Current eye research · 2022
A Novel Method to Improve Inter-Clinician Variation in the Diagnosis of Retinopathy of Prematurity Using Machine Learning.
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Nisha K L, Ganapathy Sreelekha, Puthumangalathu Savithri Sathidevi, Idaguri Madhu, Mohanachandran Poornima, Vinekar Anand, Chandra Parijat, Kulkarni Sucheta, Dogra Mangat
Аннотация
PURPOSE: Inter-clinician variation could cause uncertainty in disease management. This is reported to be high in Retinopathy of Prematurity (ROP), a potentially blinding retinal disease affecting premature infants. Machine learning has the potential to quantify the differences in decision-making between ROP specialists and trainees and may improve the accuracy of diagnosis.
METHODS: An anonymized survey of ROP images was administered to the expert(s) and the trainee(s) using a study-designed user interface. The results were analyzed for repeatability as well as to identify the level of agreement in the classification. "Ground truth" was prepared for each individual and a unique classifier was built for each individual using the same. The classifier allowed the identification of the most important features used by each individual.
RESULTS: Correlation and disagreement between the expert and the trainees were visualized using the Dipstick™ diagram. Intra-clinician repeatability and reclassification statistics were assessed for all. The repeatability was 88.4% and 86.2% for two trainees and 92.1% for the expert, respectively. Commonly used features differed for the expert and the trainees and accounted for the variability.
CONCLUSION: This novel, automated algorithm quantifies the differences using machine learning techniques. This will help audit the training process by objectively measuring differences between experts and trainees.
TRANSLATIONAL RELEVANCE: Training for image-based ROP diagnosis can be more objectively performed using this novel, machine learning-based automated image analyzer and classifier.
METHODS: An anonymized survey of ROP images was administered to the expert(s) and the trainee(s) using a study-designed user interface. The results were analyzed for repeatability as well as to identify the level of agreement in the classification. "Ground truth" was prepared for each individual and a unique classifier was built for each individual using the same. The classifier allowed the identification of the most important features used by each individual.
RESULTS: Correlation and disagreement between the expert and the trainees were visualized using the Dipstick™ diagram. Intra-clinician repeatability and reclassification statistics were assessed for all. The repeatability was 88.4% and 86.2% for two trainees and 92.1% for the expert, respectively. Commonly used features differed for the expert and the trainees and accounted for the variability.
CONCLUSION: This novel, automated algorithm quantifies the differences using machine learning techniques. This will help audit the training process by objectively measuring differences between experts and trainees.
TRANSLATIONAL RELEVANCE: Training for image-based ROP diagnosis can be more objectively performed using this novel, machine learning-based automated image analyzer and classifier.