THERMOGRAPHY ACCURACY IN THE DIAGNOSIS OF CARPAL TUNNEL SYNDROME

Carpal tunnel syndrome (CTS) is a common condition affecting hand function, often requiring accurate diagnostic tools. Electroneuromyography (ENMG) is widely accepted as the gold standard for diagnosing CTS. However, ENMG can be invasive and time-consuming. Non-invasive alternatives like thermography have been proposed to improve diagnostic efficiency. Prior research has shown that thermography can detect temperature variations associated with nerve compression. Yet, the diagnostic accuracy of thermography in CTS remains underexplored. This gap motivated the need for a study comparing thermography to ENMG. No prior work had resolved how well thermography performs in a clinical setting. The study aimed to address this uncertainty by evaluating thermography's reliability in a controlled sample. The results could help determine whether thermography can serve as a viable diagnostic tool. This study contributes to the broader field of diagnostic imaging and clinical neurology.

This study aimed to assess the diagnostic accuracy of thermography in detecting carpal tunnel syndrome (CTS) compared to electroneuromyography (ENMG). The researchers sought to determine whether thermography could serve as a non-invasive alternative to ENMG for diagnosing CTS. The motivation stemmed from the limitations of ENMG, such as invasiveness and time constraints. The study focused on patients already indicated for surgical treatment, ensuring a clinically relevant sample. The goal was to evaluate thermography's sensitivity and specificity in detecting CTS. The study also aimed to quantify diagnostic performance using metrics like area under the curve and F1-score. By comparing thermography to a gold-standard method, the researchers aimed to validate its diagnostic potential. The findings could inform clinical guidelines on the use of thermography in CTS diagnosis.

The study employed a cross-sectional diagnostic accuracy design guided by the GRAAS framework. Participants were recruited from a neurosurgery outpatient clinic and divided into a study group of 17 patients with CTS and a control group of 27 without CTS. Thermographic images were captured using a FLIR® T540 camera. The FLIR Tools+ software was used to measure average temperatures in the region of interest. Artificial Neural Networks were applied to classify the thermographic images. The diagnostic accuracy was evaluated using metrics like sensitivity, specificity, and area under the curve. The study group and control group were compared to assess thermography's performance. The GRAAS guidelines ensured rigorous reporting of reliability and agreement metrics.

The study found that thermography achieved an area under the curve of 0.908, indicating strong diagnostic performance. The precision of the thermography method was 90.81%, and the overall accuracy was 91.66%. Sensitivity reached 87.25%, while specificity was 94.44%. The F1-score was calculated at 88.99%, further supporting the method's reliability. These metrics suggest that thermography can effectively detect CTS. The high specificity indicates a low rate of false positives. The results demonstrate that thermography performs well compared to ENMG. These findings support the potential of thermography as a diagnostic tool.

The authors concluded that thermography shows significant diagnostic accuracy in detecting carpal tunnel syndrome. The high sensitivity and specificity values suggest that thermography can be a reliable diagnostic method. The study supports the use of thermography as a non-invasive alternative to ENMG. The results indicate that thermography may serve as a complementary tool in CTS diagnosis. The authors propose that further studies could explore thermography's utility in different clinical settings. The findings align with the goal of improving diagnostic efficiency without compromising accuracy. The study contributes to the evidence base for thermography in neurological diagnostics. The authors emphasize the need to validate these results in larger populations.