Assisted documentation as a new focus for artificial intelligence in endoscopy: the precedent of reliable withdrawal time and image reporting

Lux, Thomas J.; Saßmannshausen, Zita; Kafetzis, Ioannis; Sodmann, Philipp; Herold, Katja; Sudarevic, Boban; Schmitz, Rüdiger; Zoller, Wolfram G.; Meining, Alexander; Hann, Alexander

doi:10.1055/a-2122-1671

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…[10] In more recent work, an AI tool to measure colonoscopy WT and polypectomy time (similar to what we have described above) was described with potential added functionality of minimizing manual documentation that must occur after procedures. [11] In contrast to prior systems, the AI-CQ is meant to be an interactive tool. The tool loads a recorded colonoscopy video and analyzes it on demand for review.…”

Section: Discussionmentioning

confidence: 99%

“…A real-time algorithm acting as a “speedometer” to measure withdrawal speed has been described but did not improve quality 10 . In more recent work, an AI tool to measure colonoscopy WT and PT (similar to what we have described above) was described with potential added functionality of minimizing manual documentation that must occur after procedures 11 .…”

Section: Discussionmentioning

confidence: 99%

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Assessment of colonoscopy skill using machine learning to measure quality: Proof-of-concept and initial validation

Wittbrodt,

Klug,

Etemadi

et al. 2024

Endosc Int Open

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Background and Aims: Low quality colonoscopy increases cancer risk but measuring quality remains challenging. We developed an automated, interactive assessment of colonoscopy quality (AI-CQ) using machine learning (ML). Methods: Based on quality guidelines, metrics selected for AI development included insertion time (IT), withdrawal time (WT), polyp detection rate (PDR), and polyps per colonoscopy (PPC). Two novel metrics were also developed: HQ-WT (time during withdrawal with clear image) and WT-PT (withdrawal time subtracting polypectomy time). The model was pre-trained using a self-supervised vision transformer on unlabeled colonoscopy images and then finetuned for multi-label classification on another mutually exclusive colonoscopy image dataset. A timeline of video predictions and metric calculations were presented to clinicians in addition to the raw video using a web-based application. The model was externally validated using 50 colonoscopies at a second hospital. Results: The AI-CQ accuracy to identify cecal intubation was 88%. IT (ρ = 0.99) and WT (ρ = 0.99) were highly correlated between manual and AI-CQ measurements with a median difference of 1.5s and 4.5s, respectively. AI-CQ PDR did not significantly differ from manual PDR (47.6% versus 45.5%, p = 0.66). Retroflexion was correctly identified in 95.2% and number of right colon evaluations in 100% of colonoscopies. HQ-WT was 45.9% of, and significantly correlated with (ρ = 0.85) WT time. Conclusions: An interactive AI assessment of colonoscopy skill can automatically assess quality. We propose that this tool can be utilized to rapidly identify and train providers in need of remediation.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Assessment of colonoscopy skill using machine learning to measure quality: Proof-of-concept and initial validation

Wittbrodt,

Klug,

Etemadi

et al. 2024

Endosc Int Open

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“…Instead, report systems could include such default values or incorporate AI-assisted reporting for most common findings. Earlier studies could already demonstrate promising results for polyp size measurement, image acquisition, and withdrawal time estimation [ 36 , 37 ].…”

Section: Discussionmentioning

confidence: 99%

Closing the Gap: A Critical Examination of Adherence, Inconsistency, and Improvements in Colonoscopy Reporting Practices

Lux,

Herold,

Kafetzis

et al. 2024

Digestion

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Introduction: Comprehensive and standardized colonoscopy reports are crucial in colorectal cancer prevention, monitoring, and research. This study investigates adherence to national and international guidelines by analyzing reporting practices among 21 endoscopists in 7 German centers, with a focus on polyp reporting. Methods: We identified and assessed German, European, American, and World Health Organization-provided statements to identify key elements in colonoscopy reporting. Board-certified gastroenterologists rated the relevance of each element and estimated their reporting frequency. Adherence to the identified report elements was evaluated for 874 polyps from 351 colonoscopy reports ranging from March 2021 to March 2022. Results: We identified numerous recommendations for colonoscopy reporting. We categorized the reasoning behind those recommendations into clinical relevance, justification, and quality control and research. Although all elements were considered relevant by the surveyed gastroenterologists, discrepancies were observed in the evaluated reports. Particularly diminutive polyps or attributes which are rarely abnormal (e.g., surface integrity) respectively rarely performed (e.g., injection) were sparsely documented. Furthermore, the white light morphology of polyps was inconsistently documented using either the Paris classification or free text. In summary, the analysis of 874 reported polyps revealed heterogeneous adherence to the recommendations, with reporting frequencies ranging from 3% to 89%. Conclusion: The inhomogeneous report practices may result from implicit reporting practices and recommendations with varying clinical relevance. Future recommendations should clearly differentiate between clinical relevance and research and quality control or explanatory purposes. Additionally, the role of computer-assisted documentation should be further evaluated to increase report frequencies of non-pathological findings and diminutive polyps.

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“…1 Traction techniques have been reported to assist the ESD procedure. [2][3][4][5] Compared with other traction devices, this traction device has some advantages. First, it is easily obtained and manipulated.…”

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confidence: 99%

Novel three‐traction strings device with endoscopic submucosal dissection for the removal of a large rectal sessile serrated adenoma

Zhu,

Wu,

2024

Digestive Endoscopy

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Assisted documentation as a new focus for artificial intelligence in endoscopy: the precedent of reliable withdrawal time and image reporting

Cited by 7 publications

References 19 publications

Assessment of colonoscopy skill using machine learning to measure quality: Proof-of-concept and initial validation

Assessment of colonoscopy skill using machine learning to measure quality: Proof-of-concept and initial validation

Closing the Gap: A Critical Examination of Adherence, Inconsistency, and Improvements in Colonoscopy Reporting Practices

Novel three‐traction strings device with endoscopic submucosal dissection for the removal of a large rectal sessile serrated adenoma

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