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EP-4740174-A1 - ASSISTANCE IN THE DETECTION OF CARDIAC AMYLOIDOSIS USING ARTIFICIAL INTELLIGENCE

EP4740174A1EP 4740174 A1EP4740174 A1EP 4740174A1EP-4740174-A1

Abstract

The invention relates to a method for assisting in the detection of cardiac amyloidosis, the method comprising: - receiving at least one current video obtained by means of echocardiography, the current video comprising current images that each comprise an ultrasound signal and additional information; - a processing phase (Ph) comprising the step of processing each current image of the current video in order to remove the additional information from the current image; - an inference phase (E27) comprising the step of carrying out inference using a previously trained classification model by applying, as input to the classification model, at least one input video obtained from the processed video, the classification model being a convolutional neural network implementing (2+1)D spatiotemporal convolutions.

Inventors

  • STEG, PHILIPPE GABRIEL
  • DUCROCQ, Gregory
  • ELBEZ, Yedid

Assignees

  • Bioquantis

Dates

Publication Date
20260513
Application Date
20240705

Claims (15)

  1. 1. Method for assisting in the detection of cardiac amyloidosis in a patient, implemented in a treatment unit (12) and comprising: - a reception step, consisting of receiving at least one current video (Vc) obtained from an echocardiography performed on the patient, the current video comprising a plurality of current images each comprising an ultrasound signal (5) and additional information (7, 9); - a processing phase (Ph), comprising the step of processing each current image of the current video to remove the additional information from said current image, so as to produce a processed video from the current video; - an inference phase (E27), comprising the step of executing an inference of a classification model, previously trained on a set of training videos comprising cases of cardiac amyloidosis and control cases, by applying as input to the classification model at least one input video obtained from the processed video, the classification model being a convolutional neural network implementing spatiotemporal convolutions of type (2+1) D.
  2. 2. Detection assistance method according to claim 1, said convolutional neural network being based on a ResNet.
  3. 3. Detection assistance method according to one of the preceding claims, in which the inference phase comprises the step of producing, from the processed video, a plurality of image packets, each comprising a first predetermined number of successive images, the image packets forming input videos applied as input to the classification model, the inference phase thus comprising the step of producing a plurality of intermediate predictions each associated with a distinct image packet.
  4. 4. Detection assistance method according to claim 3, in which the image packets are defined successively, each image packet being shifted in time, relative to a previous image packet, by a second predetermined number of images.
  5. 5. Detection assistance method according to one of claims 3 or 4, in which if, for the current video, at least one intermediate prediction, obtained for a packet of images, is a positive prediction, which therefore detects a presence of amyloidosis, and which is associated with a confidence score greater than a predetermined threshold, the inference phase produces a final prediction which is also a positive prediction.
  6. 6. Detection assistance method according to one of the preceding claims, in which the processing phase comprises, for each current image of the current video, the step of acquiring coordinates of reference points (SI, S2, S3) delimiting a useful portion (6) of the current image comprising the ultrasonic signal.
  7. 7. Detection assistance method according to claim 6, in which the processing phase comprises the step (E22) of cropping the current image to retain only a part of the current image passing through the reference points and including the useful portion.
  8. 8. Detection assistance method according to one of claims 6 or 7, in which the additional information comprises graduations (7), the processing phase further comprising the step (E23) of applying a binary mask to the useful portion of the current image to remove said graduations.
  9. 9. Detection assistance method according to one of the preceding claims, in which the additional information comprises an electrical signal representation (9), the processing phase comprising the steps (E24) of detecting in the current image pixels corresponding to the electrical signal representation, and of removing said electrical signal representation by applying a median filter to each of said pixels.
  10. 10. Detection assistance method according to one of the preceding claims, further comprising the step (E28) of superimposing on a current image a heat map obtained from the last convolutional layer of the convolutional neural network.
  11. 11. Ultrasound scanner (13) comprising a processing unit (12) in which the detection assistance method according to one of the preceding claims is implemented.
  12. 12. Computer program comprising instructions which cause the processing unit (12) to execute the steps of the detection assistance method according to one of the claims 1 to 10.
  13. 13. Computer-readable recording medium on which the computer program according to claim 12 is recorded.
  14. 14. Method for learning the classification model of the detection assistance method according to one of claims 1 to 10, implemented in a processing unit (1) and comprising: - a reception step, consisting of receiving a set of reference videos (Vref) obtained from echocardiograms performed on reference patients; - a processing phase, implemented on each reference video, which is the same as that of the detection assistance process; - a training phase (E10), consisting of training the classification model using a set of training videos belonging to the set of reference videos, the set of training videos comprising cases of cardiac amyloidosis and control cases.
  15. 15. Learning method according to claim 14, further comprising the preliminary step of applying, on reference images of the reference videos, another previously trained classification model, to retain only A4c sections among them.

