EP4380486A1 - Medizintechnisches system und verfahren zum bereitstellen eines versorgungsvorschlags - Google Patents
Medizintechnisches system und verfahren zum bereitstellen eines versorgungsvorschlagsInfo
- Publication number
- EP4380486A1 EP4380486A1 EP22764326.9A EP22764326A EP4380486A1 EP 4380486 A1 EP4380486 A1 EP 4380486A1 EP 22764326 A EP22764326 A EP 22764326A EP 4380486 A1 EP4380486 A1 EP 4380486A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- care
- supply
- processing device
- data processing
- parameters
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000012545 processing Methods 0.000 claims abstract description 96
- 210000000988 bone and bone Anatomy 0.000 claims abstract description 69
- 239000007943 implant Substances 0.000 claims abstract description 47
- 238000004458 analytical method Methods 0.000 claims description 25
- 230000036541 health Effects 0.000 claims description 22
- 210000003049 pelvic bone Anatomy 0.000 claims description 17
- 238000013179 statistical model Methods 0.000 claims description 12
- 230000003862 health status Effects 0.000 claims description 9
- 238000005516 engineering process Methods 0.000 claims description 6
- 238000013528 artificial neural network Methods 0.000 claims description 5
- 238000010801 machine learning Methods 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000004891 communication Methods 0.000 claims description 4
- 238000010187 selection method Methods 0.000 claims description 4
- 230000008569 process Effects 0.000 claims description 3
- 238000001914 filtration Methods 0.000 claims description 2
- 230000007547 defect Effects 0.000 description 16
- 239000011159 matrix material Substances 0.000 description 13
- 238000000513 principal component analysis Methods 0.000 description 9
- 238000013439 planning Methods 0.000 description 6
- 210000000588 acetabulum Anatomy 0.000 description 5
- 230000008901 benefit Effects 0.000 description 5
- 210000001624 hip Anatomy 0.000 description 5
- 238000002513 implantation Methods 0.000 description 4
- 230000002950 deficient Effects 0.000 description 3
- 230000002349 favourable effect Effects 0.000 description 3
- 230000009471 action Effects 0.000 description 2
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- 230000011218 segmentation Effects 0.000 description 2
- 238000011477 surgical intervention Methods 0.000 description 2
- 238000001356 surgical procedure Methods 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 238000012800 visualization Methods 0.000 description 2
- 238000004873 anchoring Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 239000000316 bone substitute Substances 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000013479 data entry Methods 0.000 description 1
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- 238000010586 diagram Methods 0.000 description 1
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- 230000003287 optical effect Effects 0.000 description 1
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- 230000001575 pathological effect Effects 0.000 description 1
- 230000002980 postoperative effect Effects 0.000 description 1
- 239000003826 tablet Substances 0.000 description 1
- 238000012384 transportation and delivery Methods 0.000 description 1
- 210000000689 upper leg Anatomy 0.000 description 1
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/40—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
- A61B34/10—Computer-aided planning, simulation or modelling of surgical operations
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H15/00—ICT specially adapted for medical reports, e.g. generation or transmission thereof
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H30/00—ICT specially adapted for the handling or processing of medical images
- G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B17/00—Surgical instruments, devices or methods
- A61B2017/00017—Electrical control of surgical instruments
- A61B2017/00203—Electrical control of surgical instruments with speech control or speech recognition
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
- A61B34/10—Computer-aided planning, simulation or modelling of surgical operations
- A61B2034/101—Computer-aided simulation of surgical operations
- A61B2034/102—Modelling of surgical devices, implants or prosthesis
- A61B2034/104—Modelling the effect of the tool, e.g. the effect of an implanted prosthesis or for predicting the effect of ablation or burring
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
- A61B34/10—Computer-aided planning, simulation or modelling of surgical operations
- A61B2034/101—Computer-aided simulation of surgical operations
- A61B2034/105—Modelling of the patient, e.g. for ligaments or bones
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
- A61B34/10—Computer-aided planning, simulation or modelling of surgical operations
- A61B2034/108—Computer aided selection or customisation of medical implants or cutting guides
Definitions
- the present disclosure relates to a medical-technical system for use in supplying a bone with an implant.
- the present disclosure relates to a method for providing a treatment proposal for an upcoming treatment of a bone with an implant.
- the medical-technical system and the method are used in particular when an artificial hip socket is implanted in a human pelvic bone.
- Exemplary applications are in revision surgeries where an existing implant, particularly an artificial acetabulum in a pelvic bone, is removed and replaced with a new implant.
- supply intervention can be viewed in particular as the initially mentioned supply of a patient with an implant.
- the care intervention can in particular include relevant preparatory steps such as an analysis of the current situation and/or planning, the surgical intervention in the actual sense, and preferably a follow-up treatment or analysis of the postoperative condition.
