A clinical decision model based on machine learning for ptosis

Abstract

Abstract Background To establish a decision model based on two- (2D) and three-dimensional (3D) eye data of patients with ptosis for developing personalized surgery plans. Methods Data of this retrospective, case-control study was collected from March 2019 to June 2019 at the Department of Ophthalmology, Shanghai Ninth People’s Hospital, and then the patients were followed up for 3 months. One hundred fifty-two complete feature eyes from 100 voluntary patients with ptosis and satisfactory surgical results were selected, with 48 eyes excluded due to any severe condition or improper collection and shooting angle. Three experimental schemes were set as follows: use 2D distance alone, use 3D distance alone, and use two distances at the same time. The five most common evaluation indicators used in the binary classification problem to test the decision model were accuracy (ACC), precision, recall, F1-score, and area under the curve (AUC). Results For diagnostic discrimination, recall of “3D”, “2D” and “Both” schemes were 0.875, 0.875 and 0.938 respectively. And precision of the three schemes were 0.8333, 0.7778 and 1.0000 for the surgical procedure classification. Values of “Both” scheme that combined 2D and 3D data were the highest in two classifications. Conclusions In this study, 3D eye data are introduced into clinical practice to construct a decision model for ptosis surgery. Our decision model presents exceptional prediction effect, especially when 2D and 3D data employed jointly.

Keywords

• Ptosis
• Clinical decision making
• Computer-assisted surgery
• Machine learning