野田 昌生

OBJECTIVE: The aim of this study was to investigate the prevalence of frailty and the factors associated with frailty in patients with vestibular hypofunction. DESIGN: This observational study included 185 individuals with dizziness aged 40 and above who suffered from chronic vestibular hypofunction. We defined frailty using the diagnostic algorithm by the revised Japanese version of the Cardiovascular Health Study criteria. Frailty, prefrailty, and robust were defined as including 3 to 5, 1 to 2, and 0 points, respectively. For comparison, we also assessed the prevalence of frailty in community-dwelling adults over 40 years old (control group, n = 203). RESULTS: The average ages for the groups with vestibular hypofunction and the control were 72.0 ± 10.1 and 69.8 ± 8.2 years, respectively. In the vestibular hypofunction group (185 patients), 32 were identified as frail (17.3%) and 103 as prefrail (55.7%). Of the patients with vestibular hypofunction aged 65 years or older (n = 151), 31 (20.5%) were frail and 80 (53.0%) were prefrail. In the control group, consisting of 203 community-dwelling adults, 15 were identified as frail (7.0%) and 108 as prefrail (54.0%). Among patients with vestibular hypofunction, 64 (34.6%) exhibited low gait speed, the most common of the frailty components. Age, female, Hospital Anxiety and Depression Scale-Depression subscale, and Dizziness Handicap Inventory were associated with frailty and prefrailty in patients with vestibular hypofunction, after adjustment for confounding factors. CONCLUSIONS: The present study demonstrates that the prevalence of frailty in patients with vestibular hypofunction is higher than that in community-dwelling adults. Therefore, evaluating frailty in patients with vestibular hypofunction is crucial for identifying those at higher risk and implementing early interventions such as dietary guidance and exercises to strengthen the lower body along with vestibular rehabilitation.

BackgroundSubjective visual vertical (SVV) test is a key functional assessment tool that provides insights into vestibular imbalance. Mobile virtual reality SVV measurement system (MVR-SVV) has the potential to facilitate simple, low-cost, and reliable measurements.ObjectiveThis study aimed to verify the reliability and validity of MVR-SVV by comparing its data with the previously established bucket test (bucket-SVV).MethodsThirty-eight healthy adults completed both bucket-SVV and MVR-SVV tests. The reliability and validity of MVR-SVV were examined using intraclass correlation coefficients (ICCs), Pearson's correlation, Bland-Altman plots (BAP), and minimum detectable change (MDC).ResultsBAP results indicated that the limits of agreement for the SVV angles were 1.61 to -1.24°. No fixed errors were identified (p = 0.13), although a small proportional error was detected (y = -0.59x + 0.157, p < 0.001). Pearson's correlation coefficient between bucket-SVV and MVR-SVV was 0.716 (p < 0.001). Within-day reliability was poor for bucket-SVV, with ICC = 0.33-0.38, but moderate for MVR-SVV, with ICC = 0.70-0.71. Between-day reliability was poor for both methods, with ICC = 0.38 for MVR-SVV and ICC = 0.28 for bucket-SVV. MDC was 1.78° for bucket-SVV and 2.67° for MVR-SVV.ConclusionsOur findings suggest that MVR-SVV can be used for assessing SVV. Its portability, availability, and reliability make it a valuable tool for clinicians in clinical settings.