- relevant preparatory steps such as an analysis of the current situation and/or planning, the surgical intervention in the actual sense, and preferably a follow-up treatment or analysis of the postoperative condition.
- bone defects are identified, on the basis of which the surgeon has to make a decision as to which implant and/or which surgical technique is to be used. It is known to classify the bone defects and/or to describe the bone defects in segments.
- DE 10 2018 116 588 A1 describes a medical instrument and a corresponding method.
- an actual state data record of a bone considered to be defective is created.
- a health data set and a planning data set are created mathematically on the basis of the health data set. Instructions from the surgeon with regard to treating the bone are included in the creation of the planning data set, which includes information about characteristic anatomical features of the bone, for example.
- the planning data record can be presented to the operator on a display device.
- the set of instruments includes a medical navigation system and a marking device that can be attached to the bone to define a reference.
- the planning data record can be displayed on the display device in a spatial relationship to the bone, with characteristic landmarks of the bone being assigned to characteristic landmarks in the planning data record.
- the object of the present disclosure is to provide a medical-technical system and a method for providing a treatment proposal that support a user, in particular a surgeon, in the treatment of the bone.
- a medical-technical system or medical device/facility
- a medical system or medical facility/system
- a medical system for use in the supply of a bone with an implant, in particular in the implantation of an artificial hip socket in a human pelvic bone, especially for revision procedures
- system comprises or has a data processing device, a storage device and an information device, the following being stored linked to one another in the storage device, based on a plurality of previous supply interventions, assigned to a respective supply intervention or assignable in an (electronically readable) supply data record is: - parameter information, comprising a plurality of objectifiable parameters that are indicative of a diseased condition of the pelvic bone, riff is from a group of preferably predefined types of care, and - a success information that is indicative of a success of the care intervention intervention, the data processing device is designed and provided that from the care data record (relevant) parameters for (individual) types of care (a
- the data processing device compares parameters for the upcoming medical intervention with the determined (relevant) parameters of the plurality of previous medical interventions for an upcoming medical care intervention in order to determine at least one selected medical care type from the group of medical care types with regard to a successful medical care intervention, and to a user provides the information device with a care suggestion about the at least one selected type of care.
- a discrepancy between the parameters of the upcoming care intervention and the parameter information of the (historical) care data record is preferably calculated, wherein in particular a discrepancy between the parameters of the pending care intervention and the determined relevant parameters of the care data record is calculated.
- This deviation should preferably be within a predetermined tolerance range.
- the medical-technical system or a device for use when treating the bone with the implant, especially for revision interventions has the data processing device, the storage device and the notification device.
- a respective care intervention is assigned or can be assigned in a care data record and stored linked to one another: the parameter information, comprising a number of objectifiable parameters that are indicative of a diseased condition of the bone are; the care information, which is indicative of a type of care carried out for the patient (52) during the care intervention, from a group of preferably predefined types of care; and -
- the success information that is indicative of the success of the supply intervention the data processing device containing the following procedural steps in electronically readable form: - Determination of relevant parameters from the supply data record for the respective supply types using a statistical model, in particular using Principal component analysis, whereby the relevant parameters are decisive for the success of the respective type of care; - for an upcoming supply intervention, comparing the parameters of the upcoming supply intervention with the ascertained relevant
- the data processing device can also cause an execution unit, in particular a CPU, to carry out the above method steps (electronically).
- an execution unit in particular a CPU
- the data processing device is designed and adapted (programmed) according to the disclosure in such a way that it uses a statistical model, in particular principal component analysis, to determine relevant parameters for types of care from the care data record, which are decisive for the success of the respective type of care and they for an upcoming care intervention, compares parameters of the pending care intervention with the determined relevant parameters of the plurality of previous care interventions, with (in particular) a deviation between the parameters of the pending care intervention and the determined relevant parameters being within a predetermined tolerance range, to determine at least one selected type of care from the group of types of care with regard to a successful care intervention, and to provide a user with a care suggestion about the at least one selected type of care at the information device.
- a statistical model in particular principal component analysis
- information about previous care interventions can be stored.
- supply intervention can in particular be a single, individualized supply of a patient who has had an implant inserted, in particular an artificial acetabulum in the pelvic bone.
- a care data record for each care intervention is stored in the memory device.
- the care data record includes the parameter information, which in particular describes a preoperative condition of the bone, the care information about the type of care performed on the patient, and the success information, which was obtained in particular postoperatively.
- the disclosure also incorporates the consideration that experiences made during previous treatment interventions can be used for the upcoming treatment intervention on another patient and future treatment interventions and can be used by the system, in particular in an automated manner, to provide the treatment proposal.
