, on which a computer program is stored, which, when executed on a computer, causes the computer to execute the flow in the information processing method provided by the embodiment of the present application.
The embodiment of the present application further provides an electronic device, which includes a memory and a processor, where the processor is configured to execute the flow in the information processing method provided in the embodiment of the present application by calling the computer program stored in the memory.
In the embodiment of the application, AI diagnosis information is obtained; responding to the editing operation aiming at the AI diagnostic information to obtain a first diagnostic result; the AI diagnostic information is associated with the first diagnostic result to obtain first associated information, so that other doctors can conveniently look up the first associated information, and the rechecking efficiency is high.
Drawings
The technical solutions and advantages of the present application will become apparent from the following detailed description of specific embodiments of the present application when taken in conjunction with the accompanying drawings.
Fig. 1 is a first flowchart illustration of an information processing method according to an embodiment of the present application.
Fig. 2 is a schematic flowchart of a second information processing method according to an embodiment of the present application.
Fig. 3 is a schematic structural diagram of an information processing apparatus according to an embodiment of the present application.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Referring to the drawings, wherein like reference numbers refer to like elements, the principles of the present application are illustrated as being implemented in a suitable computing environment. The following description is based on illustrated embodiments of the application and should not be taken as limiting the application with respect to other embodiments that are not detailed herein.
Referring to fig. 1, fig. 1 is a first schematic flow chart of an information processing method according to an embodiment of the present application, where the flow may include:
101. AI diagnostic information is obtained.
AI techniques have been increasingly used in medical lesion diagnosis. The diagnosis accuracy is always very important, especially for some difficult medical records or some ambiguous medical records. AI diagnosis is a good aid, but it is difficult to replace people who ultimately have to make a judgment on the AI diagnosis. Usually, the physician will perform a diagnostic review on the result of the AI-assisted diagnosis to ensure the accuracy of the diagnosis. In the diagnostic review process, there may be situations where multiple physicians are required to review the AI diagnostic results.
However, in the related art, it is usually necessary to print out the result of the AI auxiliary diagnosis, a certain doctor rechecks the result of the AI auxiliary diagnosis, and writes the recheck result, and then finds the next doctor, and then delivers the printed result of the AI auxiliary diagnosis and the recheck result written by the same to the next doctor, and the latter doctor refers to or rechecks the result.
In the present embodiment, AI diagnostic information may be acquired. The AI diagnostic information may include an AI diagnostic image, an AI diagnostic result, and the like. The AI diagnostic image may be a CT, MR, or the like scan image marked with the location of the lesion. The AI diagnostic result can be a conclusion made regarding a lesion in the AI diagnostic image, e.g., the AI diagnostic result can describe a type of lesion in the AI diagnostic image.
In some embodiments, the AI diagnostic information may also include AI diagnostic specifications. The AI diagnostic explanation may be a specific explanation made for a lesion in the AI diagnostic image.
For example, the scanned image of CT, MR, etc. may be diagnosed by the AI diagnostic system to mark the lesion location in the scanned image, resulting in an AI diagnostic image. The AI diagnostic system may also output an AI diagnostic description and an AI diagnostic result for the lesion in the AI diagnostic image. For example, assuming that a lesion F1 is present in the AI diagnostic image, the AI diagnostic description may be: the focus F1 has calcification, the area of calcification is over 60%, etc. The AI diagnostic result may be: lesion F1 was identified as an aneurysm.
It should be noted that the above is only an example of AI diagnostic information, and is not intended to limit the present application.
102. The first diagnosis information is obtained in response to an editing operation for the AI diagnosis information.
In this embodiment, after the AI diagnostic information is obtained, it may be presented on the electronic device of the physician D1 so that the physician D1 may review the AI diagnostic information. The first diagnostic information may also be obtained in response to the physician D1 editing operations on the AI diagnostic information.
Wherein the first diagnostic information may include a first diagnostic result.
For example, it is assumed that the AI diagnostic information includes an AI diagnostic image and an AI diagnostic result. When physician D1 determines that the lesion position marker in the AI diagnostic image is correct and the AI diagnostic result is correct, physician D1 may perform an editing operation on its electronic device to input the first diagnostic result, so that the electronic device of physician D1 may obtain the first diagnostic result in response to the editing operation by physician D1 on the AI diagnostic information.
For example, assuming that the AI diagnostic image is a scanned image marked with the position of the lesion F1, the AI diagnostic result is: lesion F1 is determined to be an aneurysm, then the first diagnostic result may be: the location of lesion F1 was correctly marked and lesion F1 was determined to be an aneurysm.
For example, it is assumed that the AI diagnostic information includes an AI diagnostic image and an AI diagnostic result. When the physician D1 cannot determine whether the lesion location marking in the AI diagnostic image is correct and/or cannot determine whether the AI diagnostic result is correct, the physician D1 may perform an editing operation on its electronic device to input the first diagnostic result, so that the electronic device of the physician D1 may obtain the first diagnostic result in response to the editing operation of the physician D1 on the AI diagnostic information.
For example, assuming that the AI diagnostic image is a scanned image marked with the position of the lesion F1, the AI diagnostic result is: lesion F1 is determined to be an aneurysm, then the first diagnostic result may be: the location of lesion F1 is suspected to be correctly marked, and/or lesion F1 is suspected to be an aneurysm.
In some embodiments, the first diagnostic information may include a first diagnostic result and a first diagnostic image.
