野田 昌生

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.

When pharmacological treatments are inadequate, facial nerve paralysis from various etiologies, including Bell's palsy, Hunt syndrome, and trauma, often requires surgical intervention. Facial nerve decompression surgery aims to relieve nerve compression and restore function, with preserving hearing function, especially in pediatric cases, being crucial. Conventional methods, like the transmastoid approach, risk affecting auditory function due to ossicle manipulation. Herein, we describe the case of a 12-year-old boy with left facial palsy diagnosed with zoster sine herpete (ZSH) syndrome. Despite medical treatment, the patient's condition did not improve, prompting facial nerve decompression surgery. Employing the intact transmastoid ossicle (ITO) swaying technique, we minimized ossicular manipulation, preserving auditory function while effectively achieving facial nerve decompression. The patient demonstrated improvement postoperatively in auditory and facial nerve functions. Furthermore, audiometric assessments demonstrated no substantial deterioration in hearing thresholds, and the facial nerve function improved from Grade V to Grade II on the House-Brackmann scale. The ITO technique provides a less invasive alternative compared to conventional approaches, lowering the chance of the ossicular chain and the risk of postoperative hearing loss. This case highlights the significance of customized surgical approaches in pediatric facial nerve decompression surgery, resulting in improved patient outcomes. Further research is required to validate the efficacy and safety of this method across various clinical contexts.

Background Artificial intelligence models can learn from medical literature and clinical cases and generate answers that rival human experts. However, challenges remain in the analysis of complex data containing images and diagrams. Objective This study aims to assess the answering capabilities and accuracy of ChatGPT-4 Vision (GPT-4V) for a set of 100 questions, including image-based questions, from the 2023 otolaryngology board certification examination. Methods Answers to 100 questions from the 2023 otolaryngology board certification examination, including image-based questions, were generated using GPT-4V. The accuracy rate was evaluated using different prompts, and the presence of images, clinical area of the questions, and variations in the answer content were examined. Results The accuracy rate for text-only input was, on average, 24.7% but improved to 47.3% with the addition of English translation and prompts (P<.001). The average nonresponse rate for text-only input was 46.3%; this decreased to 2.7% with the addition of English translation and prompts (P<.001). The accuracy rate was lower for image-based questions than for text-only questions across all types of input, with a relatively high nonresponse rate. General questions and questions from the fields of head and neck allergies and nasal allergies had relatively high accuracy rates, which increased with the addition of translation and prompts. In terms of content, questions related to anatomy had the highest accuracy rate. For all content types, the addition of translation and prompts increased the accuracy rate. As for the performance based on image-based questions, the average of correct answer rate with text-only input was 30.4%, and that with text-plus-image input was 41.3% (P=.02). Conclusions Examination of artificial intelligence’s answering capabilities for the otolaryngology board certification examination improves our understanding of its potential and limitations in this field. Although the improvement was noted with the addition of translation and prompts, the accuracy rate for image-based questions was lower than that for text-based questions, suggesting room for improvement in GPT-4V at this stage. Furthermore, text-plus-image input answers a higher rate in image-based questions. Our findings imply the usefulness and potential of GPT-4V in medicine; however, future consideration of safe use methods is needed.

Facial nerve injuries stem from trauma or tumor surgery, triggering neurodegeneration and neuronal cell death in the facial nucleus, consequently inducing irreversible nerve paralysis. Following facial nerve transection, glial cells are activated and undergo proliferation, facilitating motor neuron survival, repair, and regeneration. Clinical approaches, including nerve anastomosis and hypoglossal nerve grafting, require delicate microscopic techniques. Recent advancements involve nerve reconstruction using polyglycolic acid (PGA) tubes, which yield nerve function improvement. However, the central pathophysiological effects of these procedures remain unclear. Therefore, using PGA tubes, we evaluated neurodegeneration and microglial inflammatory response in rats after facial nerve transection. Facial nerve functions were evaluated using vibrissae and blink reflex scores. In the end-to-end anastomosis and PGA tube reconstruction groups, a partial improvement in facial motor function was observed, with increased nerve fiber survival in the former. Approximately 90% of neurons survived in both groups, wherein gliosis exhibited increased microglial activation compared to that in the transection group. These results indicate that PGA tube-assisted nerve reconstruction post-facial nerve transection, although inferior to end-to-end anastomosis, improved certain functions and prevented neuronal cell death. Furthermore, the prolonged inflammatory response in the facial nerve nucleus underscored the correlation between neuronal function and survival and microglia.