Improved Object Detection Artificial Intelligence Using the Revised RetinaNet Model for the Automatic Detection of Ulcerations, Vascular Lesions, and Tumors in Wireless Capsule Endoscopy.
Ayako NakadaRyota NiikuraKeita OtaniYusuke KuroseYoshito HayashiKazuya KitamuraHiroyoshi NakanishiSeiji KawanoTestuya HondaKenkei HasataniTetsuya SumiyoshiTsutomu NishidaAtsuo YamadaTomonori AokiTatsuya HaradaTakashi KawaiMitsuhiro FujishiroPublished in: Biomedicines (2023)
The use of computer-aided detection models to diagnose lesions in images from wireless capsule endoscopy (WCE) is a topical endoscopic diagnostic solution. We revised our artificial intelligence (AI) model, RetinaNet, to better diagnose multiple types of lesions, including erosions and ulcers, vascular lesions, and tumors. RetinaNet was trained using the data of 1234 patients, consisting of images of 6476 erosions and ulcers, 1916 vascular lesions, 7127 tumors, and 14,014,149 normal tissues. The mean area under the receiver operating characteristic curve (AUC), sensitivity, and specificity for each lesion were evaluated using five-fold stratified cross-validation. Each cross-validation set consisted of between 6,647,148 and 7,267,813 images from 217 patients. The mean AUC values were 0.997 for erosions and ulcers, 0.998 for vascular lesions, and 0.998 for tumors. The mean sensitivities were 0.919, 0.878, and 0.876, respectively. The mean specificities were 0.936, 0.969, and 0.937, and the mean accuracies were 0.930, 0.962, and 0.924, respectively. We developed a new version of an AI-based diagnostic model for the multiclass identification of small bowel lesions in WCE images to help endoscopists appropriately diagnose small intestine diseases in daily clinical practice.
Keyphrases
- artificial intelligence
- deep learning
- big data
- machine learning
- small bowel
- end stage renal disease
- convolutional neural network
- ejection fraction
- newly diagnosed
- optical coherence tomography
- chronic kidney disease
- clinical practice
- prognostic factors
- physical activity
- gene expression
- electronic health record
- working memory
- patient reported
- psychometric properties
- body composition