Deep Learning-Based Psoriasis Assessment: Harnessing Clinical Trial Imaging for Accurate Psoriasis Area Severity Index Prediction.

Autor:	Xing Y; AbbVie, North Chicago, IL, USA., Zhong S; AbbVie, North Chicago, IL, USA., Aronson SL; AbbVie, North Chicago, IL, USA., Rausa FM; AbbVie, North Chicago, IL, USA., Webster DE; AbbVie, North Chicago, IL, USA., Crouthamel MH; AbbVie, North Chicago, IL, USA., Wang L; AbbVie, North Chicago, IL, USA.
Jazyk:	angličtina
Zdroj:	Digital biomarkers [Digit Biomark] 2024 Mar 04; Vol. 8 (1), pp. 13-21. Date of Electronic Publication: 2024 Mar 04 (Print Publication: 2024).
DOI:	10.1159/000536499
Abstrakt:	Introduction: Image-based machine learning holds great promise for facilitating clinical care; however, the datasets often used for model training differ from the interventional clinical trial-based findings frequently used to inform treatment guidelines. Here, we draw on longitudinal imaging of psoriasis patients undergoing treatment in the Ultima 2 clinical trial (NCT02684357), including 2,700 body images with psoriasis area severity index (PASI) annotations by uniformly trained dermatologists. Methods: An image-processing workflow integrating clinical photos of multiple body regions into one model pipeline was developed, which we refer to as the "One-Step PASI" framework due to its simultaneous body detection, lesion detection, and lesion severity classification. Group-stratified cross-validation was performed with 145 deep convolutional neural network models combined in an ensemble learning architecture. Results: The highest-performing model demonstrated a mean absolute error of 3.3, Lin's concordance correlation coefficient of 0.86, and Pearson correlation coefficient of 0.90 across a wide range of PASI scores comprising disease classifications of clear skin, mild, and moderate-to-severe disease. Within-person, time-series analysis of model performance demonstrated that PASI predictions closely tracked the trajectory of physician scores from severe to clear skin without systematically over- or underestimating PASI scores or percent changes from baseline. Conclusion: This study demonstrates the potential of image processing and deep learning to translate otherwise inaccessible clinical trial data into accurate, extensible machine learning models to assess therapeutic efficacy. Competing Interests: All authors are employees of AbbVie Inc. and may own AbbVie stock. (© 2024 The Author(s). Published by S. Karger AG, Basel.)
Databáze:	MEDLINE
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