Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2025
From Pixels to Peristalsis: Comparing Achalasia Diagnosis With Artificial Intelligence With Expert Endoscopists.
СтатьяСравнительное исследованиеНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Kaklamanos Evandros, Kristinsdottir Kristjana, Wittbrodt Matthew, Li Meng, Pitisuttithum Panyavee, Kou Wenjun, Keswani Rajesh N, Carlson Dustin, Etemadi Mozziyar, Pandolfino John E
Аннотация
BACKGROUND & AIMS: Esophageal motility disorders, such as achalasia, require the use of multiple tests to establish a diagnosis. Patients are first assessed with upper endoscopy, but subtle disease indicators are often overlooked during the procedure leading to delayed diagnosis. Artificial intelligence has the potential to prevent these misdiagnoses and serve as an early screening tool to enhance the physician's decision-making.
METHODS: We developed a video-based transformer model capable of detecting achalasia from upper endoscopy videos. Videos were collected from 1203 patients who presented with dysphagia between August 2018 and January 2024. The model performance was compared with a baseline of 2 expert physicians independently reviewing each video and consulting one another for a final consensus decision. A test set of 95 patients was used to evaluate the physician consensus and model performance based on ground truth labels obtained via high-resolution manometry the Chicago Classification v4.0 scheme.
RESULTS: The model attained similar performance to the physician consensus on the test set, achieving an accuracy and F1 of 0.926 and 0.821, respectively, compared with the physicians (0.905 and 0.710, respectively). The model also obtained fewer false negatives, achieving a sensitivity and negative predictive value of 0.800 and 0.947, respectively, compared with the physicians (0.550 and 0.893, respectively). Furthermore, the model's attention mechanism emphasized clinically relevant features such as presence of fluid or food in the esophagus, and a tight lower esophageal sphincter.
CONCLUSIONS: These results indicate that artificial intelligence can leverage upper endoscopy videos to detect achalasia with an accuracy comparable to that of expert endoscopists.
METHODS: We developed a video-based transformer model capable of detecting achalasia from upper endoscopy videos. Videos were collected from 1203 patients who presented with dysphagia between August 2018 and January 2024. The model performance was compared with a baseline of 2 expert physicians independently reviewing each video and consulting one another for a final consensus decision. A test set of 95 patients was used to evaluate the physician consensus and model performance based on ground truth labels obtained via high-resolution manometry the Chicago Classification v4.0 scheme.
RESULTS: The model attained similar performance to the physician consensus on the test set, achieving an accuracy and F1 of 0.926 and 0.821, respectively, compared with the physicians (0.905 and 0.710, respectively). The model also obtained fewer false negatives, achieving a sensitivity and negative predictive value of 0.800 and 0.947, respectively, compared with the physicians (0.550 and 0.893, respectively). Furthermore, the model's attention mechanism emphasized clinically relevant features such as presence of fluid or food in the esophagus, and a tight lower esophageal sphincter.
CONCLUSIONS: These results indicate that artificial intelligence can leverage upper endoscopy videos to detect achalasia with an accuracy comparable to that of expert endoscopists.