- the parameter information includes a plurality of objectifiable parameters, which are, for example, a result of a quantitative bone defect analysis.
- the data processing device is designed and programmed in such a way that it is based on empirical values from previous care interventions stored in the care data sets at least one selected type of supply is determined from the group of possible types of supply.
- the at least one selected type of care is suggested to the user by means of a care suggestion made available in this regard on the information device.
- the user can accept the fitting proposal made to him and carry out the fitting intervention based on this.
- the user selects a different type of supply from the group of supply types, deviating from the supply proposal.
- the experience gained by surgeons based on the success information for a selected type of care based on the specified parameter information can be used to advantage for future care interventions and can support the user in these care interventions.
- the user can in particular be a natural person, in particular a surgeon.
- the user can, for example, be or include a data processing device to which the fitting proposal is output and which, for example, automatically and/or manually controlled by an operator, controls and/or carries out the fitting intervention at least partially, for example in the sense of a human being -Machine interface.
- the supply data sets can be stored in different ways in the storage device. In particular, it is not necessary for a separate file to be assigned to a respective supply data record, although this can be possible.
- the parameter information, the health care information and the success information of the different health care interventions can be stored together, for example, in a database such that the respective information that is linked to one another and to the health care intervention can be identified at any time.
- the supply data records are stored in matrix form, for example by means of relations in a relational database. Provision can be made for the supply data records to be stored centrally in the storage device or in a decentralized manner, in which case the storage device can be spatially distributed.
- the system includes an analysis device for providing an initial data set that is indicative of an actual condition of the bone, with the data processing device being designed and programmed in such a way that it processes at least some of the parameters for providing the parameter information determined on the basis of the initial data set.
- the output data record is transmitted from the analysis device to the data processing device via a communication interface.
- the data processing device can analyze the initial data set and, for example, segment the bone for the purpose of a quantitative bone defect analysis.
- the analysis device is or includes, for example, an X-ray and/or CT device, with the output data set being or including an X-ray image and/or a CT data set of the current state.
- the data processing device is advantageously designed and programmed in such a way that it removes an implant in the bone that is present in the patient's current condition from the initial data set by calculation.
- an existing implant is removed and replaced with a new implant.
- the output data record of the data processing device for analysis is available free of an implant (i.e. a contribution in the output data record that goes back to the implant).
- the data processing device is preferably designed and programmed in such a way that it calculates a health status data record for the patient based on the initial data record. Provision is preferably made for the data processing device to determine at least some of the parameters by comparing the state of health dataset with the initial dataset or, if available, the initial dataset after arithmetical removal of the implant, as explained above.
- the health status data set is created, for example, with the aid of a statistical shape model, in which the statistically most probable healthy situation of the bone (native situation) is reconstructed.
- a quantitative bone defect analysis can then be carried out, for example, by comparing the situations "health data record” and "initial data record", possibly without an implant.
- the initial data record and/or the health status data record includes, for example, a 3D representation of the bone.
- the parameters of the parameter information can include, for example, at least one of the following: relative and/or absolute bone volume loss in at least one section of the bone; relative and/or absolute new bone formation in at least a portion of the bone;
- the groups of care types differ from one another, for example, by one or more of the following:
- groups of types of supply are provided (also describable as "types of supply”), these can differ from one another in particular on the basis of technically available and/or used solutions.
- types of treatment can be provided: a) press-fit socket as a hemispherical, cement-free socket that is fixed by friction; b) Press-fit cup with augmentation as a cement-free hemispherical cup, which must also be provided with an augmentation and screw fixation due to a bone contact area of less than 50%; c) Support shell as a defect bridging element to be screwed in when there is insufficient contact surface in the remaining bone; d) Patient-specific custom-made product.
- the data processing device is advantageously designed and programmed in such a way that it determines the at least one selected type of treatment in conjunction with a probability of a successful treatment intervention and that the treatment suggestion for the user includes an indication of the probability. In this way, the user receives further, valuable information which he can use to decide on at least one type of supply.
- the probability can be specified, for example, as a threshold probability, with the supply success occurring above a lower probability threshold or occurring up to an upper probability threshold. Alternatively or in addition, it is conceivable to state the probability as a probability interval within which the supply will be successful.
- the data processing device is preferably designed and programmed in such a way that it determines two or more selected supply types from the group of supply types and that it provides the supply suggestion to the information device about the two or more selected supply types. If the data processing device establishes, for example, that based on the parameter information for the treatment intervention to be carried out, more than just one type of treatment is likely to be successful, the treatment proposal can include the specification of two or more types of treatment.
- the treatment proposal favorably includes an indication of the respective probability of a successful treatment intervention for at least two of the selected types of treatment. This supports the user when selecting the type of care for the care intervention to be carried out.
