Objective This study aimed to elucidate the hand function recovery capacity of degenerative cervical myelopathy (DCM) patients with different severities of hand dexterity impairment.
Methods Hand functional outcome measures such as the 10-second grip and release (10s-G&R) test, modified Japanese Orthopaedic Association (mJOA) upper extremity score and Japanese Orthopaedic Association Cervical Myelopathy Evaluation Questionnaire (JOACMEQ) upper extremity function were collected before surgery and at the 1-year follow-up. A total of 102 DCM patients were categorized into mild, moderate and severe group based on the preoperative 10s-G&R test result. Hand functional parameters were compared across the 3 groups. Multivariate linear regression was conducted to explore predictive factors. Receiver operating characteristic curve analysis was performed to assess the predictive efficacy of the preoperative 10s-G&R test and establish the cutoff value for incomplete recovery of hand dexterity.
Results At the 1-year follow-up, significant improvements were observed in all hand functional parameters across all 3 groups. However, the incomplete recovery rates of the mild, moderate, severe groups were 26.67%, 46.88%, and 57.50%, respectively (p < 0.05). Multivariate regression revealed that preoperative 10s-G&R test result, age, Hoffmann sign, duration of symptom, and mJOA Upper score serve as significant predictors for postoperative 10s-G&R test outcomes. Patients with a preoperative 10s-G&R test < 15 cycles have a 1.9 times higher risk of incomplete recovery of hand function (p = 0.005).
Conclusion Most patients, regardless of their preoperative hand function, exhibit potential for improvement in hand dexterity. However, worse initial hand dexterity correlates with poorer outcomes. Surgical treatment is recommended before the 10s-G&R test drops below 15 cycles.
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Association between perioperative patient-reported outcomes and upper extremity functional performance in degenerative cervical myelopathy Harshit Arora, Hassan Darabi, Vishwajeet Singh, Aysha Alsahlawi, Jared T. Wilcox, Francis Farhadi North American Spine Society Journal (NASSJ).2026; 25: 100854. CrossRef
Objective Hand clumsiness and reduced hand dexterity can signal early signs of degenerative cervical myelopathy (DCM). While the 10-second grip and release (10-s G&R) test is a common clinical tool for evaluating hand function, a more accessible method is warranted. This study explores the use of deep learning-enhanced hand grip and release test (DL-HGRT) for predicting DCM and evaluates its capability to reduce the duration of the 10-s G&R test.
Methods The retrospective study included 508 DCM patients and 1,194 control subjects. Propensity score matching (PSM) was utilized to minimize the confounding effects related to age and sex. Videos of the 10-s G&R test were captured using a smartphone application. The 3D-MobileNetV2 was utilized for analysis, generating a series of parameters. Additionally, receiver operating characteristic curves were employed to assess the performance of the 10-s G&R test in predicting DCM and to evaluate the effectiveness of a shortened testing duration.
Results Patients with DCM exhibited impairments in most 10-s G&R test parameters. Before PSM, the number of cycles achieved the best diagnostic performance (area under the curve [AUC], 0.85; sensitivity, 80.12%; specificity, 74.29% at 20 cycles), followed by average grip time. Following PSM for age and gender, the AUC remained above 0.80. The average grip time achieved the highest AUC of 0.83 after 6 seconds, plateauing with no significant improvement in extending the duration to 10 seconds, indicating that 6 seconds is an adequate timeframe to efficiently evaluate hand motor dysfunction in DCM based on DL-HGRT.
Conclusion DL-HGRT demonstrates potential as a promising supplementary tool for predicting DCM. Notably, a testing duration of 6 seconds appears to be sufficient for accurate assessment, enhancing the test more feasible and practical without compromising diagnostic performance.
Citations
Citations to this article as recorded by
Utilizing a Novel Combinatorial Physical Performance Test-Based Clinical Assessment Tool to Screen for Radiologically Severe Degenerative Cervical Myelopathy Karlen Ka Pui Law, Kenney Ki Lee Lau, Graham Ka Hon Shea, Kenneth Man Chee Cheung Global Spine Journal.2026; 16(5): 2394. CrossRef
Video-based finger kinematics for degenerative cervical myelopathy: a smartphone-based computer vision approach for screening Viprav B. Raju, Ramesh M. Arnest, Amulya Setlur, Huy Truong, Anjishnu Banerjee, Yin Li, Aditya Vedantam European Spine Journal.2026; 35(3): 1138. CrossRef
Degenerative cervical myelopathy diagnosis: applicability of artificial intelligence-enhanced modalities in a systematic review and meta-analysis Hassan Darabi, Harshit Arora, Ramin Shekouhi, Haydn Hoffman, Reza Forghani, Francis Farhadi European Spine Journal.2026;[Epub] CrossRef
Optimising early detection of degenerative cervical myelopathy: a systematic review of quantitative screening tools for primary care Sean Inzerillo, Pemla Jagtiani, Salazar Jones BMJ Neurology Open.2025; 7(1): e000913. CrossRef
A Commentary on “Deep Learning-Enhanced Hand Grip and Release Test for Degenerative Cervical Myelopathy: Shortening Assessment Duration to 6 Seconds” Renyi Lu, Tian Gao Neurospine.2025; 22(2): 613. CrossRef
Artificial intelligence and machine learning in the management of patients with degenerative cervical myelopathy: a systematic review Vikas N. VATTIPALLY, Ritvik R. JILLALA, Carlos A. AUDE, Arjun K. MENTA, Jacob JO, Liam P. HUGHES, Jawad M. KHALIFEH, Tej D. AZAD Journal of Neurosurgical Sciences.2025;[Epub] CrossRef
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