Deep Learning and Automatic Detection of Pleomorphic Esophageal Lesions—A Necessary Step for Minimally Invasive Panendoscopy
Background: Capsule endoscopy (CE) improved the digestive tract assessment; yet, its reading burden is substantial. Deep-learning (DL) algorithms were developed for the detection of enteric and gastric lesions. Nonetheless, their application in the esophagus lacks evidence. The study aim was to deve...
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2025-01-01
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author | Miguel Martins Miguel Mascarenhas Maria João Almeida João Afonso Tiago Ribeiro Pedro Cardoso Francisco Mendes Joana Mota Patrícia Andrade Hélder Cardoso Miguel Mascarenhas-Saraiva João Ferreira Guilherme Macedo |
author_facet | Miguel Martins Miguel Mascarenhas Maria João Almeida João Afonso Tiago Ribeiro Pedro Cardoso Francisco Mendes Joana Mota Patrícia Andrade Hélder Cardoso Miguel Mascarenhas-Saraiva João Ferreira Guilherme Macedo |
author_sort | Miguel Martins |
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description | Background: Capsule endoscopy (CE) improved the digestive tract assessment; yet, its reading burden is substantial. Deep-learning (DL) algorithms were developed for the detection of enteric and gastric lesions. Nonetheless, their application in the esophagus lacks evidence. The study aim was to develop a DL model for esophageal pleomorphic lesion (PL) detection. Methods: A bicentric retrospective study was conducted using 598 CE exams. Three different CE devices provided 7982 esophageal frames, including 2942 PL lesions. The data were divided into the training/validation and test groups, in a patient-split design. Three runs were conducted, each with unique patient sets. The sensitivity, specificity, accuracy, positive and negative predictive value (PPV and NPV), area under the conventional receiver operating characteristic curve (AUC-ROC), and precision–recall curve (AUC-PR) were calculated per run. The model’s diagnostic performance was assessed using the median and range values. Results: The median sensitivity, specificity, PPV, and NPV were 75.8% (63.6–82.1%), 95.8% (93.7–97.9%), 71.9% (50.0–90.1%), and 96.4% (94.2–97.6%), respectively. The median accuracy was 93.5% (91.8–93.8%). The median AUC-ROC and AUC-PR were 0.82 and 0.93. Conclusions: This study focused on the automatic detection of pleomorphic esophageal lesions, potentially enhancing the diagnostic yield of this type of lesion, compared to conventional methods. Specific esophageal DL algorithms may provide a significant contribution and bridge the gap for the implementation of minimally invasive CE-enhanced panendoscopy. |
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institution | Kabale University |
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language | English |
publishDate | 2025-01-01 |
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spelling | doaj-art-8aa0ef6f1595488e86da6579d09c64972025-01-24T13:20:33ZengMDPI AGApplied Sciences2076-34172025-01-0115270910.3390/app15020709Deep Learning and Automatic Detection of Pleomorphic Esophageal Lesions—A Necessary Step for Minimally Invasive PanendoscopyMiguel Martins0Miguel Mascarenhas1Maria João Almeida2João Afonso3Tiago Ribeiro4Pedro Cardoso5Francisco Mendes6Joana Mota7Patrícia Andrade8Hélder Cardoso9Miguel Mascarenhas-Saraiva10João Ferreira11Guilherme Macedo12Department of Gastroenterology, São João University Hospital, 4200-437 Porto, PortugalDepartment of Gastroenterology, São João University Hospital, 4200-437 Porto, PortugalDepartment of Gastroenterology, São João University Hospital, 4200-437 Porto, PortugalDepartment of Gastroenterology, São João University Hospital, 4200-437 Porto, PortugalDepartment of Gastroenterology, São João University Hospital, 4200-437 Porto, PortugalDepartment of Gastroenterology, São João University Hospital, 4200-437 Porto, PortugalDepartment of Gastroenterology, São João University Hospital, 4200-437 Porto, PortugalDepartment of Gastroenterology, São João University Hospital, 4200-437 Porto, PortugalDepartment of Gastroenterology, São João University Hospital, 4200-437 Porto, PortugalDepartment of Gastroenterology, São João University Hospital, 4200-437 Porto, PortugalGastroenterology Department, ManopH, Instituto CUF, 4460-188 Porto, PortugalDepartment of Mechanical Engineering, Faculty of Engineering, University of Porto, 4200-465 Porto, PortugalDepartment of Gastroenterology, São João University Hospital, 4200-437 Porto, PortugalBackground: Capsule endoscopy (CE) improved the digestive tract assessment; yet, its reading burden is substantial. Deep-learning (DL) algorithms were developed for the detection of enteric and gastric lesions. Nonetheless, their application in the esophagus lacks evidence. The study aim was to develop a DL model for esophageal pleomorphic lesion (PL) detection. Methods: A bicentric retrospective study was conducted using 598 CE exams. Three different CE devices provided 7982 esophageal frames, including 2942 PL lesions. The data were divided into the training/validation and test groups, in a patient-split design. Three runs were conducted, each with unique patient sets. The sensitivity, specificity, accuracy, positive and negative predictive value (PPV and NPV), area under the conventional receiver operating characteristic curve (AUC-ROC), and precision–recall curve (AUC-PR) were calculated per run. The model’s diagnostic performance was assessed using the median and range values. Results: The median sensitivity, specificity, PPV, and NPV were 75.8% (63.6–82.1%), 95.8% (93.7–97.9%), 71.9% (50.0–90.1%), and 96.4% (94.2–97.6%), respectively. The median accuracy was 93.5% (91.8–93.8%). The median AUC-ROC and AUC-PR were 0.82 and 0.93. Conclusions: This study focused on the automatic detection of pleomorphic esophageal lesions, potentially enhancing the diagnostic yield of this type of lesion, compared to conventional methods. Specific esophageal DL algorithms may provide a significant contribution and bridge the gap for the implementation of minimally invasive CE-enhanced panendoscopy.https://www.mdpi.com/2076-3417/15/2/709capsule endoscopypanendoscopyartificial intelligenceesophageal lesions |
spellingShingle | Miguel Martins Miguel Mascarenhas Maria João Almeida João Afonso Tiago Ribeiro Pedro Cardoso Francisco Mendes Joana Mota Patrícia Andrade Hélder Cardoso Miguel Mascarenhas-Saraiva João Ferreira Guilherme Macedo Deep Learning and Automatic Detection of Pleomorphic Esophageal Lesions—A Necessary Step for Minimally Invasive Panendoscopy Applied Sciences capsule endoscopy panendoscopy artificial intelligence esophageal lesions |
title | Deep Learning and Automatic Detection of Pleomorphic Esophageal Lesions—A Necessary Step for Minimally Invasive Panendoscopy |
title_full | Deep Learning and Automatic Detection of Pleomorphic Esophageal Lesions—A Necessary Step for Minimally Invasive Panendoscopy |
title_fullStr | Deep Learning and Automatic Detection of Pleomorphic Esophageal Lesions—A Necessary Step for Minimally Invasive Panendoscopy |
title_full_unstemmed | Deep Learning and Automatic Detection of Pleomorphic Esophageal Lesions—A Necessary Step for Minimally Invasive Panendoscopy |
title_short | Deep Learning and Automatic Detection of Pleomorphic Esophageal Lesions—A Necessary Step for Minimally Invasive Panendoscopy |
title_sort | deep learning and automatic detection of pleomorphic esophageal lesions a necessary step for minimally invasive panendoscopy |
topic | capsule endoscopy panendoscopy artificial intelligence esophageal lesions |
url | https://www.mdpi.com/2076-3417/15/2/709 |
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