Such as. It is assumed that the AI diagnostic information includes an AI diagnostic image and an AI diagnostic result. When the physician D1 determines that the lesion position marking in the AI diagnostic image is correct and the AI diagnostic result is correct, but at the same time, another lesion, such as lesion F2, is found in the AI diagnostic image, the physician D1 may perform an editing operation on its electronic device to mark the position of the lesion F2 in the AI diagnostic image, obtain a first diagnostic image, and input the first diagnostic result, so that the electronic device of the physician D1 may obtain the first diagnostic result and the first diagnostic image in response to the editing operation of the physician D1 on the AI diagnostic information.
For example, assuming that the AI diagnostic image is a scanned image marked with the position of the lesion F1, the AI diagnostic result is: lesion F1 is determined to be an aneurysm, then the first diagnostic result may be: the location of lesion F1 was marked correctly and lesion F1 was identified as an aneurysm and lesion F2 as an hemangioma. The first diagnostic image is a scanned image marked with the location of lesion F1 and lesion F2.
For example, it is assumed that the AI diagnostic information includes an AI diagnostic image and an AI diagnostic result. When the physician D1 determines that the lesion position marking error in the AI diagnostic image, and/or the AI diagnostic result is erroneous, the physician D1 may perform an editing operation on his electronic device to remove the marking in the AI diagnostic image, obtain a first diagnostic image, and input the first diagnostic result.
For example, a location of a certain blood vessel appears to be raised, the AI diagnostic system identifies it as a lesion, and assumes that the lesion, F3, is identified as an aneurysm, so that the AI diagnostic image is a scanned image marked with the location of the lesion F3, and the AI diagnostic result is: lesion F3 was identified as an aneurysm. When the physician D1 determines that the lesion location in the AI diagnostic image is marked incorrectly, and/or the AI diagnostic result is incorrect, the first diagnostic image may be an image that is not marked with any lesion. The first diagnostic result may be: the lesion marked in the AI diagnostic image is normal tissue, not an aneurysm. In some embodiments, when physician D1 also finds other lesions in the AI diagnostic image, physician D1 may mark the lesion it finds, say lesion F4, in the AI diagnostic image, resulting in a first diagnostic image, in addition to eliminating the marking of the AI diagnostic system mark in the AI image. When physician D1 is able to determine that lesion F4 is an aneurysm, the first diagnostic result may be: the lesion marked in the AI diagnostic image is normal tissue and is not an aneurysm, and the lesion F4 is determined to be an aneurysm. When physician D1 is unable to determine whether lesion F4 is an aneurysm, the first diagnostic result may be: the lesion marked in the AI diagnostic image is normal tissue and is not an aneurysm, and the lesion F4 is suspected to be an aneurysm.
For example, it is assumed that the AI diagnostic information includes an AI diagnostic image and an AI diagnostic result. When the physician D1 cannot determine whether the lesion position in the AI diagnostic image is marked correctly and/or cannot determine whether the AI diagnostic result is correct and, at the same time, another lesion, such as the lesion F5, is found in the AI diagnostic image, the physician D1 may perform an editing operation on its electronic device to mark the position of the lesion F5 in the AI diagnostic image to obtain a first diagnostic image and input the first diagnostic result, so that the electronic device of the physician D1 may obtain the first diagnostic result and the first diagnostic image in response to the editing operation of the physician D1 on the AI diagnostic information.
For example, assuming that the AI diagnostic image is a scanned image marked with the position of the lesion F1, the AI diagnostic result is: lesion F1 was identified as an aneurysm and physician D1 also marked the location of lesion F5 in the AI diagnostic image, and when physician D1 identified lesion F5 as a hemangioma, the first diagnostic result may be: a location of lesion F1 is suspected to be correctly marked, and/or lesion F1 is suspected to be an aneurysm; lesion F5 was identified as hemangioma; when physician D1 is unable to determine whether lesion F5 is a hemangioma, the first diagnostic result may be: a location of lesion F1 is suspected to be correctly marked, and/or lesion F1 is suspected to be an aneurysm; lesion F5 is suspected to be a hemangioma.
In some embodiments, the first diagnostic information may also include a first diagnostic description. The first diagnostic explanation may be a specific explanation made for the AI diagnostic image and/or the lesion in the first diagnostic image.
103. And associating the AI diagnostic information with the first diagnostic information to obtain first associated information.
In this embodiment, after obtaining the AI diagnostic information and the first diagnostic information, the AI diagnostic information may be associated with the first diagnostic information to obtain first associated information, so that the subsequent physician may review based on the first associated information.
For example, the first correlation information may be sent to the electronic device of physician D2 so that physician D2 may review based on the first correlation information.
In some embodiments, after obtaining the AI diagnostic information, the AI diagnostic information may be uploaded to a disease analysis system, and the disease analysis system may assign a rechecking physician to the AI diagnostic information, such as the rechecking physician including physicians D1, D2, D3, etc., in the sequence of physician D1 → physician D2 → physician D3, so that the AI diagnostic information may be assigned to physician D1 first, physician D1 logs into the disease analysis system to see the AI diagnostic information, and then the physician rechecks the AI diagnostic information and edits in the disease analysis system to obtain the first diagnostic information; after the first diagnosis information is obtained, the first diagnosis information and the AI diagnosis information may be associated to obtain first associated information, and the first associated information is uploaded to the disease analysis system, and the disease analysis system may assign the first associated information to the physician D2, and the physician D2 continues to perform the review, and so on, until the diagnosis is finished.
In the embodiment of the application, AI diagnosis information is obtained; responding to the editing operation aiming at the AI diagnostic information to obtain a first diagnostic result; and associating the AI diagnostic information with the first diagnostic result to obtain first associated information. The first associated information can be easily consulted and compared by other doctors, and if the first associated information is sent to the electronic equipment of other doctors, the other doctors can see the first associated information, so that the other doctors can conveniently consult the first associated information, and the rechecking efficiency is high.
