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

Kita, Kosuke; Fujimori, Takahito; Suzuki, Yuki; Kaito, Takashi; Takenaka, Shota; Kanie, Yuya; Furuya, Masayuki; Wataya, Tomohiro; Nishigaki, Daiki; Sato, Junya; Tomiyama, Noriyuki; Okada, Seiji; Kido, Shoji

doi:https://doi.org/10.1016/j.compbiomed.2024.108197

アクセス数:14件（2024-06-01 02:37 集計）

固定URL: https://hdl.handle.net/11094/95443

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タイトル	Automated entry of paper-based patient-reported outcomes: Applying deep learning to the Japanese orthopaedic association back pain evaluation questionnaire
著者	著者名 Kita, Kosuke
	NRID 1000080546888 1000080546888 researchmap permalink fujimori_takahito fujimori_takahito Scopus Author ID 35271904600 35271904600 著者名 Fujimori, Takahito
	NRID 1000020845599 1000020845599 researchmap permalink ykszk ykszk Scopus Author ID 57204202740 57204202740 著者名 Suzuki, Yuki
	NRID 1000070623982 1000070623982 ORCID 0000-0003-4882-2997 0000-0003-4882-2997 researchmap permalink 911_0 911_0 Scopus Author ID 25824322100 25824322100 著者名 Kaito, Takashi
	NRID 1000040795861 1000040795861 著者名 Takenaka, Shota
	NRID 1000010896377 1000010896377 researchmap permalink ykanie1985 ykanie1985 著者名 Kanie, Yuya
	NRID 1000020987466 1000020987466 researchmap permalink furuya1019 furuya1019 著者名 Furuya, Masayuki
	NRID 1000090983023 1000090983023 著者名 Wataya, Tomohiro
	著者名 Nishigaki, Daiki
	著者名 Sato, Junya
	NRID 1000050294070 1000050294070 researchmap permalink 3430 3430 Scopus Author ID 7006024942 7006024942 著者名 Tomiyama, Noriyuki
	NRID 1000030448435 1000030448435 ORCID 0000-0002-5107-8209 0000-0002-5107-8209 researchmap permalink okadaseiji okadaseiji Scopus Author ID 35079139100 35079139100 著者名 Okada, Seiji
	NRID 1000090314814 1000090314814 ORCID 0000-0001-5293-4110 0000-0001-5293-4110 researchmap permalink read0196068 read0196068 Scopus Author ID 7004389358 7004389358 著者名 Kido, Shoji
キーワード等	Artificial intelligence
	Back pain
	Convolutional neural network
	Deep learning
	HR-PRO
	JOABPEQ
	Questionnaire
内容	Kita K., Fujimori T., Suzuki Y., et al. Automated entry of paper-based patient-reported outcomes: Applying deep learning to the Japanese orthopaedic association back pain evaluation questionnaire. Computers in Biology and Medicine 172, 108197 (2024); https://doi.org/10.1016/j.compbiomed.2024.108197.
抄録	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.
出版者	Elsevier Ltd
収録物名	Computers in Biology and Medicine
巻(号)	172
ページ	108197
刊行年月	2024-04-01
言語	英語
ハンドルURL	https://hdl.handle.net/11094/95443
PISSN	00104825
eISSN	18790534
関連情報 (isVersionOf)	DOI (出版社版) https://doi.org/10.1016/j.compbiomed.2024.108197
アクセス権	embargoed access
権利情報	© 2024. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
権利情報	The full-text file will be made open to the public on 1 April 2025 in accordance with the publisher's policy.
出版タイプ	Accepted Manuscript
カテゴリ	学術雑誌論文 Journal Article

資源タイプ	学術雑誌論文
ローカル資源タイプ	学術雑誌論文
DCMI資源タイプ	text
DCTERMS.bibliographicCitation	Computers in Biology and Medicine. 2024, 172, p. 108197
DC.title	Automated entry of paper-based patient-reported outcomes: Applying deep learning to the Japanese orthopaedic association back pain evaluation questionnaire
DC.creator	Kita, Kosuke
	Fujimori, Takahito
	Suzuki, Yuki
	Kaito, Takashi
	Takenaka, Shota
	Kanie, Yuya
	Furuya, Masayuki
	Wataya, Tomohiro
	Nishigaki, Daiki
	Sato, Junya
	Tomiyama, Noriyuki
	Okada, Seiji
	Kido, Shoji
DC.publisher	Elsevier Ltd
DC.language' scheme='DCTERMS.RFC1766	英語
DC.type' scheme='DCTERMS.DCMIType	text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2024-04-01
DC.identifier	https://doi.org/10.1016/j.compbiomed.2024.108197
DC.subject	Artificial intelligence
	Back pain
	Convolutional neural network
	Deep learning
	HR-PRO
	JOABPEQ
	Questionnaire
DC.description	Kita K., Fujimori T., Suzuki Y., et al. Automated entry of paper-based patient-reported outcomes: Applying deep learning to the Japanese orthopaedic association back pain evaluation questionnaire. Computers in Biology and Medicine 172, 108197 (2024); https://doi.org/10.1016/j.compbiomed.2024.108197.
DCTERMS.abstract	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.
DC.rights	© 2024. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
DC.rights	The full-text file will be made open to the public on 1 April 2025 in accordance with the publisher's policy.
DC.format	95443
citation_title	Automated entry of paper-based patient-reported outcomes: Applying deep learning to the Japanese orthopaedic association back pain evaluation questionnaire
citation_author	Kita, Kosuke
	Fujimori, Takahito
	Suzuki, Yuki
	Kaito, Takashi
	Takenaka, Shota
	Kanie, Yuya
	Furuya, Masayuki
	Wataya, Tomohiro
	Nishigaki, Daiki
	Sato, Junya
	Tomiyama, Noriyuki
	Okada, Seiji
	Kido, Shoji
citation_publisher	Elsevier Ltd
citation_language	英語
citation_date	2024-04-01
citation_journal_title	Computers in Biology and Medicine
citation_volume	172
citation_firstpage	108197
citation_doi	https://doi.org/10.1016/j.compbiomed.2024.108197
citation_public_url	https://hdl.handle.net/11094/95443

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