Artificial Intelligence Diagnosis of Ocular Motility Disorders From Clinical Videos
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Notice bibliographique
Résumé
BACKGROUND: Multimodal artificial intelligence (AI) models have recently expanded into video analysis. In ophthalmology, one exploratory application is the automated detection of extraocular movement (EOM) disorders. This proof-of-concept study evaluated the feasibility of using Gemini 2.0 to recognize EOM abnormalities, identify the affected eye, and recognize specific movement limitations from publicly available, real-world clinical videos. METHODS: We retrospectively collected 114 YouTube videos of EOM disorders, including cranial nerve (CN) palsies, internuclear ophthalmoplegia (INO), supranuclear disorders, nystagmus, and ocular myasthenia gravis (MG), alongside 15 control videos demonstrating normal EOMs. Videos were trimmed to include only the pertinent clinical examinations, and audio was removed to avoid diagnostic cues. Using a standardized zero-shot prompt, Gemini 2.0 analyzed each video via the Google AI Studio platform. Gemini 2.0 was evaluated based on its ability to provide the correct diagnosis, identify the affected eye, and recognize the specific movement limitation (if any). Descriptive statistics, Spearman correlations, and comparative analyses were used to assess performance. RESULTS: Gemini 2.0 correctly identified the primary diagnosis in 43 of 114 videos, yielding an overall diagnostic accuracy of 37.7%. Diagnostic performance varied by condition, with the highest accuracies observed in third nerve palsy (81.1%), INO (80.0%), sixth nerve palsy (66.7%), and ocular MG (20.0%), whereas normal EOMs were correctly classified in 93.3% of cases. In misclassified cases, the correct diagnosis appeared in the differential diagnosis in 15.5% of instances. Laterality was correctly identified in 26.5% of eligible cases overall, 73.1% among correctly diagnosed cases vs. 9.6% in misclassified ones. Similarly, movement limitations were accurately identified in 30.3% of eligible cases overall, with a marked increase to 88.5% accuracy in correctly diagnosed cases compared to 9.6% in misclassified cases. Longer videos moderately correlated with longer processing time (ρ = 0.55, P < 0.001). Significant correlations were observed between correct diagnosis and correct laterality identification (ρ = 0.45, P < 0.001), correct diagnosis and correct movement limitation identification (ρ = 0.61, P < 0.001), and laterality and movement limitation (ρ = 0.51, P < 0.001). Processing time averaged 11.0 seconds and correlated with video length (ρ = 0.55, P < 0.001). CONCLUSIONS: This proof-of-concept study demonstrates the feasibility of using Gemini 2.0 for automated recognition of EOM abnormalities in clinical videos. Although performance was stronger in overt cases, overall diagnostic accuracy remains limited. Substantial validation on standardized, clinician-annotated datasets is needed before clinical application.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle