Evidence map›Paper›PMID 32255417›Full record

ArticleRadiology2020

Artificial Intelligence System Approaching Neuroradiologist-level Differential Diagnosis Accuracy at Brain MRI.

Andreas M Rauschecker, Jeffrey D Rudie, Long Xie, Jiancong Wang, Michael Tran Duong, Emmanuel J Botzolakis, Asha M Kovalovich, John Egan, Tessa C Cook, R Nick Bryan and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
71citing papers in PubMed, 5 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

71 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Guideline
  2. The application value of Rs-fMRI-based machine learning models for differentiating mild cognitive impairment from Alzheimer's disease: a systematic review and meta-analysis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025
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  6. Artificial Intelligence in the Training of Radiology Residents: a Multicenter Randomized Controlled Trial.Journal of cancer education : the official journal of the American Association for Cancer Education · 2025
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11 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Andreas M Rauschecker *From the Department of Radiology & Biomedical Imaging, University of California, San Francisco, 513 Parnassus Ave, Room S-261, Box 0628, San Francisco, CA 94143-0628 (A.M.R.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104 (A.M.R., J.D.R., L.X., J.W., M.T.D., A.M.K., J.E., T.C.C., I.M.N., S.M., J.C.G.); Mecklenburg Radiology Associates, Charlotte, NC (E.J.B.); and Department of Radiology, University of Texas at Austin, Austin, TX (R.N.B.).ORCID 0000-0003-0633-9876
Jeffrey D Rudie *From the Department of Radiology & Biomedical Imaging, University of California, San Francisco, 513 Parnassus Ave, Room S-261, Box 0628, San Francisco, CA 94143-0628 (A.M.R.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104 (A.M.R., J.D.R., L.X., J.W., M.T.D., A.M.K., J.E., T.C.C., I.M.N., S.M., J.C.G.); Mecklenburg Radiology Associates, Charlotte, NC (E.J.B.); and Department of Radiology, University of Texas at Austin, Austin, TX (R.N.B.).ORCID 0000-0001-8609-8421
Long XieFrom the Department of Radiology & Biomedical Imaging, University of California, San Francisco, 513 Parnassus Ave, Room S-261, Box 0628, San Francisco, CA 94143-0628 (A.M.R.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104 (A.M.R., J.D.R., L.X., J.W., M.T.D., A.M.K., J.E., T.C.C., I.M.N., S.M., J.C.G.); Mecklenburg Radiology Associates, Charlotte, NC (E.J.B.); and Department of Radiology, University of Texas at Austin, Austin, TX (R.N.B.).ORCID 0000-0002-7184-7028
Jiancong WangFrom the Department of Radiology & Biomedical Imaging, University of California, San Francisco, 513 Parnassus Ave, Room S-261, Box 0628, San Francisco, CA 94143-0628 (A.M.R.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104 (A.M.R., J.D.R., L.X., J.W., M.T.D., A.M.K., J.E., T.C.C., I.M.N., S.M., J.C.G.); Mecklenburg Radiology Associates, Charlotte, NC (E.J.B.); and Department of Radiology, University of Texas at Austin, Austin, TX (R.N.B.).ORCID 0000-0002-3455-7750
Michael Tran DuongFrom the Department of Radiology & Biomedical Imaging, University of California, San Francisco, 513 Parnassus Ave, Room S-261, Box 0628, San Francisco, CA 94143-0628 (A.M.R.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104 (A.M.R., J.D.R., L.X., J.W., M.T.D., A.M.K., J.E., T.C.C., I.M.N., S.M., J.C.G.); Mecklenburg Radiology Associates, Charlotte, NC (E.J.B.); and Department of Radiology, University of Texas at Austin, Austin, TX (R.N.B.).ORCID 0000-0003-0312-5530
Emmanuel J BotzolakisFrom the Department of Radiology & Biomedical Imaging, University of California, San Francisco, 513 Parnassus Ave, Room S-261, Box 0628, San Francisco, CA 94143-0628 (A.M.R.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104 (A.M.R., J.D.R., L.X., J.W., M.T.D., A.M.K., J.E., T.C.C., I.M.N., S.M., J.C.G.); Mecklenburg Radiology Associates, Charlotte, NC (E.J.B.); and Department of Radiology, University of Texas at Austin, Austin, TX (R.N.B.).ORCID 0000-0002-2494-9670
Asha M KovalovichFrom the Department of Radiology & Biomedical Imaging, University of California, San Francisco, 513 Parnassus Ave, Room S-261, Box 0628, San Francisco, CA 94143-0628 (A.M.R.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104 (A.M.R., J.D.R., L.X., J.W., M.T.D., A.M.K., J.E., T.C.C., I.M.N., S.M., J.C.G.); Mecklenburg Radiology Associates, Charlotte, NC (E.J.B.); and Department of Radiology, University of Texas at Austin, Austin, TX (R.N.B.).
John EganFrom the Department of Radiology & Biomedical Imaging, University of California, San Francisco, 513 Parnassus Ave, Room S-261, Box 0628, San Francisco, CA 94143-0628 (A.M.R.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104 (A.M.R., J.D.R., L.X., J.W., M.T.D., A.M.K., J.E., T.C.C., I.M.N., S.M., J.C.G.); Mecklenburg Radiology Associates, Charlotte, NC (E.J.B.); and Department of Radiology, University of Texas at Austin, Austin, TX (R.N.B.).ORCID 0000-0001-8299-4587
Tessa C CookFrom the Department of Radiology & Biomedical Imaging, University of California, San Francisco, 513 Parnassus Ave, Room S-261, Box 0628, San Francisco, CA 94143-0628 (A.M.R.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104 (A.M.R., J.D.R., L.X., J.W., M.T.D., A.M.K., J.E., T.C.C., I.M.N., S.M., J.C.G.); Mecklenburg Radiology Associates, Charlotte, NC (E.J.B.); and Department of Radiology, University of Texas at Austin, Austin, TX (R.N.B.).ORCID 0000-0002-6007-7267
R Nick BryanFrom the Department of Radiology & Biomedical Imaging, University of California, San Francisco, 513 Parnassus Ave, Room S-261, Box 0628, San Francisco, CA 94143-0628 (A.M.R.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104 (A.M.R., J.D.R., L.X., J.W., M.T.D., A.M.K., J.E., T.C.C., I.M.N., S.M., J.C.G.); Mecklenburg Radiology Associates, Charlotte, NC (E.J.B.); and Department of Radiology, University of Texas at Austin, Austin, TX (R.N.B.).
Ilya M NasrallahFrom the Department of Radiology & Biomedical Imaging, University of California, San Francisco, 513 Parnassus Ave, Room S-261, Box 0628, San Francisco, CA 94143-0628 (A.M.R.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104 (A.M.R., J.D.R., L.X., J.W., M.T.D., A.M.K., J.E., T.C.C., I.M.N., S.M., J.C.G.); Mecklenburg Radiology Associates, Charlotte, NC (E.J.B.); and Department of Radiology, University of Texas at Austin, Austin, TX (R.N.B.).ORCID 0000-0003-2346-7562
Suyash MohanFrom the Department of Radiology & Biomedical Imaging, University of California, San Francisco, 513 Parnassus Ave, Room S-261, Box 0628, San Francisco, CA 94143-0628 (A.M.R.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104 (A.M.R., J.D.R., L.X., J.W., M.T.D., A.M.K., J.E., T.C.C., I.M.N., S.M., J.C.G.); Mecklenburg Radiology Associates, Charlotte, NC (E.J.B.); and Department of Radiology, University of Texas at Austin, Austin, TX (R.N.B.).ORCID 0000-0002-4025-115X
James C GeeFrom the Department of Radiology & Biomedical Imaging, University of California, San Francisco, 513 Parnassus Ave, Room S-261, Box 0628, San Francisco, CA 94143-0628 (A.M.R.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104 (A.M.R., J.D.R., L.X., J.W., M.T.D., A.M.K., J.E., T.C.C., I.M.N., S.M., J.C.G.); Mecklenburg Radiology Associates, Charlotte, NC (E.J.B.); and Department of Radiology, University of Texas at Austin, Austin, TX (R.N.B.).