- the data processing device is designed and programmed in such a way that it creates a quantitative relationship between characteristic features of a respective type of treatment and at least some of the parameters of the parameter information, preferably selected parameters, for previous treatment interventions in particular.
- the quantitative relationship can be or form a statistical model in which characteristic features of bone defects within each type of treatment are identified and related to the parameters.
- the data processing device can weight the parameters differently, for example.
- an algorithm can be stored in the data processing device so that it can run and be executed in order to create the quantitative relationship.
- Characteristic features can be identified using the algorithm, for example by means of a PC analysis (PCA, Principal Component Analysis).
- the use of a machine learning algorithm and/or a neural network of the data processing device to create the quantitative relationship is favorable.
- the data processing device is designed and programmed in such a way that it includes the success information for the respective treatment intervention in this quantitative relationship and uses this quantitative relationship to determine the at least one selected treatment type.
- a set of parameters can be identified in the event of a successful previous care intervention, which are within a predetermined or specifiable threshold with parameters of the pending supply match. These parameters can, for example, be identified as predominantly decisive when determining the type of care, so that characteristic features of the type of care can be derived from this. Based on previous experiences, this gives the opportunity to propose the best possible at least one type of care.
- the data processing device is preferably designed to be self-learning and programmed to create the quantitative relationship without the user having to do anything.
- Supply data records for new supply interventions are preferably also stored in the storage device and are taken into account for future supply proposals.
- the system preferably comprises at least one input device for accepting data input by a user after the supply intervention has been completed.
- the data input is preferably not provided immediately after completion, but rather after a predetermined or predeterminable period of time for the patient to get used to it.
- the data processing device is preferably designed and programmed in such a way that it creates the success information based on the data input and stores it in the care data record together with the parameter information and the care information.
- the input device can be at least one portable additional device located at a physical distance from the data processing device, or the at least one additional device comprises the input device, with a user application program being stored on the additional device in an executable manner, and with the data input of the user via a communication link from the additional device can be transmitted to the data processing device.
- the user application program can, for example, be what is known as an app that is stored in an executable manner on the additional device.
- the additional device is, for example, a smartphone or a tablet computer. Inputs on the additional device are transmitted to the data processing device, whereupon the latter creates and stores the success information.
- the above embodiment offers the particular advantage that the patient himself can contribute to the care and refinement of the system using the user application program, without a surgeon being required for this.
- the user data record can be changed later, in particular with regard to the success information.
- data entries for the success information can be replaced and/or supplemented if there is a change in the assessment of the success of the type of care used over time.
- the system can comprise a storage device in which a plurality of medical-technical instruments are stored, each comprising at least one implant, with the data processing device transmitting relevant provision information to the storage device depending on the treatment proposal in order to provide the at least a set of instruments.
- the data processing device can transmit the provision information spontaneously, without any action on the part of the user.
- the user triggers the transmission of the provision information after selecting the type of supply.
- the instrumentation is provided comprising the appropriate implant for the type of care. Information relating to this can be stored in the supply data record, for example.
- the instruments can be accessed via the Implant also include, for example, an insertion tool and / or other surgical instruments.
- the storage device prefferably issues request information for the subsequent delivery of an instrument set if this was requested via the provision information.
- a decentralized data processing device implemented via a cloud can be used.
- the present disclosure also relates to a method.
- a method according to the disclosure for providing a treatment proposal for an upcoming treatment of a bone with an implant, in particular using a medical-technical system of the type mentioned above, comprises:
- Parameter information comprising a plurality of objectifiable parameters that are indicative of a diseased condition of the bone; care information, which is indicative of a care type for the patient during the care intervention, from a group of preferably predefined care types; and - Success information that is indicative of a success of the care intervention; - Determination for the upcoming care intervention, with a data processing device, based on the parameter information provided for this purpose and depending on the linking of the success information with the parameter information of a plurality of previous care interventions, at least one selected type of care from the group of types of care, with regard to a successful care intervention; and - providing a supply proposal for the selected type of supply.
- the disclosure also relates to a computer-implemented (pre)selection method or suggestion method.
- the procedure has the following steps:
- a data processing device containing parameters or parameter information (in particular width of a femur), supply information (in particular type of intervention and/or type of implant) and/or success information (in particular a binary success parameter: yes/no) of the previous care interventions;
- the supply information can in particular have an implant to be used in the supply and/or an instrument that is used in the implantation of the implant.
- a quantitative relationship between the parameter information of the individual historical health care interventions and the success information of the historical health care interventions is preferably determined. It can be determined which parameters are decisive for the success of a fitting intervention.
- the relevant parameters can also be characteristic features for the success of a medical intervention be.
