CN114121237A - Neural image emergency value intelligent early warning system and method - Google Patents
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Abstract
The invention discloses an intelligent early warning system and method for a neural image critical value, belonging to the technical field of medical data processing. The invention is based on the automatic and accurate segmentation and determination of the cerebral hematoma volume of the deep learning technology; a cerebral hemorrhage critical value automatic determination and intelligent reporting system; therefore, intelligent reporting and early warning of the neuroimaging critical value are realized, emergency treatment and neurologists can be assisted to do critical early warning, preliminary diagnosis screening, preoperative assessment and the like of the nervous system critical value, working efficiency and precision are provided, precious time is won for treatment of patients, and further the reduction of the fatality rate and disability rate of the nervous system critical value is hopefully helped.
Description
Technical Field
The invention belongs to the technical field of medical data processing, and particularly relates to an intelligent early warning system and method for a neural image critical value.
Background
Large-area cerebral hemorrhage is the most critical condition of the nervous system, and has high fatality rate and disability rate. After the cerebral hemorrhage patient passes the imaging department-CT examination, the bleeding area is manually estimated by imaging doctors conventionally, and after the judgment that the bleeding area reaches the critical standard, the imaging doctors inform clinicians of reporting critical values through telephone calls, and emergency treatment is performed clinically. The traditional method is difficult to accurately measure the volume of the cerebral hematoma; the critical value reporting link is complicated, and the reporting is missed or delayed due to human factors, so that the optimal rescue opportunity of the patient is missed.
Disclosure of Invention
Aiming at the defects that in the prior art, errors exist in manual estimation of cerebral hematoma, the traditional critical value reporting process is possibly delayed, and treatment of patients is delayed, the invention provides an intelligent neural image critical value early warning system and method.
In order to achieve the purpose, the technical scheme of the invention is as follows:
an intelligent early warning system for a neural image critical value comprises a data acquisition and preprocessing module, a model establishing and training module, a qualitative and quantitative module, a judging module, a critical value reporting module and an early warning information receiving module,
the data acquisition and preprocessing module is used for acquiring an original cerebral hemorrhage CT image set, preprocessing the original cerebral hemorrhage CT image set to acquire the cerebral hemorrhage CT image set, and labeling each cerebral hemorrhage part in the cerebral hemorrhage CT image set to acquire a label set;
the model establishing and training module is used for establishing a U-Net model of the deep neural network, training the U-Net model by utilizing a cerebral hematoma CT image set and a label set, and taking the trained model as an ICH-Unet cerebral hematoma segmentation model;
the qualitative and quantitative module is used for inputting the CT image into an ICH-Unet brain hematoma segmentation model, obtaining a brain hematoma region and obtaining a hematoma volume value;
the judging module is used for classifying and judging the cerebral hemorrhage conditions of different parts according to the current nerve critical value standard and judging whether the patient reaches the critical value standard;
the critical value reporting module is used for reporting the patient information and the early warning information which reach the critical value standard to a corresponding consulting room terminal, and simultaneously sending an early warning short message to the mobile phone of a patient doctor to prompt the patient doctor to perform emergency treatment;
the early warning information receiving module is used for receiving early warning information by the corresponding consulting room terminal, highlighting and prompting the critical value information through the display screen, sending out a buzzing alarm and receiving early warning short messages by clinicians.
Furthermore, the data acquisition and preprocessing module is connected with an image department examination terminal or a CT scanner to acquire an original cerebral hemorrhage CT image.
Further, the patient information includes name, visit number, critical value item, and examination time.
Furthermore, the diagnosis room terminal is arranged in an emergency room, a neurology department outpatient service room and a ward duty room.
The invention also provides an intelligent early warning method for the neural image critical value, which comprises the following steps:
s1: acquiring a cerebral hemorrhage CT image;
s2: constructing an ICH-Unet brain hematoma segmentation model;
s3: obtaining a cerebral hematoma area and a hematoma volume value according to the ICH-Unet cerebral hematoma segmentation model;
s4: classifying and judging the cerebral hemorrhage conditions of different parts according to the current nerve critical value standard, and judging whether the patient reaches the critical value standard; if the critical value standard is met, the step S5 is entered, and if the critical value standard is not met, the routine processing is carried out;
s5: the information of the name, the number of the patient to be treated, the critical value items and the examination time of the patient is collected, the information is reported to a corresponding consulting room terminal through a local area network in a hospital, and meanwhile, an early warning short message is sent to the mobile phone of a doctor to be treated of the patient to prompt the doctor to perform emergency treatment;
s6: after receiving the early warning information, the corresponding consulting room terminal in the hospital highlights the critical value information through the display screen and sends out a buzzing alarm, and the clinician finishes information reception after finding and clicking 'confirmation'.
