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New AI model for neoplasia detection and characterisation in inflammatory bowel disease
  1. Mohamed Abdelrahim1,2,
  2. Katie Siggens3,
  3. Yuji Iwadate4,
  4. Naoto Maeda4,
  5. Hein Htet3,
  6. Pradeep Bhandari3
  1. 1 Gastroenterology, Portsmouth Hospitals University NHS Trust, Portsmouth, UK
  2. 2 Royal Devon and Exeter Hospital, Exeter, UK
  3. 3 Portsmouth Hospitals University NHS Trust, Portsmouth, UK
  4. 4 NEC Corporation, Minato-ku, Tokyo, Japan
  1. Correspondence to Pradeep Bhandari, Portsmouth Hospitals University NHS Trust, Portsmouth, UK; pradeep.bhandari{at}porthosp.nhs.uk

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Endoscopic neoplasia detection in inflammatory bowel disease (IBD) remains challenging. We developed and validated a novel artificial intelligence (AI) model for lesion detection and characterisation in 478 images from 30 patients with IBD, 10 of whom had a total of 25 neoplastic lesions (including 8 sessile serrated polyps); sensitivity and specificity for lesion detection were 93.5% and 80.6%, respectively. The IBD model was then further validated during a real-time endoscopic assessment of a further 30 consecutive patients with 25 neoplastic lesions found in 11/30 of them and achieved lesion detection rate, lesion per colonoscopy and neoplasia per colonoscopy of 90.4%, 4.6% and 0.96. respectively. The sensitivity and specificity of lesion characterisation were 87.5% and 80.6%, respectively.

In more details

Development of the IBD deep learning model

Deep learning (DL) is a subset of AI that uses multilayered computer algorithms (also called deep artificial neural networks) to automatically learn representations of data with multiple levels of abstraction, thus avoiding some of the limitations of more traditional AI techniques that rely on hand-crafted feature extraction.1

The IBD-dedicated DL model in this study was developed using RetinaNet architecture with a ResNet-101 backbone for deep feature extraction. This is a one-stage detector that uses a focal loss function to eliminate the accuracy gap between this one-stage detector and two-stage detectors while running at a faster processing speed.2 Figure 1 illustrates the structure of the DL model.

Figure 1

Shows the RetinaNet deep learning architecture used to develop the IBD deep learning model in this study.

The characterisation function in this study is a binary classification of lesions into neoplastic or non-neoplastic. Neoplastic category includes adenoma (low or high grade), cancer and any IBD-associated dysplastic lesions. Non-neoplastic category includes all other lesions (ie, hyperplastic, inflammatory and pseudopolyps).

In all stages of this study (training, validation and testing), images were classified as containing lesions (including both …

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Footnotes

  • MA and KS are joint first authors.

  • Contributors MA: study concept and design, data acquisition, drafting of the manuscript, analysis and interpretation of data, and critical revision of the manuscript. KS: study concept and design, data acquisition, analysis and interpretation of data, and critical revision of the manuscript. YI: software implementation, interpretation of technical data and revision of the manuscript. NM: software implementation, interpretation of technical data and revision of the manuscript. HH: data acquisition and revision of the manuscript. PB: study concept and design, interpretation of data and critical revision of the manuscript.

  • Funding This study was funded by NEC Japan (PHT/2020/58).

  • Competing interests YI and NM are employed by NEC Japan. PB received a research fund from NEC Japan.

  • Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.