- the quantitative relationship can be determined using a statistical model, in particular a principal component analysis (PCA), or a machine learning algorithm and/or a neural network.
- PCA principal component analysis
- a machine learning algorithm and/or a neural network Through this step, a set of parameters can be determined in particular, which are relevant for the success of the treatment intervention.
- an initial data record of the bone or of the bone with the (defective) implant is preferably read in.
- the initial data set can be a CT data set or an X-ray image with a 3D representation of the bone.
- the desired state data set which is a health state data set
- a statistical shape model for the bone In this case, a statistically probable healthy situation of the bone is preferably calculated.
- the parameters or the parameter information can preferably be determined by comparing the desired state data set (health state data set) with the actual state data set (initial data set). In particular, this can be a difference between the desired state data set and the actual state data set.
- the parameters can be objectifiable at least in part in order to be able to quantitatively detect bone defects.
- the parameters determined by comparing the target status data set with the actual status data set can be filtered in such a way that only those parameters are considered that were classified as relevant based on the historical supply data set, in particular by the statistical model . In other words, only the values of the relevant parameters can be taken into account.
- the differences between the target status data record and the actual status data record, ie the determined parameters of the above treatment intervention, are compared in a next step with the parameter information of the historical treatment data record. In this case, in particular, only the relevant parameters are compared, ie only the filtered parameters.
- a quantity of historical care data is determined in which the parameter information is similar to the corresponding information for the pending care intervention.
- similar should mean that a difference between the individual parameters lies within a predetermined tolerance range.
- the set of historical supply data can in particular be a set of supply types from the historical supply data in which the parameter information (of the relevant parameters) is similar to the parameters of the upcoming supply intervention.
- the amount of historical supply data can in particular be limited to successful historical supply interventions.
- the determined amount of historical supply data can therefore be a set of supply types that have a particularly good chance of success for the determined parameters.
- the particular types of supply may be particularly promising.
- a total of the deviations (for the relevant parameters) can also be determined and a type of care can then be classified as particularly promising if the total of the deviations is particularly small.
- the specific amount of historical supply data can be output to a user in a final step.
- the user can receive a qualified suggestion (for a type of supply from the supply data).
- the determined parameters and a success/failure of a performed care intervention can be added to the care data in order to expand the data set.
- the healthcare information can preferably have a group of healthcare types for a healthcare intervention, the parameter information can have parameters for the healthcare interventions, and the success information can have a success of the preceding healthcare interventions.
- the care data record can link the respective types of care with the parameters and the success of the care intervention.
- the object of the present disclosure is also achieved by a computer-readable storage medium which comprises instructions which, when executed by a computer, cause the latter to carry out the method steps of the method according to one of the above aspects.
- FIG. 1 a schematic representation of a medical technology system according to the disclosure in a preferred embodiment
- FIG. 2 a schematic representation of a memory device of the system from FIG. 1;
- FIG. 3 a schematic representation of a storage device of the system from FIG. 1;
- FIG. 4 an illustration of a bone considered to be diseased with an implant, in particular a pelvic bone with an artificial hip socket, which is to be subjected to a revision operation;
- FIG. 5 a schematic illustration for explaining the mode of operation of the system from FIG. 1;
- FIG. 6 a schematic illustration of a 3D representation of the pelvic bone from FIG. 4 with the implant removed by calculation;
- FIG. 7 a schematic representation of a mathematically determined state of health of the pelvic bone in a 3D representation
- FIG. 8 a schematic representation showing how the system works when a supply intervention is pending.
- FIG. 9 a flow chart of a method according to the disclosure.
- FIG. 1 shows a schematic representation of an advantageous embodiment of the me- medical system.
- the system 10 is suitable for carrying out the method according to the disclosure and advantageous exemplary embodiments of the method. A process flow is described below in connection with the explanation of the functioning of the system 10 .
- the system 10 comprises a data processing device 12, a storage device 14, an input device 16, an indication device 18, an analysis device 20 and a storage device 22.
- the aforementioned components of the system 10 can be positioned centrally, at the same location, or spatially distributed.
- the data processing device 12 can be designed and positioned centrally, for example as a computer.
- the data processing device 12 can be formed by spatially distributed computers or servers, for example by means of a cloud service.
- the memory device 14 can be integrated into the data processing device 12 or can be comprised by it.
- Further input devices 24 can be operatively connected to the system 10, in particular the data processing device 12, and in particular can be coupled in terms of information technology via a communication connection.
- the input devices 24 are in the form of portable additional devices 26 .
- FIG. 1 shows two such additional devices 26.
- the input devices 24 can be part of the system 10.
- the components of the system 10 can be or can be coupled to one another by cable and/or wirelessly in order to exchange information with one another.
