Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: The subjective visual vertical (SVV) test can evaluate otolith function and spatial awareness and is performed in dedicated vertigo centers using specialized equipment; however, it is not otherwise widely used because of the specific equipment and space requirements. An SVV test smartphone app was developed to easily perform assessments in outpatient facilities. Objective: This study aimed to verify whether the SVV test smartphone app with commercially available virtual reality goggles can be used in a clinical setting. Methods: The reference range was calculated for 15 healthy participants. We included 14 adult patients with unilateral vestibular neuritis, sudden sensorineural hearing loss with vertigo, and Meniere disease and investigated the correlation between the SVV test results and vestibular evoked myogenic potential (VEMP) results. Results: The SVV reference range of healthy participants for the sitting front-facing position was small, ranging from –2.6º to 2.3º. Among the 14 patients, 6 (43%) exceeded the reference range for healthy participants. The SVV of patients with vestibular neuritis and sudden sensorineural hearing loss tended to deviate to the affected side. A total of 9 (64%) had abnormal cervical VEMP (cVEMP) values and 6 (43%) had abnormal ocular VEMP (oVEMP) values. No significant difference was found between the presence or absence of abnormal SVV values and the presence or absence of abnormal cVEMP and oVEMP values; however, the odds ratios (ORs) suggested a higher likelihood of abnormal SVV values among those with abnormal cVEMP and oVEMP responses (OR 2.40, 95% CI 0.18-32.88; P>.99; and OR 2, 95% CI 0.90-4.45; P=.46, respectively). Conclusions: The SVV app can be used anywhere and in a short period while reducing directional bias by using virtual reality goggles, thus making it highly versatile and useful as a practical otolith dysfunction screening tool.

We introduce here an easy but effective method for detailed observation of the larynx and hypopharynx. During the endoscopic observation, the patient's head is turned to one side. Anatomical structures on the same side of the endolarynx, such as the laryngeal ventricle and inferior surface of the vocal fold, are easily observed. In addition, observation of the opposite side of the hypopharynx also becomes easier. Such head turning is also useful in patients with an oblique larynx, in whom the epiglottis obstructs insertion of the endoscope. This is a simple but very effective technique for laryngeal and hypopharyngeal observation.

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.

Although dizziness is one of the most common symptoms of menopause, the underlying mechanism is not precisely known. Therefore, this study aimed to investigate the prevalence of, and the factors associated with, dizziness in peri- and postmenopausal women. We conducted a cross-sectional study in which we analyzed the first-visit records of 471 Japanese women aged 40 to 65 years who enrolled in a health and nutrition education program at a menopause clinic. The prevalence of dizziness was estimated according to the participants’ responses to the Menopausal Health-Related Quality of Life Questionnaire. The background characteristics of age, menopause status, body composition, cardiovascular parameters, basal metabolism, and physical fitness; other menopausal symptoms, including vasomotor, insomnia, depression, and anxiety symptoms; and lifestyle characteristics were assessed for their associations with dizziness. The percentage of women who suffered from dizziness once a week or more frequently was 35.7%. Compared to the women without dizziness, those with the symptom were younger; had a higher body weight, body mass index, body fat percentage, muscle mass, and waist-to-hip ratio; had higher systolic pressure; were slower in reaction time; had higher physical and psychological symptom scores of menopause; exercised less regularly; and consumed less alcohol. A multivariate logistic regression analysis revealed that the anxiety symptom, which was evaluated by the Hospital Anxiety and Depression Scale, was the sole factor that was independently associated with dizziness (adjusted odds ratio 1.14; 95% confidence interval 1.08–1.20). Dizziness is highly prevalent in Japanese peri- and postmenopausal women and it is associated with anxiety. The treatment of anxiety in this population might improve the symptom.

Background: Observing eye movements during episodic vertigo attacks is crucial for accurately diagnosing vestibular disorders. In clinical practice, many cases lack observable symptoms or clear findings during outpatient examinations, leading to diagnostic challenges. An accurate diagnosis is essential for timely treatment, as conditions such as benign paroxysmal positional vertigo (BPPV), Ménière’s disease, and vestibular migraine require different therapeutic approaches. Objective: This study aimed to develop and evaluate a cost-effective diagnostic tool that integrates a mini-infrared camera with 3D-printed goggles, enabling at-home recording of nystagmus during vertigo attacks. Methods: A commercially available mini-infrared camera (US $25) was combined with 3D-printed goggles (US $13) to create a system for recording eye movements in dark conditions. A case study was conducted on a male patient in his 40s who experienced recurrent episodic vertigo. Results: Initial outpatient evaluations, including oculomotor and vestibular tests using infrared Frenzel glasses, revealed no spontaneous or positional nystagmus. However, with the proposed system, the patient successfully recorded geotropic direction-changing positional nystagmus during a vertigo attack at home. The nystagmus was beating distinctly stronger on the left side down with 2.0 beats/second than the right side down with 1.2 beats/second. Based on the recorded videos, a diagnosis of lateral semicircular canal-type BPPV was made. Treatment with the Gufoni maneuver effectively alleviated the patient’s symptoms, confirming the diagnosis. The affordability and practicality of the device make it particularly suitable for telemedicine and emergency care applications, enabling patients in remote or underserved areas to receive accurate diagnoses. Conclusions: The proposed system demonstrates the feasibility and utility of using affordable, accessible technology for diagnosing vestibular disorders outside of clinical settings. By addressing key challenges, such as the absence of symptoms during clinical visits and the high costs associated with traditional diagnostic tools, this device offers a practical solution for real-time monitoring and accurate diagnosis. Its potential applications extend to telemedicine, emergency settings, and resource-limited environments. Future iterations that incorporate higher-resolution imaging and automated analysis could further enhance its diagnostic capabilities and usability across diverse patient populations.

Background: Subjective 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. Objective: This study aimed to verify the reliability and validity of MVR-SVV by comparing its data with the previously established bucket test (bucket-SVV). Methods: Thirty-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). Results: BAP 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. Conclusions: Our 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.