In some embodiments, associating the AI diagnostic information with the first diagnostic information, and after obtaining the first associated information, may further include:
when the type of the (n-1) th diagnosis information is a first type, acquiring the (n-1) th associated information;
responding to the editing operation aiming at the n-1 th associated information to obtain the n-th diagnosis information;
associating the nth diagnostic information with the (n-1) th diagnostic information to obtain nth associated information, wherein n is a positive integer greater than 1;
and repeating the diagnosis steps until the diagnosis is finished.
It should be noted that if there is a suspicious content (including contents of "suspected", "suspicious" and related semantics) in the diagnosis information corresponding to the previous physician, and if the diagnosis result corresponding to the previous physician is that the lesion F6 is suspected to be an aneurysm, it indicates that there is a suspicious content in the diagnosis information corresponding to the previous physician, and if the diagnosis result corresponding to the previous physician is that the lesion F6 is determined to be an aneurysm, it indicates that there is no suspicious content in the diagnosis information corresponding to the previous physician), the next physician needs to perform the review until there is no suspicious content in the diagnosis information corresponding to the next physician. When the diagnostic information corresponding to the previous doctor has no doubtful content, the follow-up doctor does not need to carry out recheck continuously, and the follow-up doctor can only look up the diagnostic information and the AI diagnostic information corresponding to the previous doctor.
Based on the above description, in the present embodiment, the type of the n-1 th diagnostic information can be determined after the n-1 th associated information is obtained. When the (n-1) th diagnosis information has doubtful content, determining that the type of the diagnosis information is a first type; when there is no contents of doubt in the (n-1) th diagnostic information, the type thereof can be determined to be the second type. When the type of the (n-1) th diagnosis information is determined to be the first type, an editing interface can be provided to receive the editing operation of a doctor on the (n-1) th associated information, so that the (n) th diagnosis information is obtained in response to the editing operation; and then, associating the nth diagnostic information with the nth-1 related information to obtain the nth related information. Wherein n is a positive integer greater than 1.
For example, assuming that the first associated information is obtained, the first associated information includes first diagnostic information and AI diagnostic information, and the type of the first diagnostic information is a first type, the first associated information may be obtained and displayed on the electronic device of the physician D2, so that the physician D2 may review the first associated information to obtain the second diagnostic information.
Wherein the second diagnostic information may include a second diagnostic result.
For example, assuming that a lesion F7 is marked in the AI diagnostic image, the first diagnostic information includes a first diagnostic result: lesion F7 is suspected to be an aneurysm, and when physician D2 determines that lesion F7 is an aneurysm based on the AI diagnostic image, it may perform an editing operation on its electronic device to input a second diagnostic result, so that the electronic device of physician D2 may obtain the second diagnostic result in response to the editing operation by physician D2 for the first associated information. For example, the second diagnostic result may be: lesion F7 was identified as an aneurysm.
In some embodiments, the second diagnostic information may include a second diagnostic result and a second diagnostic image.
For example, assuming that a lesion F8 is marked in the AI diagnostic image, the first diagnostic result is: lesion F8 is suspected of being an aneurysm. After reviewing the above information, physician D2 determines that lesion F8 is an aneurysm and physician D2 also finds other lesions in the AI diagnostic image, and assuming lesion F9, physician D2 may perform an editing operation on its electronic device to mark the location of lesion F9 in the AI diagnostic image to obtain a second diagnostic image and input a second diagnostic result, so that the electronic device of physician D2 may obtain the second diagnostic image and the second diagnostic result in response to the second editing operation of physician D2 on the first associated information. For example, when physician D2 is able to determine that lesion F9 is a hemangioma, the second diagnostic result may be: lesion F8 was identified as an aneurysm, lesion F9 was identified as an hemangioma; when physician D2 is unable to determine whether lesion F9 is a hemangioma, the second diagnostic result may be: lesion F identified 8 as an aneurysm and lesion F9 was suspected to be an hemangioma. The second diagnostic image may be a scanned image marked with the locations of lesion F8 and lesion F9.
It should be noted that the above are only a few examples of the second diagnostic information, and are not intended to limit the present application.
After the second diagnosis information is obtained, the second diagnosis information and the first association information may be associated to obtain second association information.
And then, when the type of the second diagnostic information is the first type, continuing the same flow as the flow until the type of the obtained diagnostic information is the second type, and confirming the end of the diagnosis.
In some embodiments, associating the AI diagnostic information with the first diagnostic information, and after obtaining the first associated information, may further include:
when the type of the (n-1) th diagnosis information is a first type, acquiring the (n-1) th associated information;
responding to the editing operation aiming at the n-1 th associated information, updating the n-1 th diagnostic information to obtain the n-th diagnostic information, wherein n is a positive integer larger than 1;
and repeating the diagnosis steps until the diagnosis is finished.
If there is a content of doubt in the diagnosis information corresponding to the previous physician (if the diagnosis result corresponding to the previous physician is that the lesion F10 is suspected to be an aneurysm, this indicates that there is a content of doubt in the diagnosis information corresponding to the previous physician, and if the diagnosis result corresponding to the previous physician is that the lesion F10 is determined to be an aneurysm, this indicates that there is no content of doubt in the diagnosis information corresponding to the previous physician), the next physician needs to continue to perform the review until there is no content of doubt in the diagnosis information corresponding to the next physician. When the diagnostic information corresponding to the previous doctor has no doubtful content, the follow-up doctor does not need to carry out recheck continuously, and the follow-up doctor can only look up the diagnostic information and the AI diagnostic information corresponding to the previous doctor.
