In the study, five algorithms were trained to analyze preoperative information to screen patients for the surgery, with the models combined into an ensemble classifier. When tested on the data of several thousand patients, the classifier achieved 93.4 percent accuracy in identifying suitable candidates for the operation, a level equal to that of expert ophthalmologists and significantly more successful than other existing screening methods.
Therefore, according to the study’s authors, the machine learning method serves as an effective clinical decision support tool to screen for the surgery and could ultimately lead to a reduction in postoperative complications caused by misdiagnosis.
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