- the input devices 16 and/or 24 can be designed in different ways and can include, for example, a keyboard, a computer mouse, a touch-sensitive screen (touch screen) and/or other types of input means. Voice input is also conceivable, for example.
- the indication device 18 is or includes, in particular, an optical display unit 28 for outputting visual indications to a user.
- the analysis device 20 is or comprises a CT device 30, for example.
- Supply data sets 32 for supplying bones to patients in whom the system 10 for data acquisition and data management was used are stored in the storage device 14 .
- Each health care record 32 is associated with a respective health care procedure using the system 10 .
- a respective supply data record 32 includes parameter information 34 for a previous supply intervention, supply information 36 and success information 38 (FIG. 2).
- the information 34, 36, 38 can be stored in the respective supply data record 32 in different ways.
- the supply data records are stored in matrix form, for example in a relational database, with FIG. 5, explained below, showing an exemplary matrix representation of the contents of the supply data records 32 schematically.
- the storage device 22 shown schematically in FIG. 3 is used for storing and managing medical instruments 40.
- a respective instrument 40 includes, in particular, an implant 42 to be used in the care of the bone, in particular an artificial hip socket.
- each set of instruments 40 can include, for example, at least one surgical instrument 44 that is used when implanting the implant 42 .
- FIG. 4 shows a perspective view of a bone 46 to be treated, configured as a human pelvic bone 48.
- An implant 42 is inserted in the pelvic bone 48, in this case an artificial acetabular socket 50.
- the system 10 is used, for example, to treat the pelvic bone 48 , which is regarded as pathological and in particular has bone defects.
- the implant 42 is removed in a revision operation and replaced by a new implant.
- FIGS. 5 to 7 Reference is first made below to FIGS. 5 to 7 for the functioning of the system 10 .
- the system 10 uses the care data records 32 of a plurality of previous care interventions in order to submit a care proposal for a type of care for the care of the patient 52 . is beneficial it when a large number of care interventions are stored in the system 10 and can be used by it. For example, at least approximately 50 supply procedures are advantageous, more preferably at least approximately 200 supply procedures, with a higher number being preferred.
- the parameter information 34, the supply information 36 and the success information 38 are taken into account.
- the parameter information 34 of a respective supply data record 32 includes a plurality of objectifiable parameters which are indicative of a diseased condition of the bone 46 .
- FIG. 5 shows a schematic matrix representation of the plurality of parameters for the previous supply interventions in a matrix representation 54.
- the respective patient 52 is treated according to a treatment type from a group of preferably predefined treatment types.
- FIG. 5 shows this as an example in a matrix display 56, which shows the types of care linked to the previous care interventions.
- the care information 36 of each care data record 32 is indicative of the type of care carried out for the patient 52.
- a group of five different types of care is shown in FIG. 5 as an example.
- success information 38 of a respective supply data record 32 is used.
- a matrix display 58 shows a success assessment for a respective care intervention. Six different classifications for the success information are shown in FIG. 5 as an example.
- the CT device 30 is first used in a particularly preoperative ven data recording 60 creates an output data record 62 of the bone 46 regarded as defective.
- the initial data set 62 contains contributions from the implant 42.
- the initial data set 62 includes in particular a CT data set and/or an X-ray image with a 3D representation of the bone 46.
- the initial data set 62 is indicative of an actual condition of the bone 46 and is used by the data processing device 12 as a basis for providing at least some of the parameters.
- the output data record 62 can be provided to the data processing device 12 by the CT device 30 automatically and/or by a user.
- the data processing device 12 is designed and programmed in such a way that it carries out a segmentation 64 of the output data set 62 .
- contributions that go back to the implant 42 in the initial data set 62 are removed by calculation by the data processing device 12, with an adjusted initial data set 66 being created without contributions from the implant 42.
- this step of computational removal can be omitted and the segmented output data record 62 can be used directly by the data processing device 12.
- FIG. 6 schematically shows a 3D representation of the bone 46 in the adjusted initial data set 66.
- the data processing device 12 calculates a health status data record for the bone 46 of the patient 52 based on the initial data record, in this case the initial data record 66.
- This step is identified by the reference number 68 in FIG. Figure 7 shows a 3D representation of the bone 46 in the health data record 70.
- a statistical shape model for the bone 46 can be used to calculate the health status data set.
- a statistically probable healthy situation of the bone 46 (native situation) is calculated.
- the parameters and thus the parameter information 34 can be provided by comparing the health status data record 70 with the initial data record 66 .
- the parameters can be objectified, at least in part, in order to be able to record bone defects quantitatively.
- Reference number 72 in FIG. 5 identifies the corresponding defect analysis.
- the parameter information 34 is stored in the respective supply data record 32 , as symbolized by the matrix display 54 .
