Evidence map›Paper›PMID 42057064›Full record

ArticleBMC musculoskeletal disorders2026

Machine learning-based prediction model for paresthesia improvement after open carpal tunnel release: a preliminary study.

Takahiro Yamazaki, Yusuke Matsuura, Takuto Takeda, Seiji Ohtori, Takane Suzuki

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Article in BMC musculoskeletal disorders, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Takahiro YamazakiDepartment of Orthopaedic Surgery, Graduate school of Medicine, Chiba University, 1-8-1 Inohana Chuo-ku, Chiba, Chiba, 260-8670, Japan. taka-0407@hotmail.co.jp.
Yusuke MatsuuraDepartment of Orthopaedic Surgery, Graduate school of Medicine, Chiba University, 1-8-1 Inohana Chuo-ku, Chiba, Chiba, 260-8670, Japan.
Takuto TakedaDepartment of Orthopaedic Surgery, Graduate school of Medicine, Chiba University, 1-8-1 Inohana Chuo-ku, Chiba, Chiba, 260-8670, Japan.
Seiji OhtoriDepartment of Orthopaedic Surgery, Graduate school of Medicine, Chiba University, 1-8-1 Inohana Chuo-ku, Chiba, Chiba, 260-8670, Japan.
Takane SuzukiDepartment of Orthopaedic Surgery, Graduate school of Medicine, Chiba University, 1-8-1 Inohana Chuo-ku, Chiba, Chiba, 260-8670, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCarpal tunnel syndrome (CTS) is the most common entrapment neuropathy of the hand. While carpal tunnel release surgery generally provides good outcomes, some patients continue to experience persistent paresthesia postoperatively. This study aimed to develop a machine learning-based prediction model for paresthesia improvement following carpal tunnel surgery.

methodsWe retrospectively analyzed 94 hands from 83 patients who underwent carpal tunnel release surgery between April 2021 and March 2024. Paresthesia improvement was evaluated at 1-year follow-up using a 10-point scale, with excellent improvement defined as scores 0-1. Three machine learning algorithms (Support Vector Machine, Random Forest, and Logistic Regression) were compared. Model performance was evaluated using accuracy, precision, recall, F1-score, and area under the ROC curve (AUC).

resultsOf 94 hands, 53 (56.4%) achieved excellent paresthesia improvement. The optimized SVM model demonstrated the best performance with an AUC of 0.852, accuracy of 78.9%, precision of 88.9%, and recall of 72.7%. The five most important predictive factors were: distal motor latency (DML), age, disease duration, thenar muscle atrophy, and thumb opposition deficit.

conclusionsOur preliminary machine learning model successfully predicted excellent paresthesia improvement after carpal tunnel surgery with good accuracy using five easily obtainable clinical parameters. This pilot study provides a foundation for developing more robust prediction tools that could assist in preoperative counseling and treatment planning. CLINICAL

trial registrationClinical trial number: not applicable. This study was a retrospective analysis and not a clinical trial.

Indexed as

Carpal Tunnel SyndromeMachine LearningParesthesiaAdultAgedClassification AlgorithmsFemaleHumansMaleMiddle AgedPrediction AlgorithmsPredictive Learning ModelsRandom ForestRetrospective StudiesSupport Vector MachineTreatment OutcomeCarpal tunnel syndromeMachine learningParesthesiaPrediction modelSurgical outcomes

Identifiers

PMID42057064
PMCPMC13274145

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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.