Based on the above description, in the present embodiment, the type of the n-1 th diagnostic information can be determined after the n-1 th associated information is obtained. When the (n-1) th diagnosis information has doubtful content, determining that the type of the diagnosis information is a first type; when there is no contents of doubt in the (n-1) th diagnostic information, the type thereof can be determined to be the second type. When the type of the n-1 th diagnosis information is determined to be the first type, an editing interface can be provided to receive the editing operation of the doctor on the n-1 th associated information, so that the n-1 th diagnosis information is updated to obtain the n-th diagnosis information in response to the editing operation. Wherein n is a positive integer greater than 1.
For example, assuming that the first associated information is obtained, the first associated information includes first diagnostic information and IA diagnostic information, and the type of the first diagnostic information is a first type, the first associated information may be obtained and displayed on the electronic device of the physician D2, so that the physician D2 may review the first associated information and update the first diagnostic information to obtain second diagnostic information.
Wherein the second diagnostic information may include a second diagnostic result.
For example, assuming that a lesion F11 is marked in the AI diagnostic image, the first associated information includes a first diagnostic result: lesion F11 is suspected to be an aneurysm, and when physician D2 determines lesion F11 to be an aneurysm based on the AI diagnostic image, it may perform an editing operation on its electronic device to update the first diagnostic information, so that the electronic device of physician D2 may update the first diagnostic information to obtain the second diagnostic information in response to the editing operation by physician D2 for the first associated information. For example, the second diagnostic information may include a second diagnostic result, and the second diagnostic result may be: lesion F11 was identified as an aneurysm.
In some embodiments, the second diagnostic information may include a second diagnostic result and a second diagnostic image.
For example, assuming that a lesion F12 is marked in the AI diagnostic image, the first diagnostic information includes a first diagnostic result: lesion F12 is suspected of being an aneurysm. After reviewing the above information, physician D2 determines that lesion F12 is an aneurysm and physician D2 also finds other lesions in the AI diagnostic image, and assuming lesion F13, physician D2 may perform an editing operation on its electronic device to mark the location of lesion F13 in the AI diagnostic image to obtain a second diagnostic image and update the first diagnostic result, so that the electronic device of physician D2 may update the first diagnostic information in response to the editing operation of physician D2 on the first associated information to obtain the second diagnostic image and the second diagnostic result. For example, when physician D2 is able to determine that lesion F12 is a hemangioma, the second diagnostic result may be: lesion F11 was identified as an aneurysm, lesion F12 was identified as an hemangioma; when physician D2 is unable to determine whether lesion F12 is a hemangioma, the second diagnostic result may be: lesion F11 was identified as an aneurysm and lesion F12 was suspected as an hemangioma. The second diagnostic image may be a scanned image marked with the locations of lesion F11 and lesion F12. The second diagnostic image and the second diagnostic result may constitute second diagnostic information.
For another example, assume that a lesion F13 is marked in the AI diagnostic image, the first diagnostic information includes a first diagnostic result and a first diagnostic image, a lesion F13 and a lesion F14 are marked in the first diagnostic image, and the first diagnostic result is: lesion F13 is suspected of being an aneurysm and lesion F14 is identified as an hemangioma. After reviewing the above information, physician D2 determines that lesion F13 is an aneurysm and physician D2 also finds other lesions in the first diagnostic image, and assuming lesion F15, physician D2 may perform editing operations on its electronic device to mark the location of lesion F15 in the first diagnostic image to obtain a second diagnostic image and update the first diagnostic result, so that the electronic device of physician D2 may update the first diagnostic information in response to the editing operations performed by physician D2 on the first associated information to obtain the second diagnostic image and the second diagnostic result. For example, when physician D2 is able to determine that lesion F15 is atherosclerotic, the second diagnostic result may be: lesion F13 was identified as aneurysm, lesion F14 was identified as hemangioma, and lesion F15 was identified as atherosclerosis; when physician D2 is unable to determine whether lesion F15 is atherosclerotic, the second diagnostic result may be: lesion F13 was identified as an aneurysm, lesion F14 was identified as an hemangioma, and lesion F15 was suspected as an atherosclerotic disease. The second diagnostic image may be a scanned image marked with the locations of lesion F13, lesion F14, and lesion F15. The second diagnostic image and the second diagnostic result may constitute second diagnostic information.
It should be noted that the above are only a few examples of the second diagnostic information, and are not intended to limit the present application.
And then, when the type of the second diagnostic information is the first type, continuing the same flow as the flow until the type of the obtained diagnostic information is the second type, and confirming the end of the diagnosis.
In some embodiments, before obtaining the n-1 th association information, the method may further include:
and determining a preset keyword from the (n-1) th diagnosis information, and marking the preset keyword.
For example, assume that the (n-1) th diagnostic information is first diagnostic information, the first diagnostic information includes a first diagnostic result, and the first diagnostic result is: suspected of being an aneurysm, the type of the first diagnostic information may be determined to be of a first type. Assuming that the preset keywords are "suspected" and "in doubt" and related semantics, the "suspected" in "suspected as aneurysm" may be marked. For example, the "suspected" in the "suspected aneurysm" may be colored or colored shading may be added, or the like may be enlarged.
In some embodiments, the information processing method may further include:
detecting whether preset keywords exist in the n-1 diagnosis information or not;
and if the preset keyword exists in the n-1 th diagnosis information, determining that the n-1 th diagnosis information is the first type of diagnosis information.
In this embodiment, the type of the (n-1) th diagnostic information may be determined after the (n-1) th associated information is obtained. When the (n-1) th diagnosis information has doubtful content, determining that the type of the diagnosis information is a first type; when there is no contents of doubt in the (n-1) th diagnostic information, the type thereof can be determined to be the second type.
