Challenges of implementing computer-aided diagnostic models for neuroimages in a clinical setting

[1]  Tarik K. Alkasab,et al.  Addressing the Challenges of Implementing Artificial Intelligence Tools in Clinical Practice: Principles From Experience. , 2023, Journal of the American College of Radiology : JACR.

[2]  D. Jain.  Regulation of Digital Healthcare in India: Ethical and Legal Challenges , 2023, Healthcare.

[3]  M. Sendak,et al.  Editorial: Surfacing best practices for AI software development and integration in healthcare , 2023, Frontiers in Digital Health.

[4]  F. Barkhof,et al.  Interpretable machine learning for dementia: A systematic review , 2023, Alzheimer's & dementia : the journal of the Alzheimer's Association.

[5]  Z. Jiao,et al.  Trends in clinical validation and usage of US Food and Drug Administration-cleared artificial intelligence algorithms for medical imaging. , 2022, Clinical radiology.

[6]  M. Flagg.  Reward research for being useful — not just flashy , 2022, Nature.

[7]  Tarik K. Alkasab,et al.  Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How? , 2022, Radiology.

[8]  Jennifer M. Nicholas,et al.  Temporal order of clinical and biomarker changes in familial frontotemporal dementia , 2022, Nature Medicine.

[9]  Tarik K. Alkasab,et al.  Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How? , 2022, Radiology.

[10]  Gunvant R. Chaudhari,et al.  Deep Learning to Predict Neonatal and Infant Brain Age from Myelination on Brain MRI Scans. , 2022, Radiology.

[11]  J. Mocco,et al.  Pilot Deployment of Viz–Intracranial Hemorrhage for Intracranial Hemorrhage Detection: Real-World Performance in a Stroke Code Cohort , 2022, Stroke.

[12]  H. Im,et al.  Construction of a confounder-free clinical MRI dataset in the Mass General Brigham system for classification of Alzheimer's disease , 2022, Artif. Intell. Medicine.

[13]  H. Im,et al.  Adversarial confound regression and uncertainty measurements to classify heterogeneous clinical MRI in Mass General Brigham , 2022, PloS one.

[14]  E. Yuh,et al.  Computational Approaches for Acute Traumatic Brain Injury Image Recognition , 2022, Frontiers in Neurology.

[15]  Michael C. Burkhart,et al.  Artificial intelligence for diagnosis and prognosis in neuroimaging for dementia; a systematic review , 2021, medRxiv.

[16]  K. Smetana,et al.  Machine Learning in Action: Stroke Diagnosis and Outcome Prediction , 2021, Frontiers in Neurology.

[17]  J. E. Iglesias,et al.  Synthesis of High-Resolution Research-Quality MRI Data from Clinical MRI Data in Patients with COVID-19 , 2021, medRxiv.

[18]  Gayla R. Olbricht,et al.  A deep learning model to predict traumatic brain injury severity and outcome from MR images , 2021, 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB).

[19]  S. Klein,et al.  EASE: Clinical Implementation of Automated Tumor Segmentation and Volume Quantification for Adult Low-Grade Glioma , 2021, Frontiers in Medicine.

[20]  N. Satpute,et al.  Brain Tumor Detection Using Machine Learning and Deep Learning: A Review. , 2021, Current medical imaging.

[21]  Colin B. Compas,et al.  Federated learning for predicting clinical outcomes in patients with COVID-19 , 2021, Nature Medicine.

[22]  Karl J. Friston,et al.  Decision Models and Technology Can Help Psychiatry Develop Biomarkers , 2021, Frontiers in Psychiatry.

[23]  Rebecca M. E. Steketee,et al.  Technical and clinical validation of commercial automated volumetric MRI tools for dementia diagnosis—a systematic review , 2021, Neuroradiology.

[24]  S. Mayer,et al.  Therapies to Restore Consciousness in Patients with Severe Brain Injuries: A Gap Analysis and Future Directions , 2021, Neurocritical Care.

[25]  L. Schwartz,et al.  Multi-site, multi-platform comparison of MRI T1 measurement using the system phantom , 2021, PloS one.

[26]  R. Ranjbarzadeh,et al.  Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images , 2021, Scientific Reports.

[27]  Khurram Khurshid,et al.  Role of deep learning in brain tumor detection and classification (2015 to 2020): A review , 2021, Comput. Medical Imaging Graph..

[28]  David T. Jones,et al.  Alzheimer disease , 2021, Nature Reviews Disease Primers.

[29]  Saeid Nahavandi,et al.  Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review , 2021, Comput. Biol. Medicine.

[30]  D. Chow,et al.  Validation of a Deep Learning Tool in the Detection of Intracranial Hemorrhage and Large Vessel Occlusion , 2021, Frontiers in Neurology.