BACKGROUND: The widespread adoption of smartphones and tablet devices, along with advancements in data communication technology, has resulted in a paradigm shift in the treatment of dizziness. External factors, such as the spread of COVID-19, have accelerated this transformation in recent years. We have been pursuing telemedicine and web-based medical care to treat dizziness and have developed different products and services necessary for each treatment process stage. Several patients face difficulties in accessing medical facilities during severe vertigo episodes. Furthermore, clinical findings, such as nystagmus or other symptoms, may be absent when symptoms subside by the time of their appointment. OBJECTIVE: This study aimed to develop a smartphone app for capturing eye movements and head positions during vertigo attacks, enabling recordings anywhere, even at home or work. METHODS: We developed an app named "iCapNYS" that uses the iPhone's front camera and gyro sensor to record eye movements and head positions. The app incorporates features designed to encourage spontaneous eye movements, minimizing nystagmus suppression caused by fixation. Additionally, we designed lightweight, recyclable cardboard goggles to securely hold the smartphone and block visual stimuli from the surrounding environment, optimizing the recording conditions. RESULTS: The "iCapNYS" system successfully captured subtle peripheral vestibular nystagmus in a patient with vertigo. The recorded nystagmus characteristics are comparable to those obtained using traditional infrared CCD (charge-coupled device) cameras. CONCLUSIONS: This app is an effective tool for treating vertigo and is easy for older adults to use, as it can be recorded with only 3 taps. We expect that the introduction of this nystagmus-monitoring system will improve vertigo treatment quality, promote medical collaboration, and provide patients with peace of mind in their care.

BACKGROUND: Conventional nystagmus classification methods often rely on subjective observation by specialists, which is time-consuming and variable among clinicians. Recently, deep learning techniques have been used to automate nystagmus classification using convolutional and recurrent neural networks. These networks can accurately classify nystagmus patterns using video data. However, associated challenges including the need for large datasets when creating models, limited applicability to address specific image conditions, and the complexity associated with using these models. OBJECTIVE: This study aimed to evaluate a novel approach for nystagmus classification that used the Generative Pre-trained Transformer 4 Vision (GPT-4V) model, which is a state-of-the-art large-scale language model with powerful image recognition capabilities. METHODS: We developed a pupil-tracking process using a nystagmus-recording video and verified the optimization model's accuracy using GPT-4V classification and nystagmus recording. We tested whether the created optimization model could be evaluated in six categories of nystagmus: right horizontal, left horizontal, upward, downward, right torsional, and left torsional. The traced trajectory was input as two-dimensional coordinate data or an image, and multiple in-context learning methods were evaluated. RESULTS: The developed model showed an overall classification accuracy of 37% when using pupil-traced images and a maximum accuracy of 24.6% when pupil coordinates were used as input. Regarding orientation, we achieved a maximum accuracy of 69% for the classification of horizontal nystagmus patterns but a lower accuracy for the vertical and torsional components. CONCLUSIONS: We demonstrated the potential of versatile vertigo management in a generative artificial intelligence model that improves the accuracy and efficiency of nystagmus classification. We also highlighted areas for further improvement, such as expanding the dataset size and enhancing input modalities, to improve classification performance across all nystagmus types. The GPT-4V model validated only for recognizing still images can be linked to video classification and proposed as a novel method.

BACKGROUND: Paediatric cervical abscesses necessitate careful assessment to determine appropriate treatment strategies. Some patients require surgical intervention, although conservative management is effective. However, the criteria for the surgical indications remain unclear. Machine learning models have demonstrated promise in improving diagnostic accuracy across different medical fields. OBJECTIVE: This study aimed to assess the use of machine learning models in predicting the requirement for surgical intervention in paediatric cervical abscesses and compare their performance with that of traditional logistic regression. METHODS: A retrospective analysis was conducted on 55 paediatric patients diagnosed with cervical abscesses between 2010 and 2024. The patient demographics, clinical findings, laboratory data, and imaging characteristics were examined. Six predictive models were developed: logistic regression, Random Forest, Lasso regression, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine. Model performance was evaluated using the area under the curve (AUC), accuracy, precision, recall, and F1-score. Feature importance was examined to identify the main predictive factors. RESULTS: Among all the factors, abscess size was the most significant predictor of surgical intervention. Machine-learning models, especially XGBoost, outperformed logistic regression, achieving the highest AUC, accuracy, and recall. Inflammatory markers, including neutrophil-to-lymphocyte ratio and neutrophil count, also substantially contributed to the prediction accuracy. CONCLUSION: Machine learning models, particularly XGBoost, provide superior predictive performance compared with logistic regression, providing a valuable tool for optimising treatment decisions in paediatric cervical abscesses. These models improve clinical decision-making by integrating multiple factors, decreasing unnecessary surgeries, and enhancing patient outcomes.