Automated entry of paper-based patient-reported outcomes: Applying deep learning to the Japanese orthopaedic association back pain evaluation questionnaire.

Autor: Kita K; Department of Artificial Intelligence Diagnostic Radiology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan., Fujimori T; Department of Orthopedic Surgery, Graduate School of Medicine, Osaka University, Osaka, Japan. Electronic address: takahito-f@hotmail.co.jp., Suzuki Y; Department of Artificial Intelligence Diagnostic Radiology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan., Kaito T; Department of Orthopedic Surgery, Osaka Rosai Hospital, Osaka, Japan., Takenaka S; Department of Orthopedic Surgery, Japan Community Health Care Organization Osaka Hospital, Osaka, Japan., Kanie Y; Department of Orthopedic Surgery, Graduate School of Medicine, Osaka University, Osaka, Japan., Furuya M; Department of Orthopedic Surgery, Graduate School of Medicine, Osaka University, Osaka, Japan., Wataya T; Department of Artificial Intelligence Diagnostic Radiology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan., Nishigaki D; Department of Artificial Intelligence Diagnostic Radiology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan., Sato J; Department of Artificial Intelligence Diagnostic Radiology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan., Tomiyama N; Department of Artificial Intelligence Diagnostic Radiology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan., Okada S; Department of Orthopedic Surgery, Graduate School of Medicine, Osaka University, Osaka, Japan., Kido S; Department of Artificial Intelligence Diagnostic Radiology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan.
Jazyk: angličtina
Zdroj: Computers in biology and medicine [Comput Biol Med] 2024 Apr; Vol. 172, pp. 108197. Date of Electronic Publication: 2024 Feb 19.
DOI: 10.1016/j.compbiomed.2024.108197
Abstrakt: Background: Health-related patient-reported outcomes (HR-PROs) are crucial for assessing the quality of life among individuals experiencing low back pain. However, manual data entry from paper forms, while convenient for patients, imposes a considerable tallying burden on collectors. In this study, we developed a deep learning (DL) model capable of automatically reading these paper forms.
Methods: We employed the Japanese Orthopaedic Association Back Pain Evaluation Questionnaire, a globally recognized assessment tool for low back pain. The questionnaire comprised 25 low back pain-related multiple-choice questions and three pain-related visual analog scales (VASs). We collected 1305 forms from an academic medical center as the training set, and 483 forms from a community medical center as the test set. The performance of our DL model for multiple-choice questions was evaluated using accuracy as a categorical classification task. The performance for VASs was evaluated using the correlation coefficient and absolute error as regression tasks.
Result: In external validation, the mean accuracy of the categorical questions was 0.997. When outputs for categorical questions with low probability (threshold: 0.9996) were excluded, the accuracy reached 1.000 for the remaining 65 % of questions. Regarding the VASs, the average of the correlation coefficients was 0.989, with the mean absolute error being 0.25.
Conclusion: Our DL model demonstrated remarkable accuracy and correlation coefficients when automatic reading paper-based HR-PROs during external validation.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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