According to the characteristics of the lesion, different treatment methods such as cervical conization, cervical ablation and expectant management can be selected for high-grade cervical intraepithelial neoplasia, especially for nulliparous female patients. The image characteristics of the lesion involved by colposcopy play a decisive role in assessing the severity of the lesion and making decisions about different treatment methods. How can we design a detailed, specific clinical study that addresses empirical questions in treatment decisions?
Using Artificial Intelligence (AI) to analyze colposcopy images for the individualized treatment of High-Grade Squamous Intraepithelial Lesion (HSIL) is a promising approach in precision medicine. By leveraging AI techniques like machine learning (ML) and deep learning (DL), it is possible to improve the accuracy of diagnosis, predict patient outcomes, and assist clinicians in tailoring treatment plans. Below are steps and strategies for using AI in this context:
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