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Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media.

Publication ,  Journal Article
Schaumberg, AJ; Juarez-Nicanor, WC; Choudhury, SJ; Pastrián, LG; Pritt, BS; Prieto Pozuelo, M; Sotillo Sánchez, R; Ho, K; Zahra, N; Sener, BD ...
Published in: Mod Pathol
November 2020

Pathologists are responsible for rapidly providing a diagnosis on critical health issues. Challenging cases benefit from additional opinions of pathologist colleagues. In addition to on-site colleagues, there is an active worldwide community of pathologists on social media for complementary opinions. Such access to pathologists worldwide has the capacity to improve diagnostic accuracy and generate broader consensus on next steps in patient care. From Twitter we curate 13,626 images from 6,351 tweets from 25 pathologists from 13 countries. We supplement the Twitter data with 113,161 images from 1,074,484 PubMed articles. We develop machine learning and deep learning models to (i) accurately identify histopathology stains, (ii) discriminate between tissues, and (iii) differentiate disease states. Area Under Receiver Operating Characteristic (AUROC) is 0.805-0.996 for these tasks. We repurpose the disease classifier to search for similar disease states given an image and clinical covariates. We report precision@k = 1 = 0.7618 ± 0.0018 (chance 0.397 ± 0.004, mean ±stdev ). The classifiers find that texture and tissue are important clinico-visual features of disease. Deep features trained only on natural images (e.g., cats and dogs) substantially improved search performance, while pathology-specific deep features and cell nuclei features further improved search to a lesser extent. We implement a social media bot (@pathobot on Twitter) to use the trained classifiers to aid pathologists in obtaining real-time feedback on challenging cases. If a social media post containing pathology text and images mentions the bot, the bot generates quantitative predictions of disease state (normal/artifact/infection/injury/nontumor, preneoplastic/benign/low-grade-malignant-potential, or malignant) and lists similar cases across social media and PubMed. Our project has become a globally distributed expert system that facilitates pathological diagnosis and brings expertise to underserved regions or hospitals with less expertise in a particular disease. This is the first pan-tissue pan-disease (i.e., from infection to malignancy) method for prediction and search on social media, and the first pathology study prospectively tested in public on social media. We will share data through http://pathobotology.org . We expect our project to cultivate a more connected world of physicians and improve patient care worldwide.

Duke Scholars

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Published In

Mod Pathol

DOI

EISSN

1530-0285

Publication Date

November 2020

Volume

33

Issue

11

Start / End Page

2169 / 2185

Location

United States

Related Subject Headings

  • Social Media
  • Pathology
  • Pathology
  • Pathologists
  • Humans
  • Deep Learning
  • Algorithms
  • 3202 Clinical sciences
  • 11 Medical and Health Sciences
 

Citation

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Schaumberg, A. J., Juarez-Nicanor, W. C., Choudhury, S. J., Pastrián, L. G., Pritt, B. S., Prieto Pozuelo, M., … Fuchs, T. J. (2020). Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media. Mod Pathol, 33(11), 2169–2185. https://doi.org/10.1038/s41379-020-0540-1
Schaumberg, Andrew J., Wendy C. Juarez-Nicanor, Sarah J. Choudhury, Laura G. Pastrián, Bobbi S. Pritt, Mario Prieto Pozuelo, Ricardo Sotillo Sánchez, et al. “Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media.Mod Pathol 33, no. 11 (November 2020): 2169–85. https://doi.org/10.1038/s41379-020-0540-1.
Schaumberg AJ, Juarez-Nicanor WC, Choudhury SJ, Pastrián LG, Pritt BS, Prieto Pozuelo M, et al. Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media. Mod Pathol. 2020 Nov;33(11):2169–85.
Schaumberg, Andrew J., et al. “Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media.Mod Pathol, vol. 33, no. 11, Nov. 2020, pp. 2169–85. Pubmed, doi:10.1038/s41379-020-0540-1.
Schaumberg AJ, Juarez-Nicanor WC, Choudhury SJ, Pastrián LG, Pritt BS, Prieto Pozuelo M, Sotillo Sánchez R, Ho K, Zahra N, Sener BD, Yip S, Xu B, Annavarapu SR, Morini A, Jones KA, Rosado-Orozco K, Mukhopadhyay S, Miguel C, Yang H, Rosen Y, Ali RH, Folaranmi OO, Gardner JM, Rusu C, Stayerman C, Gross J, Suleiman DE, Sirintrapun SJ, Aly M, Fuchs TJ. Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media. Mod Pathol. 2020 Nov;33(11):2169–2185.

Published In

Mod Pathol

DOI

EISSN

1530-0285

Publication Date

November 2020

Volume

33

Issue

11

Start / End Page

2169 / 2185

Location

United States

Related Subject Headings

  • Social Media
  • Pathology
  • Pathology
  • Pathologists
  • Humans
  • Deep Learning
  • Algorithms
  • 3202 Clinical sciences
  • 11 Medical and Health Sciences