- the user in particular the surgeon, can select a suitable type of care for the patient 52 and take the parameter information 34 into account.
- the chosen supply 74 can be selected from the selection of the group of supply types.
- Reference number 76 designates the selection of a type of care for a respective care intervention, as symbolized according to matrix representation 56 .
- the data processing device 12 is designed to relate the types of supply and the parameter information to one another. This is shown in FIG. 5 using the matrix representations 78 .
- tables are presented as examples, which reflect the respective care interventions and the related parameter information 34 grouped according to the type of care.
- a table 80 can be assigned to each type of supply.
- this visualization is only an example and that, depending on the data structures used in the memory device 14, a different type of storage and/or visualization of the data could be provided.
- the user can make a success assessment 82 for the previous intervention. Data input by the user for specifying the care 74 and the success assessment 82 can be made in particular via the input devices 16 and/or 24 . It can be advantageous if an executable user application program (for example an app) that can be used by the user is stored on the additional devices 26 , via which the data inputs can be received and then transmitted from the additional device 26 to the data processing device 12 . The use of additional devices 26 offers in particular the possibility of involving the patients 52 in the assessment of success 82 .
- the data processing device 12 uses an analysis 84 to create the success information, as shown by way of example using the matrix representation 58 .
- the data processing device 12 is designed and programmed to create a quantitative relationship 86 based on previous supply interventions.
- the quantitative relationship 86 is created in particular between characteristic features of a respective type of supply and at least some of the parameters of the parameter information 34.
- the data processing device 12 includes the respective success information 38 in the quantitative relationship 86.
- the data processing device 12 creates in particular a statistical model that includes information about the main characteristics of bone defects in each type of treatment and information about the success of the selected type of treatment.
- the data processing device 12 is designed and programmed in such a way that it preferably creates the quantitative relationship 86 independently. Accordingly, the data processing device 12 can be designed and programmed to be self-learning, the quantitative relationship 86 without any action of the user to create. For example, a machine learning algorithm and/or a neural network is used to create the relationship 86.
- Reference number 88 shows a diagram of a PC analysis (PCA, Principal Component Analysis) as an example.
- PCA Principal Component Analysis
- the PC analysis can be used to identify the characteristic features of the respective type of supply and is carried out, for example, using the machine learning algorithm mentioned or the neural network.
- the database with the supply data records 32 of the system 10 can be expanded.
- the parameter information 34, the care information 36 and the success information 38 for a respective care intervention can be supplemented, evaluated by the data processing device 12 and used to improve the quantitative relationship 86.
- the parameter information 34 is created based on the defect analysis 72 for the bone 46 to be treated.
- the data processing device 12 can determine at least one selected type of care from the group of types of care.
- the data processing device 12 Since the relationship 86 links success information 38 with parameter information 34 of previous care interventions and the respective type of care, the data processing device 12 is able to determine the selected type of care with regard to a (presumably) successful care intervention. The determination of the at least one type of supply is marked with reference number 90 . Based on this, the data processing device 12 can provide the user with a supply suggestion for the at least one selected type of supply on the notification device 18, as identified by the reference symbol 92 in FIG.
- the disclosure offers the advantage that the care proposal 92 can be submitted based on previous experiences. This experience of preferably well-trained surgeons can be used to advantage for future care interventions, and users can be supported in these care interventions.
- the user in particular the operator, can adopt the treatment suggestion 92 .
- the user can opt for a different type of supply that deviates from the supply proposal.
- the data processing device 12 can transmit relevant provision information to the storage device 22 . Based on this, the storage device 22 can provide the set of instruments 40 with the implant 42 suitable for the type of supply. Information relating to this can be stored in the supply data record 32 . If necessary, the storage facility 22 can automatically trigger a repeat order for the set of instruments 42 that has been issued.
- the treatment suggestion 92 can preferably include an indication of the probability of the successful treatment intervention. Provision can also be made for the data processing device 12 to determine two or more selected types of supply from the group of types of supply.
- the provisioning suggestion 92 may include an indication of the two or more selected provisioning types.
- the fitting proposal includes an indication of the respective probability of a successful fitting procedure for at least two of the selected types of fitting.
- FIG. 9 shows a flowchart of a selection method according to disclosure.
- the procedure has the following steps.
- step S1 a care data record of previous care interventions is taken from a storage device by the data processing device 12, the care data record in particular having parameter information 34, care information 36 and/or success information 38 of the previous care interventions.
- step S2 relevant parameters of the respective types of care, which are decisive for the success of the respective type of care, are determined by the data processing device 12 from the care data record using a statistical model. A correlation between the successful type of care and a selection of (relevant) parameters is determined, which significantly influence the successful type of care.
- the statistical model can be a main component analysis.
