Computer-assisted diagnosis system in digestive endoscopy.
The purpose of this paper is to present an intelligent atlas of indexed endoscopic lesions that could be used in computer-assisted diagnosis as reference data. The development of such a system requires a mix of medical and engineering skills for analyzing and reproducing the cognitive processes that underlie the medical decision-making process. The analysis of both endoscopists experience and endoscopic terminologies developed by professional associations shows that diagnostic reasoning in digestive endoscopy uses a scene-object approach. The objects correspond to the endoscopic findings and the medical context of examination and the scene to the endoscopic diagnosis. According to expert assessment, the classes of endoscopic findings and diagnoses, their primitive characteristics (or indices), and their relationships have been listed. Each class describes an endoscopic finding or diagnosis in an intensive way. The retrieval method is based on a similarity metric that estimates the membership value of the case under investigation and the prototype of the class. A simulation test with randomized objects demonstrates a good classification of endoscopic findings. The correct class is the unique response in 68% of the tested objects, the first of multiple responses in 28%. Four descriptors are shown to be of major importance in the classification algorithm: anatomic location, shape, color, and relief. At the present time, the application database contains approximately 150 endoscopic images and is accessible via Internet. Experiments are in progress with endoscopists for the validation of the system and for the understanding of the similarity between images. The next step will integrate the system in a learning tool for junior endoscopists.
Cauvin
Jean-Michel
JM
Département d'Information Médicale, Centre Hospitalier Universitaire, Brest 29609, France. jean-michel.cauvin@chu-brest.fr
Le Guillou
Clara
C
Solaiman
Basel
B
Robaszkiewicz
Michel
M
Le Beux
Pierre
P
Roux
Christian
C
D000465:Algorithms; D001185:Artificial Intelligence; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D016145:Endoscopy, Digestive System / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity