Development of a machine learning model to predict low vision aid fitting for visually impaired patients.
Machine learning model predicted distance optical low-vision aid prescriptions with high accuracy.
Development of a machine learning model to predict low vision aid fitting for visually impaired patients.
At present, the fitting of low-vision aids (low-vision aids) for patients globally necessitates the intervention of highly skilled ophthalmologists and certified rehabilitation specialists.
Clinical characteristics and diagnostic data from patients with low vision in southeastern China were collected between October 26, 2015, and October 6, 2021, to establish the training and test datasets.
how accurately the artificial intelligence correctly identified who needed this aid type, higher is better
The dataset comprised a total of 1,241 patients diagnosed with low vision.
Our model displayed satisfactory performance in low-vision aids fitting when evaluated on the test set.
In external validation involving 112 prospective cases, the model demonstrated performance comparable to that of a mid-career ophthalmologist (5 years' experience).
This study identified significant associations between clinical characteristics and low-vision aids prescription patterns.
Leveraging historical low-vision aids fitting data, we developed a machine learning-based decision support system capable of predicting optimal fittings for the three fundamental low-vision aids categories.
The proposed tool demonstrates potential for clinical application by generating data-driven prescription recommendations.