For example, assume that the (n-1) th diagnostic information is first diagnostic information, the first diagnostic information includes a first diagnostic result, and the first diagnostic result is: if the lesion F1 is suspected to be an aneurysm and the predetermined keywords are "suspected" or "in doubt" and associated semantics, the type of the first diagnostic information can be determined to be the first type.
For another example, assume that the (n-1) th diagnostic information is first diagnostic information, the first diagnostic information includes a first diagnostic result, and the first diagnostic result is: if the focus F1 is determined to be an aneurysm and the preset keywords are "suspected", "in doubt" and related semantics, the type of the first diagnostic information can be determined to be a second type.
In some embodiments, the information processing method may further include:
and determining the type of the (n-1) th diagnostic information according to the (n-1) th diagnostic information and the pre-trained type judgment model.
To improve the accuracy of determining the type of the (n-1) th diagnostic information. A large amount of diagnostic information can be obtained in advance, and type marking can be carried out on the diagnostic information, wherein the diagnostic information can be marked as a first type or a second type; and then, the labeled diagnostic information is used for training the convolutional neural network to obtain a trained type determination model. After the n-1 th diagnostic information is obtained, the n-1 th diagnostic information can be input into a trained type determination model, and whether the type of the n-1 diagnostic information is a first type or a second type can be directly determined.
In some embodiments, the (n-1) th diagnostic information includes an (n-1) th diagnostic result, and the information processing method may further include:
determining the type of the (n-1) th diagnosis result according to the (n-1) th diagnosis result and the pre-trained type judgment model;
the type of the (n-1) th diagnosis result is determined as the type of the (n-1) th diagnosis information.
When the (n-1) th diagnostic information comprises an (n-1) th diagnostic result and an (n-1) th diagnostic image, if the (n-1) th diagnostic image participates in model training, the model precision may be influenced, in order to further improve the accuracy of determining the type of the (n-1) th diagnostic information, a large number of diagnostic results can be obtained in advance, and the diagnostic results are subjected to type marking, wherein the diagnostic information can be marked as a first type or a second type; and then, the labeled diagnosis results are used for training the convolutional neural network to obtain a trained type determination model. When the (n-1) th diagnostic information is obtained, an (n-1) th diagnostic result can be determined from the (n-1) th diagnostic information, and the (n-1) th diagnostic result can be input into a trained type determination model, so that whether the type of the (n-1) th diagnostic result is a first type or a second type can be determined, and the type of the (n-1) th diagnostic information is the first type or the second type can be determined.
In some embodiments, associating the AI diagnostic information with the first diagnostic information, and after obtaining the first associated information, may further include:
when the type of the n-1 diagnostic information is a second type, acquiring the n-1 associated information;
and displaying the n-1 th associated information.
If there is an in-doubt content in the diagnosis information corresponding to the previous physician (if the diagnosis result corresponding to the previous physician is suspected to be an aneurysm, it indicates that there is an in-doubt content in the diagnosis information corresponding to the previous physician, and if the diagnosis result corresponding to the previous physician is determined to be an aneurysm, it indicates that there is no in-doubt content in the diagnosis information corresponding to the previous physician), the next physician needs to continue to perform the review until there is no in-doubt content in the diagnosis information corresponding to the next physician. When the diagnostic information corresponding to the previous doctor has no doubtful content, the follow-up doctor does not need to carry out recheck continuously, and the follow-up doctor can only look up the diagnostic information and the AI diagnostic information corresponding to the previous doctor.
Based on the above description, in the present embodiment, the type of the n-1 th diagnostic information can be determined after the n-1 th associated information is obtained. When the (n-1) th diagnosis information has doubtful content, determining that the type of the diagnosis information is a first type; when there is no contents of doubt in the (n-1) th diagnostic information, the type thereof can be determined to be the second type. When the type of the (n-1) th diagnosis information is determined to be the second type, the editing interface is not provided, and only the display interface is provided, so that when the display operation of the display interface is received, the (n-1) th associated information is displayed in response to the display operation. Wherein n is a positive integer greater than 1.
Referring to fig. 2, fig. 2 is a schematic flow chart of an information processing method according to an embodiment of the present application, where the flow chart may include:
201. AI diagnostic information is obtained.
In the present embodiment, AI diagnostic information may be acquired. The AI diagnostic information may include an AI diagnostic image, an AI diagnostic result, and the like. The AI diagnostic image may be a CT, MR, or the like scan image marked with the location of the lesion. The AI diagnostic result can be a conclusion made regarding a lesion in the AI diagnostic image, e.g., the AI diagnostic result can describe a type of lesion in the AI diagnostic image.
In some embodiments, the AI diagnostic information may also include AI diagnostic specifications. The AI diagnostic explanation may be a specific explanation made for a lesion in the AI diagnostic image.
For example, the scanned image of CT, MR, etc. may be diagnosed by the AI diagnostic system to mark the lesion location in the scanned image, resulting in an AI diagnostic image. The AI diagnostic system may also output an AI diagnostic description and an AI diagnostic result for the lesion in the AI diagnostic image. For example, assuming that a lesion F1 is present in the AI diagnostic image, the AI diagnostic description may be: the focus F1 has calcification, the area of calcification is over 60%, etc. The AI diagnostic result may be: lesion F1 was identified as an aneurysm.
It should be noted that the above is only an example of AI diagnostic information, and is not intended to limit the present application.
202. The first diagnosis information is obtained in response to an editing operation for the AI diagnosis information.
In this embodiment, after the AI diagnostic information is obtained, it may be presented on the electronic device of the physician D1 so that the physician D1 may review the AI diagnostic information. The first diagnostic information may also be obtained in response to the physician D1 editing operations on the AI diagnostic information.
Wherein the first diagnostic information may include a first diagnostic result.
