DOP57 Development and validation of a convolutional neural network for the automatic detection of enteric ulcers and erosions in capsule endoscopy: A multicentric study
Afonso , J.(1);Mascarenhas , M.(1);Ribeiro , T.(1);Cardoso , P.(1);Gonçalves , R.(1);Ferreira , J.(2);Andrade , A.P.(1);Mascarenhas Saraiva , M.(3);Cardoso , H.(1);Macedo , G.(1);
(1)Centro Hospitalar Universitário São João, Gastroenterology, Porto, Portugal;(2)FEUP - Faculdade Engenharia Universidade do Porto, Engenharia, Porto, Portugal;(3)ManoPh Gastroenterology Clinic, Gastroenterology, Porto, Portugal;
Background
BackgroundCapsule endoscopy (CE) is the gold-standard for the evaluation of the enteric mucosa in patients with suspected or known inflammatory bowel disease, particularly Crohn’s disease. Ulcers and erosions of the small bowel are common findings and their identification in CE is paramount for an accurate disease stratification.
Several artificial intelligence (AI) algorithms have been developed to aid endoscopists to detect lesions in different endoscopic modalities. With this project we intend to develop and test an AI algorithm for the automatic identification of ulcers and erosions in the small bowel mucosa.
Methods
MethodsA total of 2565 CE exams from two different centers (1483 from São João University Hospital and 1082 from ManopH Gastroenterology Clinic) were used to develop the Convolutional Neural Network (CNN). 55320 frames of the enteric mucosa were obtained, 18396 containing enteric ulcers and erosions, and 36924 containing normal mucosa. 90% of the frames were used to develop the training dataset and 10% were used to test the network. The patients included on the training dataset were excluded from the testing dataset. This patient split brings the technology performance closer to that of a real-life setting. The output provided by the CNN was compared to the classification provided by a consensus of experts.
Results
ResultsOur model was able to automatically detect ulcers and erosions in the enteric mucosa with an accuracy of 93.2%, sensitivity of 90.4% and a specificity of 93.9%. The mean processing time for the validation dataset was 29 seconds (approximately 306 frames/second).
An example of the output obtained after the network application can be seen in Figure 1.
Conclusion
ConclusionThe authors developed a CNN for the automatic identification of enteric ulcers and erosions in CE videos and tested it in AI naïve patients. This represents an evolution in the technology readiness level into a real-life clinical setting, that will surely improve the diagnostic yield of CE exams, which will ultimately translate into better patient care.