Investigation of Usability of Artificial Intelligence Semantic Video Processing Methods in Medicine

Abstract

Aim: In this study, by using appropriate video/image processing techniques and CNN architecture, it is aimed to develop user-friendly software for healthcare professionals with various methods such as detection, identification, classification and tracking of polyps contained in the endoscopic images. 
 Material and Methods: The dataset consisted of 345 images in total. These images are images described and validated by medical doctors (experienced endoscopists) of several classes, consisting of hundreds of images for each class, such as anatomical milestones, pathological findings, or gastrointestinal procedures in the digestive tract. The images were obtained from the web address https://datasets.simula.no/kvasir, which is open source for research and educational purposes. CNN and Max-Margin object detection method (MMOD), one of the deep neural network architectures in the Dlib library, was used in the modeling phase. In the evaluation of model performance, precision, recall, F1-score, average precision (AP), mean average precision (mAP), optimal localization recall precision (oLRP), mean optimal LRP, (moLRP) and intersection over union (IoU) were used. 
 Results: In the implementation of the study, when the previously described steps on the open access video image dataset related to the colonic polyps were performed, all performance metrics examined in the training dataset were 100%, while precision of 98%, recall of 94%, F1-score of 94%, AP of 89% and mAP of 89%, oLRP of 48% and moLRP of %48 were calculated on the testing dataset. 
 Conclusion: Considering the values of the calculated performance criteria, it was found that the proposed system gave successful predictions in the diagnosis of gastrointestinal polyps.

Classification of temporomandibular joint osteoarthritis on cone beam computed tomography images using artificial intelligence system

“…This value ranging from 0 to 1 is used to evaluate detection algorithms. This value is considered true positive only if it is above 0.5 10 . The mAP value obtained for temporomandibular joint segmentation was found to be 0.995.…”

“…This value is considered true positive only if it is above 0.5. 10 The mAP value obtained for temporomandibular joint segmentation was found to be 0.995. The mAP values for the temporomandibular joint osteoarthritis classification are, respectively, 0.934 for the healthy joint, 0.856 for the flattened joint, 0.903 for the joint with osteophytes, 0.948 for the joint with erosion and 0.910 for all classes.…”