For example, it is assumed that the AI diagnostic information includes an AI diagnostic image and an AI diagnostic result. When the physician D1 cannot determine whether the lesion location marking in the AI diagnostic image is correct and/or cannot determine whether the AI diagnostic result is correct, the physician D1 may perform an editing operation on its electronic device to input the first diagnostic result, so that the electronic device of the physician D1 may obtain the first diagnostic result in response to the editing operation of the physician D1 on the AI diagnostic information.
For example, assuming that the AI diagnostic image is a scanned image marked with the position of the lesion F1, the AI diagnostic result is: lesion F1 is determined to be an aneurysm, then the first diagnostic result may be: the location of lesion F1 is suspected to be correctly marked, and/or lesion F1 is suspected to be an aneurysm.
203. And associating the AI diagnostic information with the first diagnostic information to obtain first associated information.
In this embodiment, after obtaining the AI diagnostic information and the first diagnostic information, the AI diagnostic information may be associated with the first diagnostic information to obtain first associated information, so that the subsequent physician may review based on the first associated information.
In some embodiments, a link may be generated that may point to the first association information so that a subsequent physician may review based on the link. That is, when the physician clicks on the link, the AI diagnostic information and the first diagnostic information may be presented. In some embodiments, when the AI diagnostic information includes an AI diagnostic result and an AI diagnostic image, and/or when the first diagnostic information includes a first diagnostic result and a first diagnostic image, the AI diagnostic image and/or the first diagnostic image may be subjected to a reduction process to obtain a reduced AI diagnostic image and/or the first diagnostic image, and then, when the physician clicks the link, the AI diagnostic result, the reduced AI diagnostic image, the first diagnostic result, and the reduced first diagnostic image may be presented. The physician can also click on the reduced AI diagnostic image, so that the reduced AI diagnostic image can be magnified to obtain an original-size AI diagnostic image. The physician may also click on the reduced first diagnostic image, and may zoom in on the reduced first diagnostic image to obtain the original size first diagnostic image.
In some embodiments, the link may also be named after the contents of the first diagnostic result, for example, if the first diagnostic result is: lesion F1 is suspected of being an aneurysm, then the link is named "lesion F1 is suspected of being an aneurysm". And, it can also detect whether there is a doubt content in the name of the link, that is, it detects whether there is a preset keyword in the name of the link, if there is a preset keyword, it is determined that there is a doubt content in the name of the link, the preset keyword can be marked, for example, the preset keyword is amplified and marked with color or added with colored shading, etc., if there is no preset keyword, it is determined that there is no doubt content in the name of the link, and it is not possible to do nothing. The preset keywords can be 'suspected', 'in doubt' and related semantics.
204. And detecting whether preset keywords exist in the (n-1) th diagnosis information.
205. And if the preset keyword exists in the n-1 th diagnosis information, determining that the type of the n-1 th diagnosis information is the first type.
206. And when the type of the (n-1) th diagnosis information is the first type, determining a preset keyword from the (n-1) th diagnosis information, and marking the preset keyword.
207. And acquiring the (n-1) th associated information.
208. And obtaining the nth diagnosis information in response to the editing operation aiming at the n-1 th associated information.
209. And correlating the nth diagnostic information with the nth-1 related information to obtain nth related information, wherein n is a positive integer greater than 1.
210. And repeating the diagnosis steps until the diagnosis is finished.
For example, steps 206 through 210 may be: assuming that first associated information is obtained, first diagnosis information in the first associated information includes a first diagnosis result, and the first diagnosis result is: if the lesion F1 is suspected to be an aneurysm and the predetermined keyword is "suspected", "suspected" and related semantic, the type of the first diagnostic information may be determined to be the first type, and then "suspected" in "suspected to be an aneurysm" may be marked. For example, the "suspected" in the "lesion F1 suspected of being an aneurysm" may be colored or a colored shading may be added, or it may be enlarged, etc.
Assuming that the first associated information includes an AI diagnostic image in which the position of the lesion F1 is marked, an AI diagnostic result, and a first diagnostic result: lesion F1 is suspected to be an aneurysm, and the second diagnostic information may include a second diagnostic result, which may be edited on the electronic device of physician D2 to input the second diagnostic result when physician D2 determines that lesion F1 is an aneurysm based on the AI diagnostic image in the first correlation information, so that the electronic device of physician D2 may obtain the second diagnostic result in response to the editing operation by physician D2 for the first correlation information. For example, the second diagnostic result may be: lesion F1 was identified as an aneurysm. Since the type of the second diagnosis result is the second type, it can be determined that the diagnosis is ended.
When the physician D2 cannot determine whether the lesion F1 is an aneurysm based on the AI diagnostic image either, it may perform an editing operation on its electronic device to input a second diagnostic result, so that the electronic device of the physician D2 may obtain the second diagnostic result in response to the editing operation of the physician D2 for the first associated information. For example, the second diagnostic result may be: lesion F1 is suspected of being an aneurysm. Since the type of the second diagnosis result is the first type, the obtained second related information may be continuously transmitted to the electronic device of the physician D3, so that the physician D3 performs a review based on the second related information until the obtained type of the diagnosis information is the second type, and the diagnosis is ended. And associating the second diagnosis information with the first associated information to obtain second associated information.
211. And when the type of the (n-1) th diagnostic information is a second type, detecting whether a showing operation is received.
212. And if the display operation is received, responding to the display operation and displaying the (n-1) th associated information.