Further, the construction process of the ICH-Unet brain hematoma segmentation model is as follows:
s201: receiving cerebral hemorrhage CT original data;
s202: manually marking a hematoma area through 3D-Slicer software to serve as an initial training set;
s203: dividing a data set into a training set, a testing set and a verification set;
s204: carrying out data preprocessing and data enhancement;
s205: taking a U-Net model of a deep neural network as a basic structure, carrying out model training, carrying out repeated model test through 10-fold cross validation, and debugging the model to achieve the best effect;
s206: and carrying out model verification to finally obtain an ICH-Unet brain hematoma segmentation model.
Further, the final effect of the ICH-Unet brain hematoma segmentation model is as follows: inputting the cerebral hemorrhage image, automatically judging the hemorrhage part, dividing the hematoma area and obtaining the hematoma volume value.
Has the advantages that: the invention is based on the automatic and accurate determination of the volume of the cerebral hematoma by the deep learning technology; a cerebral hemorrhage critical value automatic determination and intelligent reporting system; therefore, intelligent reporting and early warning of the neuroimaging critical value are realized, emergency treatment and neurologists can be assisted to do critical early warning, preliminary diagnosis screening, preoperative assessment and the like of the nervous system critical value, working efficiency and precision are provided, precious time is won for treatment of patients, and further the reduction of the fatality rate and disability rate of the nervous system critical value is hopefully helped.
Drawings
FIG. 1 is a system flow diagram of the present invention;
FIG. 2 is a flow chart of the ICH-Unet brain hematoma segmentation model construction of the present invention;
FIG. 3 is a diagram of the ICH-Unet brain hematoma auto-segmentation model architecture according to the present invention.
Detailed Description
The invention is illustrated below with reference to specific examples. It will be understood by those skilled in the art that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention in any way.
An intelligent early warning system for a neural image critical value, as shown in fig. 1 and fig. 2, comprises a data acquisition and preprocessing module, a model establishing and training module, a qualitative and quantitative module, a judging module, a critical value reporting module and an early warning information receiving module,
the data acquisition and preprocessing module is used for acquiring an original cerebral hemorrhage CT image set, preprocessing the original cerebral hemorrhage CT image set to acquire the cerebral hemorrhage CT image set, and labeling each cerebral hemorrhage part in the cerebral hemorrhage CT image set to acquire a label set;
the model establishing and training module is used for establishing a U-Net model of the deep neural network, training the U-Net model by utilizing a cerebral hematoma CT image set and a label set, and taking the trained model as an ICH-Unet cerebral hematoma segmentation model;
the qualitative and quantitative module is used for inputting the CT image into an ICH-Unet brain hematoma segmentation model, obtaining a brain hematoma region and obtaining a hematoma volume value;
the judging module is used for classifying and judging the cerebral hemorrhage conditions of different parts according to the current nerve critical value standard and judging whether the patient reaches the critical value standard;
the critical value reporting module is used for reporting the patient information and the early warning information which reach the critical value standard to a corresponding consulting room terminal, and simultaneously sending an early warning short message to the mobile phone of a patient doctor to prompt the patient doctor to perform emergency treatment;
the early warning information receiving module is used for receiving early warning information by the corresponding consulting room terminal, highlighting and prompting the critical value information through the display screen, sending out a buzzing alarm and receiving early warning short messages by clinicians.
The data acquisition and preprocessing module is connected with an image department examination terminal or a CT scanner to acquire an original cerebral hemorrhage CT image.
The patient information includes name, number of visit, critical value item, and examination time.
Wherein, the diagnosis room terminal is arranged in an emergency room, a neurology department outpatient service room and a ward duty room.
An intelligent warning method for a neural image critical value, as shown in fig. 1 and 2, comprises the following steps:
s1: acquiring a cerebral hemorrhage CT image;
s2: constructing an ICH-Unet brain hematoma segmentation model;
s3: obtaining a cerebral hematoma area and a hematoma volume value according to the ICH-Unet cerebral hematoma segmentation model;
s4: classifying and judging the cerebral hemorrhage conditions of different parts according to the current nerve critical value standard, and judging whether the patient reaches the critical value standard; if the critical value standard is met, the step S5 is entered, and if the critical value standard is not met, the routine processing is carried out;
s5: the information of the name, the number of the patient to be treated, the critical value items and the examination time of the patient is collected, the information is reported to a corresponding consulting room terminal through a local area network in a hospital, and meanwhile, an early warning short message is sent to the mobile phone of a doctor to be treated of the patient to prompt the doctor to perform emergency treatment;
s6: after receiving the early warning information, the corresponding consulting room terminal in the hospital highlights the critical value information through the display screen and sends out a buzzing alarm, and the clinician finishes information reception after finding and clicking 'confirmation'.
As shown in fig. 2 and fig. 3, the ICH-Unet brain hematoma segmentation model is constructed as follows:
s201: receiving cerebral hemorrhage CT original data;
s202: manually marking a hematoma area through 3D-Slicer software to serve as an initial training set;
s203: dividing a data set into a training set, a testing set and a verification set;
s204: carrying out data preprocessing and data enhancement;
s205: taking a U-Net model of a deep neural network as a basic structure, carrying out model training, carrying out repeated model test through 10-fold cross validation, and debugging the model to achieve the best effect;
s206: and carrying out model verification to finally obtain an ICH-Unet brain hematoma segmentation model.
