Advertisement

High pooled performance of convolutional neural networks in computer-aided diagnosis of GI ulcers and/or hemorrhage on wireless capsule endoscopy images: a systematic review and meta-analysis

      Background and Aims

      Diagnosis of GI ulcers and/or hemorrhage by wireless capsule endoscopy (WCE) is limited by the physician-dependent, tedious, time-consuming process of image and/ or video classification. Computer-aided diagnosis (CAD) by convolutional neural network (CNN)-based machine learning may help reduce this burden. Our aim was to conduct a meta-analysis and appraise the reported data.

      Methods

      Multiple databases were searched (from inception to November 2019), and studies that reported on the performance of CNN in the diagnosis of GI ulcerations and/or hemorrhage on WCE were selected. A random-effects model was used to calculate the pooled rates. In cases where multiple 2 × 2 contingency tables were provided for different thresholds, we assumed the data tables were independent from each other. Heterogeneity was assessed by I2% and 95% prediction intervals.

      Results

      Nine studies were included in our final analysis that evaluated the performance of CNN-based CAD of GI ulcers and/or hemorrhage by WCE. The pooled accuracy was 95.4% (95% confidence interval [CI], 94.3-96.3), sensitivity was 95.5% (95% CI, 94-96.5), specificity was 95.8% (95% CI, 94.7-96.6), positive predictive value was 95.8% (95% CI, 90.5-98.2), and negative predictive value was 96.8% (95% CI, 94.9-98.1). I2% heterogeneity was negligible except for the pooled positive predictive value.

      Conclusions

      Based on our meta-analysis, CNN-based CAD of GI ulcerations and/or hemorrhage on WCE achieves a high-level performance. The quality of the evidence is robust, and therefore CNN-based CAD has the potential to become the first choice of machine learning to optimize WCE image/video reading.

      Abbreviations:

      CAD (computer-aided diagnosis), CI (confidence interval), CNN (convolutional neural networks), NPV (negative predictive value), PI (prediction interval), PPV (positive predictive value), WCE (wireless capsule endoscopy)
      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Gastrointestinal Endoscopy
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Iddan G.
        • Meron G.
        • Glukhovsky A.
        • et al.
        Wireless capsule endoscopy.
        Nature. 2000; 405: 417
        • Wang A.
        • Banerjee S.
        • Barth B.A.
        • et al.
        Wireless capsule endoscopy.
        Gastrointest Endosc. 2013; 78: 805-815
        • Abu Dayyeh B.K.
        • Thosani N.
        • Konda V.
        • et al.
        • ASGE Technology Committee
        ASGE Technology Committee systematic review and meta-analysis assessing the ASGE PIVI thresholds for adopting real-time endoscopic assessment of the histology of diminutive colorectal polyps.
        Gastrointest Endosc. 2015; 81: 502.e1-502.e16
        • Alaskar H.
        • Hussain A.
        • Al-Aseem N.
        • et al.
        Application of convolutional neural networks for automated ulcer detection in wireless capsule endoscopy images.
        Sensors (Basel). 2019; 19: 1265
        • Aoki T.
        • Yamada A.
        • Aoyama K.
        • et al.
        Automatic detection of erosions and ulcerations in wireless capsule endoscopy images based on a deep convolutional neural network.
        Gastrointest Endosc. 2019; 89: 357-363.e2
        • Aoki T.
        • Yamada A.
        • Kato Y.
        • et al.
        Automatic detection of blood content in capsule endoscopy images based on a deep convolutional neural network.
        J Gastroenterol Hepatol. 2020; 35: 1196-1200
        • Ding Z.
        • Shi H.
        • Zhang H.
        • et al.
        Gastroenterologist-level identification of small-bowel diseases and normal variants by capsule endoscopy using a deep-learning model.
        Gastroenterology. 2019; 157: 1044-1054.e5
        • Fan S.
        • Xu L.
        • Fan Y.
        • et al.
        Computer-aided detection of small intestinal ulcer and erosion in wireless capsule endoscopy images.
        Phys Med Biol. 2018; 63: 165001
        • Jeon Y.
        • Cho E.
        • Moon S.
        • et al.
        Deep convolutional neural network-based automated lesion detection in wireless capsule endoscopy.
        Proc SPIE. 2019; : 11050
        • Klang E.
        • Barash Y.
        • Yehuda Margalit R.
        • et al.
        Deep learning algorithms for automated detection of Crohn's disease ulcers by video capsule endoscopy.
        Gastrointest Endosc. 2020; 91: 606-613.e2
        • Leenhardt R.
        • Vasseur P.
        • Li C.
        • et al.
        A neural network algorithm for detection of GI angiectasia during small-bowel capsule endoscopy.
        Gastrointest Endosc. 2019; 89: 189-194
        • Tsuboi A.
        • Oka S.
        • Aoyama K.
        • et al.
        Artificial intelligence using a convolutional neural network for automatic detection of small-bowel angioectasia in capsule endoscopy images.
        Dig Endosc. 2020; 32: 382-390
        • Liu X.
        • Faes L.
        • Kale A.U.
        • et al.
        A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis.
        Lancet Digital Health. 2019; 1: e271-e297
        • Stroup D.F.
        • Berlin J.A.
        • Morton S.C.
        • et al.
        Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group.
        JAMA. 2000; 283: 2008-2012
        • Krizhevsky A.
        • Sutskever I.
        • Hinton G.E.
        ImageNet classification with deep convolutional neural networks.
        in: Bartlett P. Advances in neural information processing systems. Neural Information Processing Systems Foundation, Inc, San Diego, CA2012: 1097-1105
        • DerSimonian R.
        • Laird N.
        Meta-analysis in clinical trials.
        Controlled Clinical Trials. 1986; 7: 177-188
        • Higgins J.P.
        • Thompson S.G.
        • Deeks J.J.
        • et al.
        Measuring inconsistency in meta-analyses.
        BMJ. 2003; 327: 557
        • Mohan B.P.
        • Adler D.G.
        Heterogeneity in systematic review and meta-analysis: how to read between the numbers.
        Gastrointest Endosc. 2019; 89: 902-903
        • Boal Carvalho P.
        • Magalhaes J.
        • Dias D.E.C.F.
        • et al.
        Suspected blood indicator in capsule endoscopy: a valuable tool for gastrointestinal bleeding diagnosis.
        Arq Gastroenterol. 2017; 54: 16-20
        • Yung D.E.
        • Sykes C.
        • Koulaouzidis A.
        The validity of suspected blood indicator software in capsule endoscopy: a systematic review and meta-analysis.
        Expert Rev Gastroenterol Hepatol. 2017; 11: 43-51
        • Novozámský A.
        • Flusser J.
        • Tachecí I.
        • et al.
        Automatic blood detection in capsule endoscopy video.
        J Biomed Opt. 2016; 21: 126007
        • Aoki T.
        • Yamada A.
        • Aoyama K.
        • et al.
        Clinical usefulness of a deep learning-based system as the first screening on small-bowel capsule endoscopy reading.
        Dig Endosc. 2020; 32: 585-591

      Linked Article