Gastroenterology Meets Machine Learning: Status Quo and Quo Vadis

Machine learning has undergone a transition phase from being a pure statistical tool to being one of the main drivers of modern medicine. In gastroenterology, this technology is motivating a growing number of studies that rely on these innovative methods to deal with critical issues related to this practice. Hence, in the light of the burgeoning research on the use of machine learning in gastroenterology, a systematic review of the literature is timely. In this work, we present the results gleaned through a systematic review of prominent gastroenterology literature using machine learning techniques. Based on the analysis of 88 journal articles, we delimit the scope of application, we discuss current limitations including bias, lack of transparency, accountability, and data availability, and we put forward future avenues.

[1]  Hong Yan,et al.  Molecular subtyping of cancer: current status and moving toward clinical applications , 2019, Briefings Bioinform..

[2]  Yevgeniy Vorobeychik,et al.  A Crowdsourcing Framework for Medical Data Sets , 2018, AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science.

[3]  Joelle Pineau,et al.  Treating Epilepsy via Adaptive Neurostimulation: a Reinforcement Learning Approach , 2009, Int. J. Neural Syst..

[4]  F. Cheriet,et al.  Deep feature learning for automatic tissue classification of coronary artery using optical coherence tomography. , 2017, Biomedical optics express.

[5]  Weiguo Fan,et al.  A new image classification method using CNN transfer learning and web data augmentation , 2018, Expert Syst. Appl..

[6]  M Buscema,et al.  International experience on the use of artificial neural networks in gastroenterology. , 2007, Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver.

[7]  I. Vlahavas,et al.  Machine Learning and Data Mining Methods in Diabetes Research , 2017, Computational and structural biotechnology journal.

[8]  Javier Escudero,et al.  Discriminative analysis of schizophrenia using support vector machine and recursive feature elimination on structural MRI images , 2016, Medicine.

[9]  J. Buckner,et al.  MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas , 2016, Medical physics.

[10]  Marc Peter Deisenroth,et al.  Deep Reinforcement Learning: A Brief Survey , 2017, IEEE Signal Processing Magazine.

[12]  Stavroula G. Mougiakakou,et al.  Model-Free Machine Learning in Biomedicine: Feasibility Study in Type 1 Diabetes , 2016, PloS one.

[13]  Dimitrios I. Fotiadis,et al.  Machine learning applications in cancer prognosis and prediction , 2014, Computational and structural biotechnology journal.

[14]  Z. Kou,et al.  Texture analysis of magnetic resonance T1 mapping with dilated cardiomyopathy , 2018, Medicine.

[15]  Federico Cabitza,et al.  Machine Learning in Orthopedics: A Literature Review , 2018, Front. Bioeng. Biotechnol..

[16]  A. Aldo Faisal,et al.  The use of reinforcement learning algorithms to meet the challenges of an artificial pancreas , 2013, Expert review of medical devices.

[17]  Srini Tridandapani,et al.  Deep Learning in Radiology. , 2018, Academic radiology.

[18]  Nicholas V. Annetta,et al.  Restoring cortical control of functional movement in a human with quadriplegia , 2016, Nature.

[19]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[20]  John Quackenbush,et al.  Histopathological Image QTL Discovery of Immune Infiltration Variants , 2018, iScience.

[21]  G. Antoch,et al.  Performance and clinical impact of machine learning based lung nodule detection using vessel suppression in melanoma patients. , 2018, Clinical imaging.

[22]  D. Bing,et al.  Predicting the hearing outcome in sudden sensorineural hearing loss via machine learning models , 2018, Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery.

[23]  Rajeev Kumar,et al.  Receiver operating characteristic (ROC) curve for medical researchers , 2011, Indian pediatrics.

[24]  Jatinder Singh,et al.  Critical appraisal skills programme , 2013 .

[25]  Andrew H. Beck,et al.  Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer , 2017, JAMA.

[26]  M. Kosorok,et al.  Reinforcement Learning Strategies for Clinical Trials in Nonsmall Cell Lung Cancer , 2011, Biometrics.

[27]  J. Espinoza,et al.  Machine learning for tackling microbiota data and infection complications in immunocompromised patients with cancer , 2018, Journal of internal medicine.

[28]  Damini Dey,et al.  Prognostic Value of Combined Clinical and Myocardial Perfusion Imaging Data Using Machine Learning. , 2017, JACC. Cardiovascular imaging.

[29]  Oana Geman,et al.  Deep Learning Tools for Human Microbiome Big Data , 2016, SOFA.

[30]  Arthur L. Samuel,et al.  Some Studies in Machine Learning Using the Game of Checkers , 1967, IBM J. Res. Dev..

[31]  D. Moher,et al.  Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. , 2010, International journal of surgery.

[32]  R. Mirnezami,et al.  Surgery 3.0, artificial intelligence and the next‐generation surgeon , 2018, The British journal of surgery.

[33]  Niema M. Pahlevan,et al.  Artificial Intelligence Estimation of Carotid-Femoral Pulse Wave Velocity using Carotid Waveform , 2018, Scientific Reports.

[34]  Bing Lang,et al.  Deep Learning for Histopathological Image Analysis: Towards Computerized Diagnosis on Cancers , 2017, Deep Learning and Convolutional Neural Networks for Medical Image Computing.

[35]  Gari D. Clifford,et al.  A machine learning approach to multi-level ECG signal quality classification , 2014, Comput. Methods Programs Biomed..

[36]  Amina Adadi,et al.  Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) , 2018, IEEE Access.

[37]  A. Petruzziello,et al.  Global epidemiology of hepatitis C virus infection: An up-date of the distribution and circulation of hepatitis C virus genotypes , 2016, World journal of gastroenterology.

[38]  Pearl Brereton,et al.  Performing systematic literature reviews in software engineering , 2006, ICSE.

[39]  Reza Ghaeini,et al.  A Deep Learning Approach for Cancer Detection and Relevant Gene Identification , 2017, PSB.

[40]  Hongmin Cai,et al.  Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning , 2016, Scientific Reports.

[41]  Virginia Dignum,et al.  Responsible Artificial Intelligence: Designing Ai for Human Values , 2017 .

[42]  João Paulo Papa,et al.  A survey on Barrett's esophagus analysis using machine learning , 2018, Comput. Biol. Medicine.

[43]  Ravinder Agarwal,et al.  Machine learning techniques for medical diagnosis of diabetes using iris images , 2018, Comput. Methods Programs Biomed..

[44]  E. Çeltikçi.  A Systematic Review on Machine Learning in Neurosurgery: The Future of Decision-Making in Patient Care. , 2018, Turkish neurosurgery.

[45]  Aaron Y. Lee,et al.  Artificial intelligence and deep learning in ophthalmology , 2018, British Journal of Ophthalmology.

[46]  Andreas K. Maier,et al.  Comparative Analysis of Unsupervised Algorithms for Breast MRI Lesion Segmentation , 2018, Bildverarbeitung für die Medizin.

[47]  Dario Farina,et al.  Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation , 2017, Nature Biomedical Engineering.

[48]  Andrea S Martinez-Vernon,et al.  An improved machine learning pipeline for urinary volatiles disease detection: Diagnosing diabetes , 2018, PloS one.

[49]  A. Çinar.  Multivariable Adaptive Artificial Pancreas System in Type 1 Diabetes , 2017, Current Diabetes Reports.

[50]  Daisuke Komura,et al.  Machine Learning Methods for Histopathological Image Analysis , 2017, Computational and structural biotechnology journal.