Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy

By M. Yamada, Yutaka Saito, Hitoshi Imaoka, Masahiro Saiko, Shigemi Yamada, Hiroko Kondo, H. Takamaru, T. Sakamoto, Jun Sese, A. Kuchiba and 2 more. 2019

244 citations recorded by the source corpus.

Gaps in colonoscopy skills among endoscopists, primarily due to experience, have been identified, and solutions are critically needed. Hence, the development of a real-time robust detection system for colorectal neoplasms is considered to significantly reduce the risk of missed lesions during colonoscopy. Here, we develop an artificial intelligence (AI) system that automatically detects early signs of colorectal cancer during colonoscopy; the AI system shows the sensitivity and specificity are 97.3% (95% confidence interval [CI] = 95.9%–98.4%) and 99.0% (95% CI = 98.6%–99.2%), respectively, and the area under the curve is 0.975 (95% CI = 0.964–0.986) in the validation set. Moreover, the sensitivities are 98.0% (95% CI = 96.6%–98.8%) in the polypoid subgroup and 93.7% (95% CI = 87.6%–96.9%) in the non-polypoid subgroup; To accelerate the detection, tensor metrics in the trained model was decomposed, and the system can predict cancerous regions 21.9 ms/image on average. These findings suggest that the system is sufficient to support endoscopists in the high detection against non-polypoid lesions, which are frequently missed by optical colonoscopy. This AI system can alert endoscopists in real-time to avoid missing abnormalities such as non-polypoid polyps during colonoscopy, improving the early detection of this disease.

Subfields: Artificial Intelligence, Oncology, Pulmonary and Respiratory Medicine

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Citations in the corpus

Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine (2020, methodology): “Therefore, we believe that the system has reached a stage where it can be developed for clinical implementation as an endoscopic diagnostic support system [36].”
From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment. (2023, methodology): “17 Here, we discuss examples of ML—primarily DL—applied to three clinical stages: risk stratiﬁ-cation, diagnosis, and prognosis and treatment planning.”
Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review (2024, background): “For instance, CNNs find application in the classification, segmentation, and registration of various medical images such as endoscopic, X-ray, magnetic resonance imaging (MRI), computed tomography (CT), ultrasound (US), skin, and histopathology images [3–10].”
A Comprehensive Study on Colorectal Polyp Segmentation With ResUNet++, Conditional Random Field and Test-Time Augmentation (2021, background): “The quality of a colonoscopy examination is largely determined by the experience and skill of the endoscopist [23].”
Deep learning in cancer pathology: a new generation of clinical biomarkers (2020, background): “Magnetic resonance imaging (MRI) data, which contain much more information than CT data, are also amenable for DL-based mining, 16 and DL has also shown robust results for non-radiology tasks such as the analysis of real-time endoscopy images 17,18 and skin cancer detection in dermoscopy images.”