Atsushi Inaba1,2, Keisuke Hori1, Yusuke Yoda1,3, Hiroaki Ikematsu1,4, Hiroaki Takano3, Hiroki Matsuzaki3, Yoshiki Watanabe5, Nobuyoshi Takeshita3, Toshifumi Tomioka6, Genichiro Ishii2,7, Satoshi Fujii7, Ryuichi Hayashi6, Tomonori Yano1,3. 1. Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Chiba, Japan. 2. Course of Advanced Clinical Research of Cancer, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo, Japan. 3. Medical Device Innovation Center, National Cancer Center Hospital East, Kashiwa, Chiba, Japan. 4. Division of Science and Technology for Endoscopy, Exploratory Oncology Research & Clinical Trial Center, National Cancer Center East, Kashiwa, Chiba, Japan. 5. Department of Medical Information, National Cancer Center Hospital East, Kashiwa, Chiba, Japan. 6. Department of Head and Neck Surgery, National Cancer Center Hospital East, Kashiwa, Chiba, Japan. 7. Division of Pathology, Exploratory Oncology Research & Clinical Trial Center, National Cancer Center East, Kashiwa, Chiba, Japan.
Abstract
BACKGROUND: There are no published reports evaluating the ability of artificial intelligence (AI) in the endoscopic diagnosis of superficial laryngopharyngeal cancer (SLPC). We presented our newly developed diagnostic AI model for SLPC detection. METHODS: We used RetinaNet for object detection. SLPC and normal laryngopharyngeal mucosal images obtained from narrow-band imaging were used for the learning and validation data sets. Each independent data set comprised 400 SLPC and 800 normal mucosal images. The diagnostic AI model was constructed stage-wise and evaluated at each learning stage using validation data sets. RESULTS: In the validation data sets (100 SLPC cases), the median tumor size was 13.2 mm; flat/elevated/depressed types were found in 77/21/2 cases. Sensitivity, specificity, and accuracy improved each time a learning image was added and were 95.5%, 98.4%, and 97.3%, respectively, after learning all SLPC and normal mucosal images. CONCLUSIONS: The novel AI model is helpful for detection of laryngopharyngeal cancer at an early stage.
BACKGROUND: There are no published reports evaluating the ability of artificial intelligence (AI) in the endoscopic diagnosis of superficial laryngopharyngeal cancer (SLPC). We presented our newly developed diagnostic AI model for SLPC detection. METHODS: We used RetinaNet for object detection. SLPC and normal laryngopharyngeal mucosal images obtained from narrow-band imaging were used for the learning and validation data sets. Each independent data set comprised 400 SLPC and 800 normal mucosal images. The diagnostic AI model was constructed stage-wise and evaluated at each learning stage using validation data sets. RESULTS: In the validation data sets (100 SLPC cases), the median tumor size was 13.2 mm; flat/elevated/depressed types were found in 77/21/2 cases. Sensitivity, specificity, and accuracy improved each time a learning image was added and were 95.5%, 98.4%, and 97.3%, respectively, after learning all SLPC and normal mucosal images. CONCLUSIONS: The novel AI model is helpful for detection of laryngopharyngeal cancer at an early stage.
Authors: Muhammad Adeel Azam; Claudio Sampieri; Alessandro Ioppi; Pietro Benzi; Giorgio Gregory Giordano; Marta De Vecchi; Valentina Campagnari; Shunlei Li; Luca Guastini; Alberto Paderno; Sara Moccia; Cesare Piazza; Leonardo S Mattos; Giorgio Peretti Journal: Front Oncol Date: 2022-06-01 Impact factor: 5.738
Authors: Alberto Paderno; Cesare Piazza; Francesca Del Bon; Davide Lancini; Stefano Tanagli; Alberto Deganello; Giorgio Peretti; Elena De Momi; Ilaria Patrini; Michela Ruperti; Leonardo S Mattos; Sara Moccia Journal: Front Oncol Date: 2021-03-24 Impact factor: 6.244