Background: This study aimed to examine whether robotic self-training improved upper-extremity function versus conventional self-training in mild-to-moderate hemiplegic chronic stroke patients.Methods: Study design was a multi-center, prospective, randomized, parallel-group study comparing three therapist-guided interventions (1-hour sessions, 3×/wk, 10 weeks). We identified 161 prospective patients with chronic, poststroke, upper-limb hemiplegia treated at participating rehabilitation centers. Patients were enrolled between November 29, 2016, and November 12, 2018 in Japan. A blinded web-based allocation system was used to randomly assign 129 qualifying patients into 3 groups: (1) conventional self-training plus conventional therapy (control, N=42); (2) robotic self-training (ReoGo-J) plus conventional therapy (robotic therapy [RT], N=44); or (3) robotic self-training plus constraint-induced movement therapy (N=43). Primary outcome: Fugl-Meyer Assessment for upper-extremity. Secondary outcomes: Motor Activity Log-14 amount of use and quality of movement; Fugl-Meyer Assessment shoulder/elbow/forearm, wrist, finger, and coordination scores; Action Research Arm Test Score; Motricity Index; Modified Ashworth Scale; shoulder, elbow, forearm, wrist, and finger range of motion; and Stroke Impact Scale (the assessors were blinded). Safety outcomes were adverse events.Results: Safety was assessed in 127 patients. An intention-to-treat full analysis set (N=121), and a per-protocol set (N=115) of patients who attended 80% of sessions were assessed. One severe adverse event was recorded, unrelated to the robotic device. No significant differences in Fugl-Meyer Assessment for upper-extremity scores were observed between groups (RT versus control: −1.04 [95% CI, −2.79 to 0.71],P=0.40; RT versus movement therapy: −0.33 [95% CI, −2.02 to 1.36],P=0.90). The RT in the per-protocol set improved significantly in the Fugl-Meyer Assessment for upper-extremity shoulder/elbow/forearm score (RT versus control: −1.46 [95% CI, −2.63 to −0.29];P=0.037).Conclusions: Robotic self-training did not improve upper-limb function versus usual self-training, but may be effective combined with conventional therapy in some populations (per-protocol set).Registration: URL:https://www.umin.ac.jp/ctr; Unique identifier: UMIN000022509.

Background: The integration of artificial intelligence (AI), particularly deep learning models, has transformed the landscape of medical technology, especially in the field of diagnosis using imaging and physiological data. In otolaryngology, AI has shown promise in image classification for middle ear diseases. However, existing models often lack patient-specific data and clinical context, limiting their universal applicability. The emergence of GPT-4 Vision (GPT-4V) has enabled a multimodal diagnostic approach, integrating language processing with image analysis. Objective: In this study, we investigated the effectiveness of GPT-4V in diagnosing middle ear diseases by integrating patient-specific data with otoscopic images of the tympanic membrane. Methods: The design of this study was divided into two phases: (1) establishing a model with appropriate prompts and (2) validating the ability of the optimal prompt model to classify images. In total, 305 otoscopic images of 4 middle ear diseases (acute otitis media, middle ear cholesteatoma, chronic otitis media, and otitis media with effusion) were obtained from patients who visited Shinshu University or Jichi Medical University between April 2010 and December 2023. The optimized GPT-4V settings were established using prompts and patients’ data, and the model created with the optimal prompt was used to verify the diagnostic accuracy of GPT-4V on 190 images. To compare the diagnostic accuracy of GPT-4V with that of physicians, 30 clinicians completed a web-based questionnaire consisting of 190 images. Results: The multimodal AI approach achieved an accuracy of 82.1%, which is superior to that of certified pediatricians at 70.6%, but trailing behind that of otolaryngologists at more than 95%. The model’s disease-specific accuracy rates were 89.2% for acute otitis media, 76.5% for chronic otitis media, 79.3% for middle ear cholesteatoma, and 85.7% for otitis media with effusion, which highlights the need for disease-specific optimization. Comparisons with physicians revealed promising results, suggesting the potential of GPT-4V to augment clinical decision-making. Conclusions: Despite its advantages, challenges such as data privacy and ethical considerations must be addressed. Overall, this study underscores the potential of multimodal AI for enhancing diagnostic accuracy and improving patient care in otolaryngology. Further research is warranted to optimize and validate this approach in diverse clinical settings.

Objectives: The objective of this study was to evaluate the predictive precision of combining the corticospinal tract lesion load (CST-LL) with the diffusion-tensor fractional anisotropy of the corticospinal tract (CST-FA) in the lesioned hemispheres regarding motor outcomes.Methods: Patients with putaminal and/or thalamic hemorrhage who had undergone computed tomography (CT) soon after onset in our hospital were retrospectively enrolled. The CST-LL was calculated after registration of the CT images to a standard brain. Diffusion-tensor imaging was performed during the second week after onset. Standardized automated tractography was employed to calculate the CST-FA. Outcomes were assessed at discharge from our affiliated rehabilitation facility using total scores of the motor component of the Stroke Impairment Assessment Set (SIAS-motor total; null to full, 0 to 25). Multivariate regression analysis was performed with CST-LL and CST-FA as explanatory variables and SIAS-motor total as a target value.Results: Twenty-five patients participated in this study. SIAS-motor total ranged from 0 to 25 (median, 17). CST-LL ranged from 0.298 to 7.595 (median, 2.522) mL, and the lesion-side CST-FA ranged from 0.211 to 0.530 (median, 0.409). Analysis revealed that both explanatory variables were detected as statistically significant contributory factors. The estimated t values indicated that the contributions of these two variables were almost equal. The obtained regression model accounted for 63.9% of the variability of the target value.Conclusions: Incorporation of the CST-LL with the lesion-side CST-FA enhances the precision of the stroke outcome prediction model.

In the assessment of skeletal muscle strength, rate of force development (RFD) is clinically identified as a functional index that reflects the effects of aging, but there are few reports on RFD of the tongue. The purpose of this study was to examine the relationship between RFD of tongue pressure (RFD-TP) and oral and whole-body physical performance in older adults, and to clarify its characteristics. We enrolled adults aged ≥65 years with pathological occlusal contact in premolar and molar regions of teeth in the Tamba-Sasayama area, Japan, from 2017 to 2018. Maximum tongue pressure (MTP) and the speed to reach the maximum tongue pressure (RFD-TP) were evaluated as measures of tongue function. Oral functions related to objective measures of tongue function, such as repetitive saliva swallowing test, oral diadochokinesis, and physical status or performance, such as mini mental state examination, body mass index, skeletal mass index, knee extension force, one-leg standing time, grip strength, walking speed, timed up-and-go test, and five-time chair stand speed was evaluated. No significant correlation was found between MTP and age, but RFD-TP had a significant negative correlation with age. Neither RFD-TP nor MTP showed a significant correlation with oral function. RFD-TP was associated with physical performance, such as knee extension force and one-leg standing time. RFD-TP is more sensitive to aging than MTP. In addition, RFD-TP is related to physical performance and may be useful for the early detection of frailty.