Introduction
Device-assisted enteroscopy (DAE) allows deep exploration of the gastrointestinal (GI) tract, combining its diagnostic abilities with tissue sampling and the application of endoscopic therapy. Although the application of artificial intelligence (AI) is growing exponentially in various imaged-based gastroenterology procedures, there is still a lack of evidence of the AI technical feasibility and clinical applicability of DAE. Convolutional Neural Networks (CNN) are a multi-layer AI architecture with high performance levels for image analysis. This study aimed to develop and test a multi-brand CNN-based algorithm for automatically detecting protruding lesions in DAE images.
Aims & Methods
A unicentric retrospective study was conducted for the development of a CNN, based on a total of 258 DAE exams. DAE was performed by two experienced endoscopists using the double-balloon enteroscopy system Fujifilm EN-580T (n = 152), the single-balloon enteroscopy system Olympus EVIS EXERA II SIF-Q180 (n = 98) and the Olympus PowerSpiral Motorized Enteroscope PSF-1 (n = 8). A total of 32043 images from small bowel were included, 3162 images containing protruding lesions (polyps, epithelial tumors, subepithelial lesions and nodules). The remaining images showed normal mucosa. A training dataset comprising 80% of the total pool of images was used for development of the network. The output provided by the network was compared to a consensus classification provided. Our primary outcome measures were sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and an area under the curve precision–recall curve (AUC-PR).
Results
The CNN model for automatic detection of GI protruding lesions identified these lesions with a sensitivity of 92.9%, a specificity of 99.0%, positive and negative predictive values of 89.6% and 90.2%, respectively. The algorithm’s accuracy was 98.4%. The AUC-PR was 1.00. The CNN analyzed images at a rate of approximately 260 frames per second, enabling AI live application in a real-life clinical setting in DAE.
Conclusion
The authors have developed a pioneer combined deep learning system for automatic detection of protruding lesions in different segments of the gastrointestinal tract. To the best of our knowledge, this is the first study regarding the automatic multi-brand panendoscopic detection using Olympus PowerSpiral Motorized Enteroscope, overcoming a relevant interoperability challenge. Our results highlight that using a CNN to detect protruding lesions is associated with high overall accuracy. The development of these tools may enhance the diagnostic yield of device-assisted enteroscopy in the setting of AI application in digestive endoscopy, particularly for panendoscopic evaluation.
References
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- Mascarenhas, M.; Afonso, J.; Ribeiro, T.; Andrade, P.; Cardoso, H.; Macedo, G. The Promise of Artificial Intelligence in Digestive Healthcare and the Bioethics Challenges It Presents. Medicina 2023, 59, 790. https://doi.org/10.3390/medicina59040790
- Cardoso, Pedro MD, MSc1,2,*; Saraiva, Miguel Mascarenhas MD, MSc1,2,3,*; Afonso, Ribeiro, Tiago MD, MSc; Andrade, Patrícia MD; Ferreira, João PhD4,5; Cardoso, Hélder MD, MSc1,2,3; Macedo, Guilherme PhD. Artificial Intelligence and Device-Assisted Enteroscopy: Automatic Detection of Enteric Protruding Lesions Using a Convolutional Neural Network. Clinical and Translational Gastroenterology 13(8):p e00514, August 2022. | DOI: 10.14309/ctg.0000000000000514
- Martins, M.; Mascarenhas, M.; Afonso, J.; Ribeiro, T.; Cardoso, P.; Mendes, F.; Cardoso, H.; Andrade, P.; Macedo, G. Deep-Learning and Device-Assisted Enteroscopy: Automatic Panendoscopic Detection of Ulcers and Erosions. Medicina 2023, 59, 172. https://doi.org/10.3390/medicina59010172
