Purpose: This study evaluated volumetric analysis using automatic whole-brain segmentation as a potential tool to enhance diagnostic accuracy alongside traditional magnetic resonance imaging (MRI) markers in the diagnosis of idiopathic normal pressure hydrocephalus (INPH). Materials and Methods: Twenty-six patients diagnosed with INPH exhibited progressive symptoms, including gait dysfunction and cognitive impairment, confirmed by MRI evidence of enlarged ventricles and normal cerebrospinal fluid pressure. Automatic segmentation was performed on sagittal T1-weighted volumetric images using LesionQuant. Age- and sex-matched groups with Alzheimer’s disease (AD) and normal control (NC) groups were included. Multinomial logistic regression was applied to predict diagnoses (NC, INPH, or AD) based on volumetric parameters. Results: Compared to the AD and NC groups, enlarged inferior lateral ventricles were observed in the INPH group. The inferior lateral ventricle volume showed a positive linear correlation with the Evans’ index (R2 = 0.639) and a negative linear correlation with the callosal angle (R2 = 0.482). High classification accuracy was achieved, with 87.5% of NC cases, 88.5% of INPH cases, and 76% of AD cases correctly identified. Conclusion Automated volumetric markers appear valuable for diagnosing INPH and distinguishing it from other neurodegenerative diseases.

Purpose: This study evaluated volumetric analysis using automatic whole-brain segmentation as a potential tool to enhance diagnostic accuracy alongside traditional magnetic resonance imaging (MRI) markers in the diagnosis of idiopathic normal pressure hydrocephalus (INPH). Materials and Methods: Twenty-six patients diagnosed with INPH exhibited progressive symptoms, including gait dysfunction and cognitive impairment, confirmed by MRI evidence of enlarged ventricles and normal cerebrospinal fluid pressure. Automatic segmentation was performed on sagittal T1-weighted volumetric images using LesionQuant. Age- and sex-matched groups with Alzheimer’s disease (AD) and normal control (NC) groups were included. Multinomial logistic regression was applied to predict diagnoses (NC, INPH, or AD) based on volumetric parameters. Results: Compared to the AD and NC groups, enlarged inferior lateral ventricles were observed in the INPH group. The inferior lateral ventricle volume showed a positive linear correlation with the Evans’ index (R2 = 0.639) and a negative linear correlation with the callosal angle (R2 = 0.482). High classification accuracy was achieved, with 87.5% of NC cases, 88.5% of INPH cases, and 76% of AD cases correctly identified. Conclusion Automated volumetric markers appear valuable for diagnosing INPH and distinguishing it from other neurodegenerative diseases.

Purpose: This study evaluated volumetric analysis using automatic whole-brain segmentation as a potential tool to enhance diagnostic accuracy alongside traditional magnetic resonance imaging (MRI) markers in the diagnosis of idiopathic normal pressure hydrocephalus (INPH). Materials and Methods: Twenty-six patients diagnosed with INPH exhibited progressive symptoms, including gait dysfunction and cognitive impairment, confirmed by MRI evidence of enlarged ventricles and normal cerebrospinal fluid pressure. Automatic segmentation was performed on sagittal T1-weighted volumetric images using LesionQuant. Age- and sex-matched groups with Alzheimer’s disease (AD) and normal control (NC) groups were included. Multinomial logistic regression was applied to predict diagnoses (NC, INPH, or AD) based on volumetric parameters. Results: Compared to the AD and NC groups, enlarged inferior lateral ventricles were observed in the INPH group. The inferior lateral ventricle volume showed a positive linear correlation with the Evans’ index (R2 = 0.639) and a negative linear correlation with the callosal angle (R2 = 0.482). High classification accuracy was achieved, with 87.5% of NC cases, 88.5% of INPH cases, and 76% of AD cases correctly identified. Conclusion Automated volumetric markers appear valuable for diagnosing INPH and distinguishing it from other neurodegenerative diseases.

Purpose: Artificial intelligence has been applied across various fields of medicine, with large language models (LLMs) demonstrating potential to assist in clinical decision-making for rare diseases. This study assessed the performance of LLMs in aiding the phenotyping process and guiding the identification of correct genetic conditions. Materials and Methods: Clinical images from 10 Korean individuals with genetically confirmed rare diseases were collected through a literature review. Using identical prompts, the top 10 Human Phenotype Ontology (HPO) terms and suspected genetic conditions were queried across three LLMs: generative pre-trained transformers (GPTs) models GPT-4o and GPT o1, and Claude 3.5 Sonnet. Concordance between 5 manually curated key HPO terms and the top 10 predicted terms were assessed, and the accuracy of genetic diagnoses among the LLMs was analyzed. Results: Clinical images, ranging from 3 to 9 per case, were used as input for 10 Korean rare disease cases. The average number of key HPO terms correctly matched among the top 10 predictions was 3.0 (1.99-4.01) for GPT-4o, 2.8 (2.06-3.54) for GPT o1, and 1.7 (0.80-2.60) for Claude 3.5 (mean, [95% confidence interval]). GPT models provided more specific HPO terms than Claude in these cases. The accuracy of genetic diagnosis within the top 10 predictions was 2/10 for GPT-4o, 3/10 for GPT o1, and 0/10 for Claude 3.5, with frequent hallucination events observed. Conclusion LLMs demonstrate potential as a supportive tool for image-based rare disease phenotyping, while the frequent hallucinations highlight the need for further investigation and caution in clinical application.