Description

AID TO DETECTION OF CARDIAC AMYLOIDOSIS BY ARTIFICIAL INTELLIGENCE The invention relates to the field of detection of cardiac amyloidosis. BACKGROUND OF THE INVENTION Cardiac amyloidosis is a disease characterized by the presence of insoluble proteins in the heart tissues. It is a severe disease, with a poor prognosis in the absence of appropriate treatment and management. However, this disease is difficult to detect, especially in its early stages. As a result, it was long considered to be very rare, when in reality it is largely underdiagnosed. It therefore seems particularly interesting to develop a diagnostic support system to detect this disease early, especially since we are now seeing the arrival of new treatments that are effective in the early stages of the disease. A diagnostic support system is known that uses an algorithm for detecting cardiac amyloidosis from cardiovascular MRI images (or CMR, for Cardiovascular Magnetic Resonance). However, the examination using the CMR technique is an examination that presents a high cost. This high cost is doubly problematic, because: it complicates the acquisition of a sufficient number of "reference" images, allowing the detection algorithm to be effectively adjusted; it prevents the organization of "massive" detection in the population, which would seem necessary to detect this disease in its early stages. There is also a diagnostic support system that uses the patient's electrocardiogram to try to detect the onset of amyloidosis early. This test is less expensive than CMR. However, the accuracy of the detection is not satisfactory, which limits the interest of the system - or even prevents its deployment. SUBJECT OF THE INVENTION The invention aims to enable cardiac amyloidosis to be detected early, inexpensively and effectively. SUMMARY OF THE INVENTION In order to achieve this goal, a method is proposed for aiding in the detection of cardiac amyloidosis in a patient, implemented in a treatment unit and comprising: - a receiving step, consisting of receiving at least one current video obtained from an echocardiography performed on the patient, the current video comprising a plurality of current images each comprising an ultrasound signal and additional information; - a processing phase, comprising the step of processing each current image of the current video to remove additional information from said current image, so as to produce a processed video from the current video; - an inference phase, comprising the step of executing an inference of a classification model, previously trained on a set of training videos comprising cases of cardiac amyloidosis and control cases, by applying as input to the classification model at least one input video obtained from the processed video, the classification model being a convolutional neural network implementing spatiotemporal convolutions of type (2+1) D. The classification model used is particularly efficient for detecting cardiac amyloidosis, due to its ability to capture very precise spatio-temporal characteristics and to reduce the computational cost by separating spatial and temporal convolution. The implementation of the treatment phase and the use of this classification model allow the very effective detection of cardiac amyloidosis, early on. Echocardiography is also significantly less expensive than CMR, which helps to overcome the disadvantages mentioned above. We further propose a method of assisting detection as previously described, said convolutional neural network being based on a ResNet. We further propose a method for aiding detection as previously described, in which the inference phase comprises the step of producing, from the processed video, a plurality of image packets, each comprising a first predetermined number of successive images, the image packets forming input videos applied as input to the classification model, the inference phase thus comprising the step of producing a plurality of intermediate predictions each associated with a distinct image packet. We further propose a method of aiding detection as previously described, in which the image packets are defined successively, each image packet being shifted in time, relative to a previous frame packet, by a second predetermined number of frames. We further propose a method for aiding detection as previously described, in which if, for the current video, at least one intermediate prediction, obtained for a packet of images, is a positive prediction, which therefore detects a presence of amyloidosis, and which is associated with a confidence score greater than a predetermined threshold, the inference phase produces a final prediction which is also a positive prediction. We further propose a method for aiding detection as previously described, in which the processing phase comprises, for each current image of the current video, the step of acquiring coordinates of reference points delimiting a useful portion of the current image comp