Real-World Validation of a Computer-Aided Diagnosis System for Prediction of Polyp Histology in Colonoscopy: A Prospective Multicenter Study.

Computer-aided diagnosis (CADx) of polyp histology could support endoscopists in clinical decision-making. However, this has not been validated in a real-world setting. We performed a prospective, multicenter study comparing CADx and endoscopist predictions of polyp histology in real-time colonoscopy. Optical diagnosis based on visual inspection of polyps was made by experienced endoscopists. After this, the automated output from the CADx support tool was recorded. All imaged polyps were resected for histological assessment. Primary outcome was difference in diagnostic performance between CADx and endoscopist prediction of polyp histology. Subgroup analysis was performed for polyp size, bowel preparation, difficulty of location of the polyps, and endoscopist experience. A total of 661 eligible polyps were resected in 320 patients aged ≥40 years between March 2021 and July 2022. CADx had an overall accuracy of 71.6% (95% confidence interval [CI] 68.0-75.0), compared with 75.2% (95% CI 71.7-78.4) for endoscopists ( P = 0.023). The sensitivity of CADx for neoplastic polyps was 61.8% (95% CI 56.9-66.5), compared with 70.3% (95% CI 65.7-74.7) for endoscopists ( P < 0.001). The interobserver agreement between CADx and endoscopist predictions of polyp histology was moderate (83.1% agreement, κ 0.661). When there was concordance between CADx and endoscopist predictions, the accuracy increased to 78.1%. The overall diagnostic accuracy and sensitivity for neoplastic polyps was higher in experienced endoscopists compared with CADx predictions, with moderate interobserver agreement. Concordance in predictions increased this diagnostic accuracy. Further research is required to improve the performance of CADx and to establish its role in clinical practice.

Li JW ，Wu CCH ，Lee JWJ ，Liang R ，Soon GST ，Wang LM ，Koh XH ，Koh CJ ，Chew WD ，Lin KW ，Thian MY ，Matthew R ，Kim G ，Khor CJL ，Fock KM ，Ang TL ，So JBY ，Artificial Intelligence in Gastrointestinal Endoscopy Singapore (AIGES) Study Group ... -  《-》

Artificial Intelligence Allows Leaving-In-Situ Colorectal Polyps.

Artificial Intelligence (AI) could support cost-saving strategies for colonoscopy because of its accuracy in the optical diagnosis of colorectal polyps. However, AI must meet predefined criteria to be implemented in clinical settings. An approved computer-aided diagnosis (CADx) module for differentiating between adenoma and nonadenoma in unmagnified white-light colonoscopy was used in a consecutive series of colonoscopies. For each polyp, CADx output and subsequent endoscopist diagnosis with advanced imaging were matched against the histology gold standard. The primary outcome was the negative predictive value (NPV) of CADx for adenomatous histology for ≤5-mm rectosigmoid lesions. We also calculated the NPV for AI-assisted endoscopist predictions, and agreement between CADx and histology-based postpolypectomy surveillance intervals according to European and American guidelines. Overall, 544 polyps were removed in 162 patients, of which 295 (54.2%) were ≤5-mm rectosigmoid histologically verified lesions. CADx diagnosis was feasible in 291 of 295 (98.6%), and the NPV for ≤5-mm rectosigmoid lesions was 97.6% (95% CI, 94.1%-99.1%). There were 242 of 295 (82%) lesions that were amenable for a leave-in-situ strategy. Based on CADx output, 212 of 544 (39%) would be amenable to a resect-and-discard strategy, resulting in a 95.6% (95% CI, 90.8%-98.0%) and 95.9% (95% CI, 89.8%-98.4%) agreement between CADx- and histology-based surveillance intervals according to European and American guidelines, respectively. A similar NPV (97.6%; 95% CI, 94.8%-99.1%) for ≤5-mm rectosigmoids was achieved by AI-assisted endoscopists assessing polyps with electronic chromoendoscopy, with a CADx-concordant diagnosis in 97.2% of cases. In this study, CADx without advanced imaging exceeded the benchmarks required for optical diagnosis of colorectal polyps. CADx could help implement cost-saving strategies in colonoscopy by reducing the burden of polypectomy and/or pathology. gov registration number: NCT04884581.

