Background: Artificial intelligence (AI) has gained tremendous popularity recently, especially the use of natural language processing (NLP). ChatGPT is a state-of-the-art chatbot capable of creating natural conversations using NLP. The use of AI in medicine can have a tremendous impact on health care delivery. Although some studies have evaluated ChatGPT’s accuracy in self-diagnosis, there is no research regarding its precision and the degree to which it recommends medical consultations. Objective: The aim of this study was to evaluate ChatGPT’s ability to accurately and precisely self-diagnose common orthopedic diseases, as well as the degree of recommendation it provides for medical consultations. Methods: Over a 5-day course, each of the study authors submitted the same questions to ChatGPT. The conditions evaluated were carpal tunnel syndrome (CTS), cervical myelopathy (CM), lumbar spinal stenosis (LSS), knee osteoarthritis (KOA), and hip osteoarthritis (HOA). Answers were categorized as either correct, partially correct, incorrect, or a differential diagnosis. The percentage of correct answers and reproducibility were calculated. The reproducibility between days and raters were calculated using the Fleiss κ coefficient. Answers that recommended that the patient seek medical attention were recategorized according to the strength of the recommendation as defined by the study. Results: The ratios of correct answers were 25/25, 1/25, 24/25, 16/25, and 17/25 for CTS, CM, LSS, KOA, and HOA, respectively. The ratios of incorrect answers were 23/25 for CM and 0/25 for all other conditions. The reproducibility between days was 1.0, 0.15, 0.7, 0.6, and 0.6 for CTS, CM, LSS, KOA, and HOA, respectively. The reproducibility between raters was 1.0, 0.1, 0.64, –0.12, and 0.04 for CTS, CM, LSS, KOA, and HOA, respectively. Among the answers recommending medical attention, the phrases “essential,” “recommended,” “best,” and “important” were used. Specifically, “essential” occurred in 4 out of 125, “recommended” in 12 out of 125, “best” in 6 out of 125, and “important” in 94 out of 125 answers. Additionally, 7 out of the 125 answers did not include a recommendation to seek medical attention. Conclusions: The accuracy and reproducibility of ChatGPT to self-diagnose five common orthopedic conditions were inconsistent. The accuracy could potentially be improved by adding symptoms that could easily identify a specific location. Only a few answers were accompanied by a strong recommendation to seek medical attention according to our study standards. Although ChatGPT could serve as a potential first step in accessing care, we found variability in accurate self-diagnosis. Given the risk of harm with self-diagnosis without medical follow-up, it would be prudent for an NLP to include clear language alerting patients to seek expert medical opinions. We hope to shed further light on the use of AI in a future clinical study.

Background: Cervical myelopathy (CM) causes several symptoms such as clumsiness of the hands and often requires surgery. Screening and early diagnosis of CM are important because some patients are unaware of their early symptoms and consult a surgeon only after their condition has become severe. The 10-second hand grip and release test is commonly used to check for the presence of CM. The test is simple but would be more useful for screening if it could objectively evaluate the changes in movement specific to CM. A previous study analyzed finger movements in the 10-second hand grip and release test using the Leap Motion, a noncontact sensor, and a system was developed that can diagnose CM with high sensitivity and specificity using machine learning. However, the previous study had limitations in that the system recorded few parameters and did not differentiate CM from other hand disorders. Objective: This study aims to develop a system that can diagnose CM with higher sensitivity and specificity, and distinguish CM from carpal tunnel syndrome (CTS), a common hand disorder. We then validated the system with a modified Leap Motion that can record the joints of each finger. Methods: In total, 31, 27, and 29 participants were recruited into the CM, CTS, and control groups, respectively. We developed a system using Leap Motion that recorded 229 parameters of finger movements while participants gripped and released their fingers as rapidly as possible. A support vector machine was used for machine learning to develop the binary classification model and calculated the sensitivity, specificity, and area under the curve (AUC). We developed two models, one to diagnose CM among the CM and control groups (CM/control model), and the other to diagnose CM among the CM and non-CM groups (CM/non-CM model). Results: The CM/control model indexes were as follows: sensitivity 74.2%, specificity 89.7%, and AUC 0.82. The CM/non-CM model indexes were as follows: sensitivity 71%, specificity 72.87%, and AUC 0.74. Conclusions: We developed a screening system capable of diagnosing CM with higher sensitivity and specificity. This system can differentiate patients with CM from patients with CTS as well as healthy patients and has the potential to screen for CM in a variety of patients.

