Cataract is a major cause of blindness due to eye diseases. Methods for evaluating the degree of lens opacification in cataracts are divided into subjective and objective methods. The commonly used subjective method is the Lens Opacification Classification System (LOCS Ⅲ), while the objective methods mainly include the Dysfunctional Lens Index (DLI) of the Ray Tracing aberration analysis system, the PNS score of the Pentacam anterior segment analysis system, etc. Subjective diagnosis may lead to certain misjudgments, which have affected clinical diagnosis and treatment. There is an urgent need to add objective diagnostic measures to assist clinical work. The Scanning Source Optical Coherence Tomography (SS - OCT) biometer - IOL Master 700 forms an OCT imaging of the eye based on the swept - source optical coherence tomography (OCT) biometric technology. It can visually show the longitudinal section of the entire lens, and the clear display of the patient's lens tomographic OCT image is obtained through image visualization measurement. The main purpose of this study is to analyze the lens images obtained by the IOLmaster 700. Based on the current mainstream algorithm models such as ResNet - 34 and XGBoost, develop a heterogeneous accelerated artificial intelligence algorithm according to our research needs to accurately calculate the degree of lens opacification. And write image analysis software by ourselves to automatically calculate the required indicators and output them. Establish a heterogeneous accelerated artificial intelligence - assisted lens opacification grading and prediction system, supporting software for biometer equipment, and a cataract lens image database. The software provides online service functions, and all researchers can use the image analysis function of the software after logging in, truly realizing the sharing of large instrument supporting software operations. Thereby improving the accuracy and efficiency of clinical diagnosis and treatment, the prognostic prediction level of patients after cataract surgery, guiding clinical diagnosis and treatment more accurately, and at the same time, it can be used as a tool for community screening.
Study Type
OBSERVATIONAL
Enrollment
2,000
patients who were diagnosed cataract would go through tests with IOL-MASTER 700 to achieve ocular biometry parameters.
Cataract Grade
Achieved cataract images from IOL-MASTER 700 will be graded by several experienced doctors. And each image will be graded in three parts: cortical, nuclear and posterior subcapsular opacification.
Time frame: 3 months
AI predicted cataract grade
Achieved cataract images from IOL-MASTER 700 will be used to train, validate and test the AI algorithm. Predicted grade will be carried out and each image will be graded by AI in three parts: cortical, nuclear and posterior subcapsular opacification.
Time frame: 3 months
AI model performance
To evaluate the model performance, several indices are introduced: Sensitivity, Specificity, Accuracy, Precision, F1 score, receiver operating characteristic curve and area under the curve.
Time frame: 3 months
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