Hassan C ，Balsamo G ，Lorenzetti R ，Zullo A ，Antonelli G ... -  《-》

Impact of computer-aided characterization for diagnosis of colorectal lesions, including sessile serrated lesions: Multireader, multicase study.

Computer-aided characterization (CADx) may be used to implement optical biopsy strategies into colonoscopy practice; however, its impact on endoscopic diagnosis remains unknown. We aimed to evaluate the additional diagnostic value of CADx when used by endoscopists for assessing colorectal polyps. This was a single-center, multicase, multireader, image-reading study using randomly extracted images of pathologically confirmed polyps resected between July 2021 and January 2022. Approved CADx that could predict two-tier classification (neoplastic or nonneoplastic) by analyzing narrow-band images of the polyps was used to obtain a CADx diagnosis. Participating endoscopists determined if the polyps were neoplastic or not and noted their confidence level using a computer-based, image-reading test. The test was conducted twice with a 4-week interval: the first test was conducted without CADx prediction and the second test with CADx prediction. Diagnostic performances for neoplasms were calculated using the pathological diagnosis as reference and performances with and without CADx prediction were compared. Five hundred polyps were randomly extracted from 385 patients and diagnosed by 14 endoscopists (including seven experts). The sensitivity for neoplasia was significantly improved by referring to CADx (89.4% vs. 95.6%). CADx also had incremental effects on the negative predictive value (69.3% vs. 84.3%), overall accuracy (87.2% vs. 91.8%), and high-confidence diagnosis rate (77.4% vs. 85.8%). However, there was no significant difference in specificity (80.1% vs. 78.9%). Computer-aided characterization has added diagnostic value for differentiating colorectal neoplasms and may improve the high-confidence diagnosis rate.

Kato S ，Kudo SE ，Minegishi Y ，Miyata Y ，Maeda Y ，Kuroki T ，Takashina Y ，Mochizuki K ，Tamura E ，Abe M ，Sato Y ，Sakurai T ，Kouyama Y ，Tanaka K ，Ogawa Y ，Nakamura H ，Ichimasa K ，Ogata N ，Hisayuki T ，Hayashi T ，Wakamura K ，Miyachi H ，Baba T ，Ishida F ，Nemoto T ，Misawa M ，Collaborators ... -  《-》

Artificial Intelligence for Real-Time Prediction of the Histology of Colorectal Polyps by General Endoscopists.

Rex DK ，Bhavsar-Burke I ，Buckles D ，Burton J ，Cartee A ，Comar K ，Edwards A ，Fennimore B ，Fischer M ，Gerich M ，Gilmore A ，Hamdeh S ，Hoffman J ，Ibach M ，Jackson M ，James-Stevenson T ，Kaltenbach T ，Kaplan J ，Kapur S ，Kohm D ，Kriss M ，Kundumadam S ，Kyanam Kabir Baig KR ，Menard-Katcher P ，Kraft C ，Langworthy J ，Misra B ，Molloy E ，Munoz JC ，Norvell J ，Nowak T ，Obaitan I ，Patel S ，Patel M ，Peter S ，Reid BM ，Rogers N ，Ross J ，Ryan J ，Sagi S ，Saito A ，Samo S ，Sarkis F ，Scott FI ，Siwiec R ，Sullivan S ，Wieland A ，Zhang J ，Repici A ，Hassan C ，Byrne MF ，Rastogi A ... -  《-》

Real-Time Artificial Intelligence-Based Optical Diagnosis of Neoplastic Polyps during Colonoscopy.

Barua I ，Wieszczy P ，Kudo SE ，Misawa M ，Holme Ø ，Gulati S ，Williams S ，Mori K ，Itoh H ，Takishima K ，Mochizuki K ，Miyata Y ，Mochida K ，Akimoto Y ，Kuroki T ，Morita Y ，Shiina O ，Kato S ，Nemoto T ，Hayee B ，Patel M ，Gunasingam N ，Kent A ，Emmanuel A ，Munck C ，Nilsen JA ，Hvattum SA ，Frigstad SO ，Tandberg P ，Løberg M ，Kalager M ，Haji A ，Bretthauer M ，Mori Y ... -  《-》