Accurate and efficient image segmentation is crucial in anatomy, histology, and pathology research. Conventional manual approaches are time‐consuming, whereas fully automated artificial intelligence segmentation requires substantial manual correction owing to inaccuracy. To address this, we developed SegRef3D, a tool integrating the Segment Anything Model 2 with multiframe tracking and interactive refinement functions, enabling streamlined segmentation workflows for anatomical research. SegRef3D is implemented as a standalone, offline desktop application that operates entirely in a local environment, eliminating the need for cloud‐based services. SegRef3D provides a unified workflow from data import to segmentation, object tracking, refinement, and three‐dimensional model export. Users can specify segmentation prompts through bounding box input, track objects across multiple frames with start–end range selection, and refine results using intuitive Add to Mask and Erase from Mask tools. Up to 20 objects can be handled simultaneously, with each assigned a unique color. The software supports the Standard Tessellation Language output for three‐dimensional modeling and includes volume measurement functions. The SegRef3D prototype, called Seg&Ref, has been applied in studies using serial histological sections, correlative microscopy with block‐face imaging, and pelvic magnetic resonance imaging. Building on these applications, SegRef3D further enhances usability and enables a seamless workflow. SegRef3D offers an accessible, efficient, and accurate segmentation environment tailored for morphological and anatomical studies. Combining artificial intelligence‐powered automatic segmentation with human‐guided refinement in a user‐friendly graphical user interface bridges the gap between research needs and computational methods. By supporting applications that span traditional anatomy and modern pathology, SegRef3D provides a versatile platform for integrative morphological analysis. Its open‐source availability ensures its broad applicability in research, education, and clinical training in the anatomical sciences.

Cardiac amyloidosis is a fatal disease of severe heart failure caused by the accumulation of amyloid in the myocardium. This disease is often advanced by the time cardiac symptoms appear; therefore, early detection and treatment are critical for a good prognosis. Recently, it has been suggested that cardiac amyloidosis is implicated in several orthopedic diseases, including carpal tunnel syndrome (CTS), which is often reported to precede cardiac dysfunction. Shoulder disease has also been suggested to be associated with cardiac amyloidosis; however, there have been no reports investigating the rate of amyloid deposition in shoulder specimens and the simultaneous prevalence of cardiac amyloidosis. Herein, we investigated the prevalence of intraoperative specimen amyloid deposition and cardiac amyloidosis in shoulder disease and CTS to determine the usefulness of shoulder specimen screening as a predictor of cardiac amyloidosis development. A total of 41 patients undergoing arthroscopic shoulder surgery and 33 patients undergoing CTS surgery were enrolled in this study. The shoulder group included rotator cuff tears, contracture of the shoulder, synovitis, and calcific tendonitis. In the shoulder group, a small sample of synovium and the long head of the biceps brachii tendon were harvested, while the transverse carpal ligament was harvested from the CTS group. The intraoperative specimens were pathologically examined for amyloid deposition, and patients with amyloid deposition were examined for the presence of cardiac amyloidosis by cardiac evaluation. In the shoulder group, three cases (7.3%) of transthyretin amyloid deposition were found, all of which involved rotator cuff tears. None of these three cases with amyloid deposition were associated with cardiac amyloidosis. When examining the specimens, the amyloid deposition rate in the long head of the biceps brachii tendon was higher than that in the synovium. In the CTS group, 12 cases (36.4%) of transthyretin amyloid deposition were observed. Of these cases, seven underwent cardiac evaluation and two were identified with cardiac amyloidosis. While the prevalence of amyloid deposition and cardiac amyloidosis in the CTS group was consistent with previous reports, the shoulder group showed a lower deposition rate and no concomitant cardiac amyloidosis. Therefore, it remains debatable whether investigating amyloid deposition in samples obtained from shoulder surgery is beneficial for the early detection of cardiac amyloidosis.

Background: A distal radius fracture (DRF) is a common initial fragility fracture among women in their early postmenopausal period, which is associated with an increased risk of subsequent fractures. Gait assessments are valuable for evaluating fracture risk; inertial measurement units (IMUs) have been widely used to assess gait under free-living conditions. However, little is known about long-term changes in patients with DRF, especially concerning daily-life gait. We hypothesized that, in the long term, the daily-life gait parameters in patients with DRF could enable us to reveal future risk factors for falls and fractures. Objective: This study assessed the spatiotemporal characteristics of patients with DRF at 4 weeks and 6 months of recovery. Methods: We recruited 16 women in their postmenopausal period with DRF as their first fragility fracture (mean age 62.3, SD 7.0 years) and 28 matched healthy controls (mean age 65.6, SD 8.0 years). Daily-life gait assessments and physical assessments, such as hand grip strength (HGS), were performed using an in-shoe IMU sensor. Participants’ results were compared with those of the control group, and their recovery was assessed for 6 months after the fracture. Results: In the fracture group, at 4 weeks after DRF, lower foot height in the swing phase (P=.049) and higher variability of stride length (P=.03) were observed, which improved gradually. However, the dorsiflexion angle in the fracture group tended to be lower consistently during 6 months (at 4 weeks: P=.06; during 6 months: P=.07). As for the physical assessments, the fracture group showed lower HGS at all time points (at 4 weeks: P<.001; during 6 months: P=.04), despite significant improvement at 6 months (P<.001). Conclusions: With an in-shoe IMU sensor, we discovered the recovery of spatiotemporal gait characteristics 6 months after DRF surgery without the participants’ awareness. The consistently unchanged dorsiflexion angle in the swing phase and lower HGS could be associated with fracture risk, implying the high clinical importance of appropriate interventions for patients with DRF to prevent future fractures. These results could be applied to a screening tool for evaluating the risk of falls and fractures, which may contribute to constructing a new health care system using wearable devices in the near future.