iGIE : innovation, investigation and insights · 2023
Deep learning site classification model for automated photodocumentation in upper GI endoscopy (with video).
СтатьяНазвание и аннотация: Перевод пока не подготовлен
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.
Liu Liang Yen, Fetzer Jeffrey R, Coelho-Prabhu Nayantara, Damani Devanshi N, Sivasubramaniam Priyadharshini, Agrawal Upasana, Arunachalam Shivaram P, League John B, Leggett Cadman L
Аннотация
BACKGROUND AND AIMS: Photodocumentation during EGD can be automated and standardized using deep learning (DL) models for anatomic site classification. EGD video data contain a significant number of suboptimal quality image frames for computational analysis (eg, off-center or blurry). We aimed to develop a DL model that extracts high-quality frames from EGD video data for anatomic classification.
METHODS: The photodocumentation algorithm consisted of 2 image filters that extract high-quality image frames (appropriately centered, minimal to no blurriness) classified into 1 of 8 anatomic sites: esophagus, gastroesophageal junction, stomach body, fundus, angularis, antrum, duodenal bulb, and duodenum. Model training, testing, and internal validation were performed using 8231 EGD still images and 26,103 video-derived images with an even split among anatomic sites. Images were independently rated per category by 2 gastroenterologists. External validation was performed using an independent dataset of 2142 EGD still images. Model performance (accuracy, F1 score) for 5 EGD videos (6308 frames) was analyzed using a majority vote strategy across 5, 10, 20, and 30 consecutive frame windows.
RESULTS: Internal testing and external validation for site classification showed overall accuracies of 98.1% and 95.0%, respectively; F1 scores ranged from 90.0% to 99.0% and 92.0% to 97.0% across anatomic sites, respectively. When applied to EGD video data, overall accuracies ranged from 89.7% to 94.8% across sampling window sizes.
CONCLUSIONS: We present a DL model capable of extracting high-quality frames from EGD video data and performing subsequent anatomic site classification with acceptable accuracy, allowing automated photodocumentation for consistent study quality and video indexing for annotated study review.
METHODS: The photodocumentation algorithm consisted of 2 image filters that extract high-quality image frames (appropriately centered, minimal to no blurriness) classified into 1 of 8 anatomic sites: esophagus, gastroesophageal junction, stomach body, fundus, angularis, antrum, duodenal bulb, and duodenum. Model training, testing, and internal validation were performed using 8231 EGD still images and 26,103 video-derived images with an even split among anatomic sites. Images were independently rated per category by 2 gastroenterologists. External validation was performed using an independent dataset of 2142 EGD still images. Model performance (accuracy, F1 score) for 5 EGD videos (6308 frames) was analyzed using a majority vote strategy across 5, 10, 20, and 30 consecutive frame windows.
RESULTS: Internal testing and external validation for site classification showed overall accuracies of 98.1% and 95.0%, respectively; F1 scores ranged from 90.0% to 99.0% and 92.0% to 97.0% across anatomic sites, respectively. When applied to EGD video data, overall accuracies ranged from 89.7% to 94.8% across sampling window sizes.
CONCLUSIONS: We present a DL model capable of extracting high-quality frames from EGD video data and performing subsequent anatomic site classification with acceptable accuracy, allowing automated photodocumentation for consistent study quality and video indexing for annotated study review.