- step S3 the data processing device 12 reads in an output data set 62 or an actual state data set.
- the output data record 62 is in particular the CT data record of the bone 46.
- a target status data record or health status data record 70 is calculated by the data processing device 12 on the basis of the actual status data record (output data record 62).
- the health status data set 70 is calculated in particular with the aid of a shaped bone model.
- the data processing device 12 determines differences or discrepancies in parameters/parameter information 34 by comparing the state of health data record 70 and the initial data record 62.
- the determined differences or discrepancies with regard to the relevant parameters determined are filtered by the data processing device 12.
- the filtered differences in the parameters are compared with the corresponding ones from the historically successful treatment interventions.
- a set of historical supply data is determined in which the differences are similar to one another or, in particular, within one
- step S8 the specified quantity of historically successful supply data is output by the notification device 18.
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- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Medical Informatics (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Epidemiology (AREA)
- Surgery (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Biomedical Technology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Data Mining & Analysis (AREA)
- Robotics (AREA)
- Pathology (AREA)
- Databases & Information Systems (AREA)
- Heart & Thoracic Surgery (AREA)
- Molecular Biology (AREA)
- Animal Behavior & Ethology (AREA)
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- Urology & Nephrology (AREA)
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Abstract
Description
Claims
Applications Claiming Priority (2)
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| DE102021120380.5A DE102021120380A1 (de) | 2021-08-05 | 2021-08-05 | Medizintechnisches System und Verfahren zum Bereitstellen eines Versorgungsvorschlags |
| PCT/EP2022/072018 WO2023012310A1 (de) | 2021-08-05 | 2022-08-04 | Medizintechnisches system und verfahren zum bereitstellen eines versorgungsvorschlags |
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| EP4380486A1 true EP4380486A1 (de) | 2024-06-12 |
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| EP (1) | EP4380486A1 (de) |
| JP (1) | JP2024527147A (de) |
| CN (1) | CN117999042A (de) |
| AU (1) | AU2022324701A1 (de) |
| DE (1) | DE102021120380A1 (de) |
| WO (1) | WO2023012310A1 (de) |
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| US8126736B2 (en) * | 2009-01-23 | 2012-02-28 | Warsaw Orthopedic, Inc. | Methods and systems for diagnosing, treating, or tracking spinal disorders |
| US9220570B2 (en) * | 2012-06-29 | 2015-12-29 | Children's National Medical Center | Automated surgical and interventional procedures |
| JP2015531253A (ja) * | 2012-08-31 | 2015-11-02 | スミス アンド ネフュー インコーポレーテッド | 個別患者向けインプラント技術 |
| US20140081659A1 (en) | 2012-09-17 | 2014-03-20 | Depuy Orthopaedics, Inc. | Systems and methods for surgical and interventional planning, support, post-operative follow-up, and functional recovery tracking |
| SG10201707562PA (en) * | 2013-03-15 | 2017-11-29 | Synaptive Medical Barbados Inc | Intramodal synchronization of surgical data |
| JP2017000550A (ja) * | 2015-06-12 | 2017-01-05 | 国立大学法人 東京大学 | 人工関節置換術支援装置及び方法 |
| DE102015118318B4 (de) * | 2015-10-27 | 2018-05-03 | Karl Leibinger Medizintechnik Gmbh & Co. Kg | Automatisierte Generierung von Knochenbehandlungsmitteln |
| US20190010858A1 (en) | 2017-07-10 | 2019-01-10 | GM Global Technology Operations LLC | Controlling engine coolant fluid temperature |
| DE102018116558A1 (de) | 2018-07-09 | 2020-01-09 | Aesculap Ag | Medizintechnisches Instrumentarium und Verfahren |
| US20220110685A1 (en) * | 2019-02-05 | 2022-04-14 | Smith & Nephew, Inc. | Methods for improving robotic surgical systems and devices thereof |
| CN113408174B (zh) * | 2021-06-28 | 2024-07-12 | 大连理工大学 | 骨骼模型构建方法、装置、计算机设备和存储介质 |
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- 2022-08-04 AU AU2022324701A patent/AU2022324701A1/en active Pending
- 2022-08-04 EP EP22764326.9A patent/EP4380486A1/de active Pending
- 2022-08-04 US US18/681,055 patent/US20240371495A1/en active Pending
- 2022-08-04 CN CN202280054356.6A patent/CN117999042A/zh active Pending
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|---|---|
| US20240371495A1 (en) | 2024-11-07 |
| CN117999042A (zh) | 2024-05-07 |
| WO2023012310A1 (de) | 2023-02-09 |
| JP2024527147A (ja) | 2024-07-19 |
| DE102021120380A1 (de) | 2023-02-09 |
| AU2022324701A1 (en) | 2024-03-14 |
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