[31]  Ioannis D. Apostolopoulos,et al.  Multi-input deep learning approach for Cardiovascular Disease diagnosis using Myocardial Perfusion Imaging and clinical data. , 2021, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[32]  Dinggang Shen,et al.  Multi-site MRI harmonization via attention-guided deep domain adaptation for brain disorder identification , 2021, Medical Image Anal..

[33]  A. Chincarini,et al.  A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimer’s disease, and mild cognitive impairment using brain 18F-FDG PET , 2021, European Journal of Nuclear Medicine and Molecular Imaging.

[34]  Anonymous,et al.  2021 Alzheimer's disease facts and figures , 2021, Alzheimer's & dementia : the journal of the Alzheimer's Association.

[35]  Gerhard S. Drenthen,et al.  Quality control strategies for brain MRI segmentation and parcellation: Practical approaches and recommendations - insights from the Maastricht study , 2021, NeuroImage.

[36]  S. Haneuse,et al.  Assessing Missing Data Assumptions in EHR-Based Studies: A Complex and Underappreciated Task. , 2021, JAMA network open.

[37]  V. Thijs,et al.  Review of deep learning algorithms for the automatic detection of intracranial hemorrhages on computed tomography head imaging , 2021, Journal of NeuroInterventional Surgery.

[38]  P. Daniore,et al.  Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015-20): a comparative analysis. , 2021, The Lancet. Digital health.

[39]  Arthur W. Toga,et al.  Retrospective motion artifact correction of structural MRI images using deep learning improves the quality of cortical surface reconstructions , 2021, NeuroImage.

[40]  R. Hanel,et al.  Automated Cerebral Hemorrhage Detection Using RAPID , 2020, American Journal of Neuroradiology.

[41]  Ehsan Adeli,et al.  Training confounder-free deep learning models for medical applications , 2020, Nature Communications.

[42]  Zhenzhong Liu,et al.  Review of Deep Learning Approaches for the Segmentation of Multiple Sclerosis Lesions on Brain MRI , 2020, Frontiers in Neuroinformatics.

[43]  Ziyin Liu,et al.  Neural Networks Fail to Learn Periodic Functions and How to Fix It , 2020, NeurIPS.

[44]  Russell T. Shinohara,et al.  Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA , 2020, NeuroImage.

[45]  Mingliang Wang,et al.  A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis , 2020, Frontiers in Neuroscience.

[46]  Micah J. Sheller,et al.  The future of digital health with federated learning , 2020, npj Digital Medicine.

[47]  J. Mongan,et al.  Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers. , 2020, Radiology. Artificial intelligence.

[48]  H. Langerman,et al.  Alzheimer’s Disease – Why We Need Early Diagnosis , 2019, Degenerative neurological and neuromuscular disease.

[49]  Giles W. Story,et al.  Will artificial intelligence eventually replace psychiatrists? , 2019, The British Journal of Psychiatry.

[50]  Mahmoud Mostapha,et al.  Role of deep learning in infant brain MRI analysis. , 2019, Magnetic resonance imaging.

[51]  Ferran Prados,et al.  The quantitative neuroradiology initiative framework: application to dementia. , 2019, The British journal of radiology.

[52]  L. Oakden-Rayner,et al.  The Rebirth of CAD: How Is Modern AI Different from the CAD We Know? , 2019, Radiology. Artificial intelligence.

[53]  K. Cao,et al.  Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network , 2019, European Radiology.

[54]  R. Kwok.  Junior AI researchers are in demand by universities and industry , 2019, Nature.

[55]  Ninon Burgos,et al.  Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation , 2019, Medical Image Anal..

[56]  Nicholas J. Buser,et al.  Variations in structural MRI quality significantly impact commonly used measures of brain anatomy , 2019, bioRxiv.

[57]  G. B. Frisoni,et al.  Dementia imaging in clinical practice: a European-wide survey of 193 centres and conclusions by the ESNR working group , 2019, Neuroradiology.

[58]  Chris Woolston.  Huge variations in US postdoc salaries point to undervalued workforce , 2019, Nature.

[59]  Ruzena Bajcsy,et al.  Fully Automated Echocardiogram Interpretation in Clinical Practice , 2018, Circulation.

[60]  Saori C. Tanaka,et al.  Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias , 2018, bioRxiv.

[61]  S. Rauch,et al.  Clinical Applications of Neuroimaging in Psychiatric Disorders. , 2018, The American journal of psychiatry.

[62]  Falkai Peter,et al.  Forty years of structural brain imaging in mental disorders: is it clinically useful or not? , 2018, Dialogues in clinical neuroscience.

[63]  Erick Jorge Canales-Rodríguez,et al.  Using structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group , 2018, Molecular Psychiatry.