For example, steps 211 to 212 may be: assuming that the n-1 th associated information is the second associated information, the n-1 th diagnostic information is the second diagnostic information, and the type of the second diagnostic information is the second type, the diagnosis can be determined to be finished, after the diagnosis is determined to be finished, only the subsequent physicians are allowed to consult the second associated information, the subsequent physicians are not allowed to modify the second associated information, and the like, a display interface may be provided to receive the display operation of the physicians, so that the second associated information is displayed in response to the display operation.
Referring to fig. 3, fig. 3 is a schematic structural diagram of an information processing apparatus according to an embodiment of the present disclosure. The information processing apparatus 300 includes: an acquisition module 301, an editing module 302 and an association module 303.
An obtaining module 301, configured to obtain AI diagnostic information;
an editing module 302, configured to obtain first diagnostic information in response to an editing operation for the AI diagnostic information;
the associating module 303 is configured to associate the AI diagnostic information with the first diagnostic information to obtain first associated information.
In some embodiments, the association module 303 may be configured to: when the type of the n-1 diagnostic information is a first type, acquiring the n-1 associated information; responding to the editing operation aiming at the n-1 th associated information to obtain the n-th diagnosis information; correlating the nth diagnostic information with the nth-1 related information to obtain nth related information, wherein n is a positive integer greater than 1; and repeating the diagnosis steps until the diagnosis is finished.
In some embodiments, the association module 303 may be configured to: when the type of the (n-1) th diagnosis information is a first type, acquiring the (n-1) th associated information; responding to the editing operation aiming at the n-1 th associated information, updating the n-1 th diagnostic information to obtain the n-th diagnostic information, wherein n is a positive integer larger than 1; and repeating the diagnosis steps until the diagnosis is finished.
In some embodiments, the association module 303 may be configured to: and determining a preset keyword from the (n-1) th diagnosis information, and marking the preset keyword.
In some embodiments, the association module 303 may be configured to: detecting whether preset keywords exist in the n-1 diagnosis information or not; and if the preset keyword exists in the n-1 diagnosis information, determining that the type of the n-1 diagnosis information is a first type.
In some embodiments, the association module 303 may be configured to: and determining the type of the n-1 diagnostic information according to the n-1 diagnostic information and a pre-trained type judgment model.
In some embodiments, the association module 303 may be configured to: when the type of the (n-1) th diagnosis information is a second type, detecting whether a display operation is received; and if the display operation is received, responding to the display operation and displaying the n-1 associated information.
Correspondingly, the embodiment of the present application further provides an electronic device, where the electronic device may be a terminal or a server, and the terminal may be a terminal device such as a smart phone, a tablet Computer, a notebook Computer, a touch screen, a game machine, a Personal Computer (PC), a Personal Digital Assistant (PDA), and the like. As shown in fig. 4, fig. 4 is a schematic structural diagram of an electronic device provided in the embodiment of the present application. The electronic device 400 includes a processor 401 having one or more processing cores, a memory 402 having one or more computer-readable storage media, and a computer program stored on the memory 402 and executable on the processor. The processor 401 is electrically connected to the memory 402. Those skilled in the art will appreciate that the electronic device configurations shown in the figures do not constitute limitations of the electronic device, and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
The processor 401 is a control center of the electronic device 400, connects various parts of the whole electronic device 400 by using various interfaces and lines, performs various functions of the electronic device 400 and processes data by running or loading software programs and/or modules stored in the memory 402 and calling data stored in the memory 402, thereby performing overall monitoring of the electronic device 400.
In this embodiment, the processor 401 in the electronic device 400 loads instructions corresponding to processes of one or more application programs into the memory 402 according to the following steps, and the processor 401 runs the application programs stored in the memory 402, so as to implement various functions:
obtaining AI diagnostic information;
responding to the editing operation aiming at the AI diagnostic information to obtain first diagnostic information;
and associating the AI diagnostic information with the first diagnostic information to obtain first associated information.
The above operations can be implemented in the foregoing embodiments, and are not described in detail herein.
Optionally, as shown in fig. 4, the electronic device 400 further includes: touch-sensitive display screen 403, radio frequency circuit 404, audio circuit 405, input unit 406 and power 407. The processor 401 is electrically connected to the touch display screen 403, the radio frequency circuit 404, the audio circuit 405, the input unit 406, and the power source 407. Those skilled in the art will appreciate that the electronic device configuration shown in fig. 4 does not constitute a limitation of the electronic device and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
The touch display screen 403 can be used for displaying a user interface and receiving operation instructions generated by a user acting on the user interface. The touch display screen 403 may include a display panel and a touch panel. The display panel may be used, among other things, to display information entered by or provided to a user and various graphical user interfaces of the electronic device, which may be made up of graphics, text, icons, video, and any combination thereof. Alternatively, the Display panel may be configured in the form of a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like. The touch panel may be used to collect touch operations of a user on or near the touch panel (for example, operations of the user on or near the touch panel using any suitable object or accessory such as a finger, a stylus pen, and the like), and generate corresponding operation instructions, and the operation instructions execute corresponding programs. Alternatively, the touch panel may include two parts, a touch detection device and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor 401, and can receive and execute commands sent by the processor 401. The touch panel may overlay the display panel, and when the touch panel detects a touch operation thereon or nearby, the touch panel may transmit the touch operation to the processor 401 to determine the type of the touch event, and then the processor 401 may provide a corresponding visual output on the display panel according to the type of the touch event. In the embodiment of the present application, the touch panel and the display panel may be integrated into the touch display screen 403 to realize input and output functions. However, in some embodiments, the touch panel and the touch panel can be implemented as two separate components to perform the input and output functions. That is, the touch display screen 403 may also be used as a part of the input unit 406 to implement an input function.
The rf circuit 404 may be used for transceiving rf signals to establish wireless communication with a network device or other electronic devices via wireless communication, and for transceiving signals with the network device or other electronic devices.