Funding

Biomedical Imaging for Clinician ScientistsT32EB001631 · NIBIB · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI THOMAS M LINK · 2005 to 2026
$5.6M
RESEARCH TRACK RADIOLOGY RESIDENCYT32EB004311 · NIBIB · UNIVERSITY OF PENNSYLVANIA · PI Terence P Gade, Misun Hwang · 2005 to 2026
$4.5M
NIBIB NIH HHS T32 EB001631NIBIB NIH HHS T32 EB004311
6 · The paper itself

Abstract

Background Although artificial intelligence (AI) shows promise across many aspects of radiology, the use of AI to create differential diagnoses for rare and common diseases at brain MRI has not been demonstrated. Purpose To evaluate an AI system for generation of differential diagnoses at brain MRI compared with radiologists. Materials and Methods This retrospective study tested performance of an AI system for probabilistic diagnosis in patients with 19 common and rare diagnoses at brain MRI acquired between January 2008 and January 2018. The AI system combines data-driven and domain-expertise methodologies, including deep learning and Bayesian networks. First, lesions were detected by using deep learning. Then, 18 quantitative imaging features were extracted by using atlas-based coregistration and segmentation. Third, these image features were combined with five clinical features by using Bayesian inference to develop probability-ranked differential diagnoses. Quantitative feature extraction algorithms and conditional probabilities were fine-tuned on a training set of 86 patients (mean age, 49 years ± 16 [standard deviation]; 53 women). Accuracy was compared with radiology residents, general radiologists, neuroradiology fellows, and academic neuroradiologists by using accuracy of top one, top two, and top three differential diagnoses in 92 independent test set patients (mean age, 47 years ± 18; 52 women). Results For accuracy of top three differential diagnoses, the AI system (91% correct) performed similarly to academic neuroradiologists (86% correct;

Indexed as

Artificial IntelligenceAdultAgedBrainBrain DiseasesDiagnosis, Computer-AssistedDiagnosis, DifferentialFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedRare DiseasesRetrospective StudiesSensitivity and Specificity

Identifiers

PMID32255417
PMCPMC7263320

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.