From fig. 2, the final effect of the ICH-uet brain hematoma segmentation model is shown: inputting the cerebral hemorrhage image, automatically judging the hemorrhage part, dividing the hematoma area and obtaining the hematoma volume value.
TABLE 1 Critical value criteria for different cerebral hemorrhage sites (established based on current consensus of neurosurgery)
| Cerebral hemorrhage part | Critical value standard (hematoma volume-V) |
| Hemorrhage of the lobe or basal ganglia | V ≥ 30ml |
| Thalamic hemorrhage | V ≥ 15ml |
| Cerebral hemorrhage | V ≥ 10ml |
| Bleeding of brain stem | V ≥ 5ml |
Claims (7)
1. An intelligent early warning system and method for a neural image critical value is characterized by comprising a data acquisition and preprocessing module, a model establishing and training module, a qualitative and quantitative module, a judging module, a critical value reporting module and an early warning information receiving module,
the data acquisition and preprocessing module is used for acquiring an original cerebral hemorrhage CT image set, preprocessing the original cerebral hemorrhage CT image set to acquire the cerebral hemorrhage CT image set, and labeling each cerebral hemorrhage part in the cerebral hemorrhage CT image set to acquire a label set;
the model establishing and training module is used for establishing a U-Net model of the deep neural network, training the U-Net model by utilizing a cerebral hematoma CT image set and a label set, and taking the trained model as an ICH-Unet cerebral hematoma segmentation model;
the qualitative and quantitative module is used for inputting the CT image into an ICH-Unet brain hematoma segmentation model, obtaining a brain hematoma region and obtaining a hematoma volume value;
the judging module is used for classifying and judging the cerebral hemorrhage conditions of different parts according to the current nerve critical value standard and judging whether the patient reaches the critical value standard;
the critical value reporting module is used for reporting the patient information and the early warning information which reach the critical value standard to a corresponding consulting room terminal, and simultaneously sending an early warning short message to the mobile phone of a patient doctor to prompt the patient doctor to perform emergency treatment;
the early warning information receiving module is used for receiving early warning information by the corresponding consulting room terminal, highlighting and prompting the critical value information through the display screen, sending out a buzzing alarm and receiving early warning short messages by clinicians.
2. The neuroimaging crisis value intelligent early warning system and method of claim 1, wherein the data acquisition and preprocessing module is connected with an imaging department examination terminal or a CT scanner to acquire an original cerebral hemorrhage CT image.
3. The neuroimaging crisis value intelligent warning system and method of claim 1, wherein the patient information includes name, number of visits, crisis value item, and examination time.
4. The neuroimaging crisis value intelligent early warning system and method of claim 1, wherein the office terminal is installed in emergency room, neurology department outpatient and ward duty room.
5. An intelligent early warning method for a neural image critical value is characterized by comprising the following steps:
s1: acquiring a cerebral hemorrhage CT image;
s2: constructing an ICH-Unet brain hematoma segmentation model;
s3: obtaining a cerebral hematoma area and a hematoma volume value according to the ICH-Unet cerebral hematoma segmentation model;
s4: classifying and judging the cerebral hemorrhage conditions of different parts according to the current nerve critical value standard, and judging whether the patient reaches the critical value standard; if the critical value standard is met, the step S5 is entered, and if the critical value standard is not met, the routine processing is carried out;
s5: the information of the name, the number of the patient to be treated, the critical value items and the examination time of the patient is collected, the information is reported to a corresponding consulting room terminal through a local area network in a hospital, and meanwhile, an early warning short message is sent to the mobile phone of a doctor to be treated of the patient to prompt the doctor to perform emergency treatment;
s6: after receiving the early warning information, the corresponding consulting room terminal in the hospital highlights the critical value information through the display screen and sends out a buzzing alarm, and the clinician finishes information reception after finding and clicking 'confirmation'.
6. The system and method for intelligent warning of neurological image crisis values according to claim 5, wherein the ICH-uet brain hematoma segmentation model is constructed as follows:
s201: receiving cerebral hemorrhage CT original data;
s202: manually marking a hematoma area through 3D-Slicer software to serve as an initial training set;
s203: dividing a data set into a training set, a testing set and a verification set;
s204: carrying out data preprocessing and data enhancement;
s205: taking a U-Net model of a deep neural network as a basic structure, carrying out model training, carrying out repeated model test through 10-fold cross validation, and debugging the model to achieve the best effect;
s206: and carrying out model verification to finally obtain an ICH-Unet brain hematoma segmentation model.
7. The system and method for intelligent warning of neurological image crisis values according to claims 5 or 6, wherein the final effect of the ICH-Unet brain hematoma segmentation model is: inputting the cerebral hemorrhage image, automatically judging the hemorrhage part, dividing the hematoma area and obtaining the hematoma volume value.
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| CN115954097A (en) * | 2022-09-26 | 2023-04-11 | 深圳市万景数字有限公司 | Medical auxiliary device based on virtual reality technology and control system thereof |
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