Artificial Intelligence Technology Applications in the Pathologic Diagnosis of the Gastrointestinal Tract

References

• American Medical Association . Augmented intelligence in healthcare. (2020) www.ama-assn.org/system/files/2019-01/augmented-intelligence-policy-report.pdf
   Google Scholar
• Shung DL , ByrneMF. How artificial intelligence will impact colonoscopy and colorectal screening. Gastrointest. Endosc. Clin. N. Am.30(3), 585–595 (2020).
   PubMedGoogle Scholar
• Lui TK , TsuiVW, LeungWK. Accuracy of artificial intelligence-assisted detection of upper GI lesions: a systematic review and meta-analysis. Gastrointest. Endosc.S0016-5107(20), 34459–X (2020) ( In Press).
   Google Scholar
• Ebigbo A , PalmC, ProbstAet al. A technical review of artificial intelligence as applied to gastrointestinal endoscopy: clarifying the terminology. Endosc. Int. Open.7(12), E1616–E1623 (2019).
   PubMed Web of Science ®Google Scholar
• Arvaniti E , FrickerKS, MoretMet al. Automated Gleason grading of prostate cancer tissue microarrays via deep learning. Sci. Rep.8(1), 12054 (2018).
   PubMedGoogle Scholar
• Liu Y , KohlbergerT, NorouziMet al. Artificial intelligence-based breast cancer nodal metastasis detection: insights into the black box for pathologists. Arch. Pathol. Lab. Med.143(7), 859–868 (2019).
   PubMed Web of Science ®Google Scholar
• Ogino S , NowakJA, HamadaTet al. Insights into pathogenic interactions among environment, host, and tumor at the crossroads of molecular pathology and epidemiology. Annu. Rev. Pathol.14, 83–103 (2019).
   PubMed Web of Science ®Google Scholar
• Komura D , IshikawaS. Machine learning approaches for pathologic diagnosis. Virchows Arch.475(2), 131–138 (2019).
   PubMed Web of Science ®Google Scholar
• Nielsen JA , LagerDJ, LewinMet al. Incidence of diagnostic change in colorectal polyp specimens after deeper sectioning at 2 different laboratories staffed by the same pathologists. Am. J. Clin. Pathol.140(2), 231–237 (2013).
   PubMed Web of Science ®Google Scholar
• Wani S , RubensteinJH, ViethM, BergmanJ. Diagnosis and management of low-grade dysplasia in Barrett’s esophagus: expert review from the Clinical Practice Updates Committee of the American Gastroenterological Association. Gastroenterology.151(5), 822–835 (2016).
   PubMed Web of Science ®Google Scholar
• Liu Y , KohlbergerT, NorouziMet al. Artificial intelligence-based breast cancer nodal metastasis detection: insights into the black box for pathologists. Arch. Pathol. Lab. Med.143(7), 859–868 (2019).
   PubMed Web of Science ®Google Scholar
• Hanna MG , ReuterVE, HameedMRet al. Whole slide imaging equivalency and efficiency study: experience at a large academic center. Mod. Pathol.32(7), 916–928 (2019).
   PubMed Web of Science ®Google Scholar
• Wei JW , WeiJW, JacksonCRet al. Automated detection of celiac disease on duodenal biopsy slides: a deep learning approach. J. Pathol. Inform.10, 7 (2019).
   PubMedGoogle Scholar
• Akintayo A , TylkaGL, SinghAKet al. A deep learning framework to discern and count microscopic nematode eggs. Sci. Rep.8(1), 9145 (2018).
   PubMedGoogle Scholar
• Martin DR , HansonJA, GullapalliRRet al. A deep learning convolutional neural network can recognize common patterns of injury in gastric pathology. Arch. Pathol. Lab. Med.144(3), 370–378 (2020).
   PubMed Web of Science ®Google Scholar
• Yang F , JiaX, LeiPet al. Quantification of hepatic steatosis in histologic images by deep learning method. J. Xray. Sci. Technol.27(6), 1033–1045 (2019).
   PubMed Web of Science ®Google Scholar
• Yu Y , WangJ, NgCWet al. Deep learning enables automated scoring of liver fibrosis stages. Sci. Rep.8(1), 16016 (2018).
   PubMedGoogle Scholar
• Korbar B , OlofsonAM, MiraflorAPet al. Deep learning for classification of colorectal polyps on whole-slide images. J. Pathol. Inform.8, 30 (2017).
   PubMedGoogle Scholar
• Iizuka O , KanavatiF, KatoKet al. Deep learning models for histopathological classification of gastric and colonic epithelial tumours. Sci. Rep.10(1), 1504 (2020).
   PubMed Web of Science ®Google Scholar
• Van Eycke YR , BalsatC, VersetLet al. Segmentation of glandular epithelium in colorectal tumours to automatically compartmentalise IHC biomarker quantification: a deep learning approach. Med. Image. Anal.49, 35–45 (2018).
   PubMed Web of Science ®Google Scholar
• Shapcott M , HewittKJ, RajpootN. Deep learning with sampling in colon cancer histology. Front. Bioeng. Biotechnol.7, 52 (2019).
   PubMed Web of Science ®Google Scholar
• Chang Young Hwan , ThibaultG, MadinOet al. Deep learning based nucleus classification in pancreas histological images. Conf. Proc. IEEE Eng. Med. Bio. l Soc.2017, 672–675 (2017).
   Google Scholar
• Mori H , MiwaH. A histopathologic feature of the behavior of gastric signet-ring cell carcinoma; an image analysis study with deep learning. Pathol. Int.69(7), 437–439 (2019).
   PubMed Web of Science ®Google Scholar
• Tomita N , AbdollahiB, WeiJet al. Attention-based deep neural networks for detection of cancerous and precancerous esophagus tissue on histopathological slides. JAMA Netw. Open.2(11), e1914645 (2019).
   PubMed Web of Science ®Google Scholar
• Bychkov D , LinderN, TurkkiRet al. Deep learning based tissue analysis predicts outcome in colorectal cancer. Sci. Rep.8(1), 3395 (2018).
   PubMedGoogle Scholar
• Skrede OJ , DeRaedt S, KleppeAet al. Deep learning for prediction of colorectal cancer outcome: a discovery and validation study. Lancet.395(10221), 350–360 (2020).
   PubMed Web of Science ®Google Scholar
• Kather JN , KrisamJ, CharoentongPet al. Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLoS Med.16(1), e1002730 (2019).
   PubMed Web of Science ®Google Scholar
• Kather JN , PearsonAT, HalamaNet al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat. Med.25(7), 1054–1056 (2019).
   PubMed Web of Science ®Google Scholar
• Sharma H , ZerbeN, KlempertIet al. Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology. Comput. Med. Imaging. Graph.61, 2–13 (2017).
   PubMed Web of Science ®Google Scholar
• Allen TC . Pathologists will prevail. Arch. Pathol. Lab. Med.144(4), 416–419 (2020).
   PubMed Web of Science ®Google Scholar
• Robboy SJ , GuptaS, CrawfordJMet al. The pathologist workforce in the United States: II, an interactive modeling tool for analyzing future qualitative and quantitative staffing demands for services. Arch. Pathol. Lab. Med.139(11), 1413–1430 (2015).
   PubMed Web of Science ®Google Scholar
• Qu J , HirutaN, TeraiK, NosatoHet al. Gastric pathology image classification using stepwise fine-tuning for deep neural networks. J. Health Eng.8961781 (2018).
   PubMed Web of Science ®Google Scholar