The audio circuit 405 may be used to provide an audio interface between the user and the electronic device through a speaker, microphone. The audio circuit 405 may transmit the electrical signal converted from the received audio data to a speaker, and convert the electrical signal into a sound signal for output; on the other hand, the microphone converts the collected sound signal into an electrical signal, which is received by the audio circuit 405 and converted into audio data, which is then processed by the audio data output processor 401 and then transmitted to, for example, another electronic device via the rf circuit 404, or the audio data is output to the memory 402 for further processing. The audio circuit 405 may also include an earbud jack to provide communication of a peripheral headset with the electronic device.
The input unit 406 may be used to receive input numbers, character information, or user characteristic information (e.g., fingerprint, iris, facial information, etc.), and to generate keyboard, mouse, joystick, optical, or trackball signal inputs related to user settings and function control.
The power supply 407 is used to power the various components of the electronic device 400. Optionally, the power source 407 may be logically connected to the processor 401 through a power management system, so as to implement functions of managing charging, discharging, power consumption management, and the like through the power management system. The power supply 407 may also include one or more dc or ac power sources, recharging systems, power failure detection circuitry, power converters or inverters, power status indicators, or any other component.
Although not shown in fig. 4, the electronic device 400 may further include a camera, a sensor, a wireless fidelity module, a bluetooth module, etc., which are not described in detail herein.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
As can be seen from the above, the electronic device provided in this embodiment obtains the AI diagnostic information; responding to the editing operation aiming at the AI diagnostic information to obtain first diagnostic information; the AI diagnostic information is associated with the first diagnostic information to obtain first associated information, so that other doctors can conveniently look up the first associated information, and the rechecking efficiency is high.
It will be understood by those skilled in the art that all or part of the steps of the methods of the above embodiments may be performed by instructions or by associated hardware controlled by the instructions, which may be stored in a computer readable storage medium and loaded and executed by a processor.
To this end, the present application provides a computer-readable storage medium, in which a plurality of computer programs are stored, where the computer programs can be loaded by a processor to execute the steps in the control method according to any one of the techniques provided in the present application. For example, the computer program may perform the steps of:
obtaining AI diagnostic information; responding to the editing operation aiming at the AI diagnostic information to obtain first diagnostic information; and associating the AI diagnostic information with the first diagnostic information to obtain first associated information.
The above operations can be implemented in the foregoing embodiments, and are not described in detail herein.
Wherein the storage medium may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.
Since the computer program stored in the storage medium can execute the steps in any information processing method provided in the embodiments of the present application, the beneficial effects that can be achieved by any information processing method provided in the embodiments of the present application can be achieved, and detailed descriptions are omitted here for the foregoing embodiments.
The foregoing detailed description is directed to an information processing method, an information processing apparatus, a storage medium, and an electronic device provided in the embodiments of the present application, and specific examples are applied in the present application to explain the principles and implementations of the present application, and the descriptions of the foregoing embodiments are only used to help understand the method and the core ideas of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. An information processing method characterized by comprising:
obtaining AI diagnostic information;
responding to the editing operation aiming at the AI diagnostic information to obtain first diagnostic information;
and associating the AI diagnostic information with the first diagnostic information to obtain first associated information.
2. The information processing method according to claim 1, wherein, after the associating the AI diagnostic information with the first diagnostic information to obtain first associated information, the method further comprises:
when the type of the n-1 diagnostic information is a first type, acquiring the n-1 associated information;
responding to the editing operation aiming at the n-1 th associated information to obtain the n-th diagnosis information;
correlating the nth diagnostic information with the nth-1 related information to obtain nth related information, wherein n is a positive integer greater than 1;
and repeating the diagnosis steps until the diagnosis is finished.
3. The information processing method according to claim 1, wherein, after the associating the AI diagnostic information with the first diagnostic information to obtain first associated information, the method further comprises:
when the type of the (n-1) th diagnosis information is a first type, acquiring the (n-1) th associated information;
responding to the editing operation aiming at the n-1 th associated information, updating the n-1 th diagnostic information to obtain the n-th diagnostic information, wherein n is a positive integer larger than 1;
and repeating the diagnosis steps until the diagnosis is finished.
4. The information processing method according to claim 2 or 3, wherein before the acquiring the n-1 th association information, further comprising:
and determining a preset keyword from the (n-1) th diagnosis information, and marking the preset keyword.
5. The information processing method according to claim 4, characterized by further comprising:
detecting whether preset keywords exist in the n-1 diagnosis information or not;
and if the preset keyword exists in the n-1 diagnosis information, determining that the type of the n-1 diagnosis information is a first type.
6. The information processing method according to claim 4, characterized by further comprising:
and determining the type of the n-1 diagnostic information according to the n-1 diagnostic information and a pre-trained type judgment model.
7. The information processing method according to claim 2 or 3, characterized by further comprising:
when the type of the (n-1) th diagnosis information is a second type, detecting whether a display operation is received;
and if the display operation is received, responding to the display operation and displaying the n-1 associated information.
8. An information processing apparatus characterized by comprising:
the acquisition module is used for acquiring AI diagnostic information;
the editing module is used for responding to the editing operation aiming at the AI diagnostic information to obtain first diagnostic information;
and the association module is used for associating the AI diagnostic information with the first diagnostic information to obtain first associated information.
9. A storage medium, characterized in that a computer program is stored therein, which when run on a computer, causes the computer to execute an information processing method according to any one of claims 1 to 7.
10. An electronic device, characterized in that the electronic device comprises a processor and a memory, wherein a computer program is stored in the memory, and the processor is configured to execute the information processing method according to any one of claims 1 to 7 by calling the computer program stored in the memory.
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