[64]  Bruce R. Rosen,et al.  DeepNeuro: an open-source deep learning toolbox for neuroimaging , 2018, Neuroinformatics.

[65]  Vince D. Calhoun,et al.  Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging , 2018, Front. Neurosci..

[66]  Rose Bruffaerts,et al.  Machine learning in neurology: what neurologists can learn from machines and vice versa , 2018, Journal of Neurology.

[67]  Y. Fang,et al.  Analysis of Misdiagnosis of Bipolar Disorder in An Outpatient Setting , 2018, General Psychiatry.

[68]  Brandon K. Fornwalt,et al.  Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration , 2018, npj Digital Medicine.

[69]  Arcot Sowmya,et al.  Deep fusion pipeline for mild cognitive impairment diagnosis , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[70]  Irene Y. H. Gu,et al.  An efficient 3D deep convolutional network for Alzheimer's disease diagnosis using MR images , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[71]  Bruce R. Rosen,et al.  Distributed deep learning networks among institutions for medical imaging , 2018, J. Am. Medical Informatics Assoc..

[72]  Nick C Fox,et al.  Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference , 2017, Nature Communications.

[73]  Huafu Chen,et al.  Recognition of early-onset schizophrenia using deep-learning method , 2017, Applied Informatics.

[74]  J. Piven,et al.  Brain and behavior development in autism from birth through infancy , 2017, Dialogues in clinical neuroscience.

[75]  Srikrishna Varadarajan,et al.  RADnet: Radiologist level accuracy using deep learning for hemorrhage detection in CT scans , 2017, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[76]  Danni Cheng,et al.  CNNs based multi-modality classification for AD diagnosis , 2017, 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI).

[77]  Dinggang Shen,et al.  Multi-stage Diagnosis of Alzheimer's Disease with Incomplete Multimodal Data via Multi-task Deep Learning , 2017, DLMIA/ML-CDS@MICCAI.

[78]  Christos Davatzikos,et al.  A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages , 2017, NeuroImage.

[79]  Hal Hodson,et al.  Google DeepMind and healthcare in an age of algorithms , 2017, Health and Technology.

[80]  Yulia Dodonova,et al.  Residual and plain convolutional neural networks for 3D brain MRI classification , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[81]  Vince D. Calhoun,et al.  Prediction of Individual Differences from Neuroimaging Data , 2017, NeuroImage.

[82]  Alan C. Evans,et al.  Early brain development in infants at high risk for autism spectrum disorder , 2017, Nature.

[83]  Richard Barnett.  Autism , 2016, The Lancet.

[84]  Keun-Ah Cheon,et al.  Characteristics of Brains in Autism Spectrum Disorder: Structure, Function and Connectivity across the Lifespan , 2015, Experimental neurobiology.

[85]  C. Lehman,et al.  Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection. , 2015, JAMA internal medicine.

[86]  M. Chun,et al.  A neuromarker of sustained attention from whole-brain functional connectivity , 2015, Nature Neuroscience.

[87]  C. Keysers,et al.  Increased Functional Connectivity Between Subcortical and Cortical Resting-State Networks in Autism Spectrum Disorder. , 2015, JAMA psychiatry.

[88]  Vladimir Fonov,et al.  Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge , 2015, NeuroImage.

[89]  H. Vinters,et al.  Emerging concepts in Alzheimer's disease. , 2015, Annual review of pathology.

[90]  Alex Martin,et al.  Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards , 2014, NeuroImage: Clinical.

[91]  Randy L. Gollub,et al.  High Throughput Tools to Access Images from Clinical Archives for Research , 2014, Journal of Digital Imaging.

[92]  Alessandra Retico,et al.  Neuroimaging-based methods for autism identification: a possible translational application? , 2014, Functional neurology.

[93]  Dinggang Shen,et al.  Multiple-Network Classification of Childhood Autism Using Functional Connectivity Dynamics , 2014, MICCAI.

[94]  Vincent Giampietro,et al.  Disorder-specific functional abnormalities during temporal discounting in youth with Attention Deficit Hyperactivity Disorder (ADHD), Autism and comorbid ADHD and Autism , 2014, Psychiatry Research: Neuroimaging.

[95]  Harold Varmus,et al.  Rescuing US biomedical research from its systemic flaws , 2014, Proceedings of the National Academy of Sciences.

[96]  Larry Culpepper,et al.  Misdiagnosis of bipolar depression in primary care practices. , 2014, The Journal of clinical psychiatry.

[97]  Jared A. Nielsen,et al.  Multisite functional connectivity MRI classification of autism: ABIDE results , 2013, Front. Hum. Neurosci..

[98]  Cedric E. Ginestet,et al.  Intrinsic gray-matter connectivity of the brain in adults with autism spectrum disorder , 2013, Proceedings of the National Academy of Sciences.

