ABSTRACT
BACKGROUND: Migraine is characterized by recurrent episodes of unilateral, pulsating headaches. At the cerebral and ocular levels, it is recognized that the vascular narrowing and loss of blood flow are transient; however, the chronic nature of migraine may result in long-term functional and structural changes in these structures. It could result in axonal loss and an alteration in the thickness of the retinal nerve fiber layers (RNFL). This study aimed to measure the RNFL thickness, which provides a useful indication of the state of the axons and the loss of ganglion cells in migraine patients, and to find out if RNFL thickness and the clinical features of migraine are correlated. MATERIALS AND METHODS: Sixty patients with migraine and 60 age-gender-matched controls were recruited. A complete neurological and ophthalmological examination was performed, and spectral-domain optical coherence tomography (SD-OCT) was done to measure RNFL. RESULTS: All quadrants of the retina on both sides showed non-statistically significant differences in RNFL thickness between migraine patients and controls (p-value >0.05). Furthermore, in all retinal quadrants on both sides, there was no statistically significant difference in RNFL thickness between migraine patients with aura and those without aura (p-value >0.05). Significant correlations were found between the duration of migraine disease and the superior RNFL thickness of both eyes, as well as the inferior RNFL in the right eye. There was also a significant correlation between the headache attack duration and RNFL thickness of the superior retina (p<0.05), Conclusion: Our key finding was that when comparing migraine patients to controls, RNFL thickness did not significantly change; however, the duration of migraine disease did significantly affect RNFL thickness.
ABSTRACT
OBJECTIVE: We aim to compare the capabilities of ChatGPT 3.5, Microsoft Bing, and Google Gemini in handling neuro-ophthalmological case scenarios. METHODS: Ten randomly chosen neuro-ophthalmological cases from a publicly accessible database were used to test the accuracy and suitability of all three models, and the case details were followed by the following query: "What is the most probable diagnosis?" RESULTS: On the basis of the accuracy of diagnosis, all three chat boxes (ChatGPT 3.5, Microsoft Bing, and Google Gemini) gave the correct diagnosis in four (40%) out of 10 cases, whereas in terms of suitability, ChatGPT 3.5, Microsoft Bing, and Google Gemini gave six (60%), five (50%), and five (50%) out of 10 case scenarios, respectively. CONCLUSION: ChatGPT 3.5 performs better than the other two when it comes to handling neuro-ophthalmological case difficulties. These results highlight the potential benefits of developing artificial intelligence (AI) models for improving medical education and ocular diagnostics.
