BACKGROUND: Because pit pattern classification of colorectal lesions is clinically useful in determining treatment options for colorectal tumors but requires extensive training, we developed a computerized system to automatically quantify and thus classify pit patterns depicted on magnifying endoscopy images. OBJECTIVE: To evaluate the utility and limitations of our automated pit pattern classification system. DESIGN: Retrospective study. SETTING: Department of endoscopy at a university hospital. MAIN OUTCOME MEASUREMENTS: Performance of our automated computer-based system for classification of pit patterns on magnifying endoscopic images in comparison to classification by diagnosis of the 134 regular pit pattern images by an endoscopist. RESULTS: For type I and II pit patterns, the results of discriminant analysis were in complete agreement with the endoscopic diagnoses. Type IIIl was diagnosed in 29 of 30 cases (96.7%) and type IV was diagnosed in 1 case. Twenty-nine of 30 cases (96.7%) were diagnosed as type IV pit pattern. The overall accuracy of our computerized recognition system was 132 of 134 (98.5%). CONCLUSIONS: Our system is best characterized as semiautomated but is a step toward the development of a fully automated system to assist in the diagnosis of colorectal lesions based on classification of pit patterns.
BACKGROUND: Because pit pattern classification of colorectal lesions is clinically useful in determining treatment options for colorectal tumors but requires extensive training, we developed a computerized system to automatically quantify and thus classify pit patterns depicted on magnifying endoscopy images. OBJECTIVE: To evaluate the utility and limitations of our automated pit pattern classification system. DESIGN: Retrospective study. SETTING: Department of endoscopy at a university hospital. MAIN OUTCOME MEASUREMENTS: Performance of our automated computer-based system for classification of pit patterns on magnifying endoscopic images in comparison to classification by diagnosis of the 134 regular pit pattern images by an endoscopist. RESULTS: For type I and II pit patterns, the results of discriminant analysis were in complete agreement with the endoscopic diagnoses. Type IIIl was diagnosed in 29 of 30 cases (96.7%) and type IV was diagnosed in 1 case. Twenty-nine of 30 cases (96.7%) were diagnosed as type IV pit pattern. The overall accuracy of our computerized recognition system was 132 of 134 (98.5%). CONCLUSIONS: Our system is best characterized as semiautomated but is a step toward the development of a fully automated system to assist in the diagnosis of colorectal lesions based on classification of pit patterns.
Authors: Rahul Pannala; Kumar Krishnan; Joshua Melson; Mansour A Parsi; Allison R Schulman; Shelby Sullivan; Guru Trikudanathan; Arvind J Trindade; Rabindra R Watson; John T Maple; David R Lichtenstein Journal: VideoGIE Date: 2020-11-09