[99]  Mark H. Johnson,et al.  Intervention for Infants at Risk of Developing Autism: A Case Series , 2013, Journal of autism and developmental disorders.

[100]  V. Giampietro,et al.  Disorder-specific functional abnormalities during sustained attention in youth with Attention Deficit Hyperactivity Disorder (ADHD) and with Autism , 2013, Molecular Psychiatry.

[101]  Mark S. Cohen,et al.  Insights into multimodal imaging classification of ADHD , 2012, Front. Syst. Neurosci..

[102]  Mark E. Schmidt,et al.  The Alzheimer’s Disease Neuroimaging Initiative: A review of papers published since its inception , 2012, Alzheimer's & Dementia.

[103]  V. Giampietro,et al.  Disorder-specific functional abnormalities during sustained attention in youth with Attention Deficit Hyperactivity Disorder (ADHD) and with Autism , 2013, Molecular Psychiatry.

[104]  Clifford R. Jack,et al.  Diagnostic neuroimaging across diseases , 2011, NeuroImage.

[105]  Rafael Malach,et al.  Disrupted Neural Synchronization in Toddlers with Autism , 2011, Neuron.

[106]  John G. Csernansky,et al.  Open Access Series of Imaging Studies: Longitudinal MRI Data in Nondemented and Demented Older Adults , 2010, Journal of Cognitive Neuroscience.

[107]  Tamara Miner Haygood,et al.  Interpretation Time of Computer-aided Detection at Screening Mammography 1 , 2010 .

[108]  J. Martinot,et al.  Brain imaging findings in children and adolescents with mental disorders: A cross-sectional review , 2010, European Psychiatry.

[109]  E. Burnside,et al.  Computer-aided diagnostic models in breast cancer screening. , 2010, Imaging in medicine.

[110]  L. Philpotts,et al.  Can computer-aided detection be detrimental to mammographic interpretation? , 2009, Radiology.

[111]  C. Rowe,et al.  The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer's disease , 2009, International Psychogeriatrics.

[112]  S. Debener,et al.  Default-mode brain dysfunction in mental disorders: A systematic review , 2009, Neuroscience & Biobehavioral Reviews.

[113]  Neil D. Lawrence,et al.  Dataset Shift in Machine Learning , 2009 .

[114]  S. Astley,et al.  Single reading with computer-aided detection for screening mammography. , 2008, The New England journal of medicine.

[115]  Yong He,et al.  Disrupted small-world networks in schizophrenia. , 2008, Brain : a journal of neurology.

[116]  Nick C Fox,et al.  Automatic classification of MR scans in Alzheimer's disease. , 2008, Brain : a journal of neurology.

[117]  Clifford R. Jack,et al.  Alzheimer's disease diagnosis in individual subjects using structural MR images: Validation studies , 2008, NeuroImage.

[118]  C. D'Orsi,et al.  Influence of computer-aided detection on performance of screening mammography. , 2007, The New England journal of medicine.

[119]  Alan C. Evans,et al.  Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI , 2006, NeuroImage.

[120]  Daniel P. Kennedy,et al.  Failing to deactivate: resting functional abnormalities in autism. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[121]  J. Sterne,et al.  Accuracy of magnetic resonance imaging for the diagnosis of multiple sclerosis: systematic review , 2006, BMJ : British Medical Journal.

[122]  Sandra Rees,et al.  Fetal and neonatal origins of altered brain development. , 2005, Early human development.

[123]  Hans-Ulrich Wittchen,et al.  Mental disorders in primary care , 2003, Dialogues in clinical neuroscience.

[124]  Alan C. Evans,et al.  Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis , 2002, IEEE Transactions on Medical Imaging.

[125]  A. Rosen,et al.  Misdiagnosis of schizophrenia in bipolar patients: a multiethnic comparison. , 1983, The American journal of psychiatry.

[126]  R. Gollub,et al.  Use of Medical Imaging to Advance Mental Health Care: Contributions from Neuroimaging Informatics , 2021, Health Informatics.

[127]  Ali Sunyaev,et al.  What Your Radiologist Might be Missing: Using Machine Learning to Identify Mislabeled Instances of X-ray Images , 2021, HICSS.

[128]  Adrian V. Dalca,et al.  SynthSeg: Domain Randomisation for Segmentation of Brain MRI Scans of any Contrast and Resolution , 2021, ArXiv.

[129]  H. Matsuda.  Voxel-based Morphometry of Brain MRI in Normal Aging and Alzheimer's Disease. , 2013, Aging and disease.

[130]  E. Bullmore,et al.  Behavioral / Systems / Cognitive Functional Connectivity and Brain Networks in Schizophrenia , 2010 .