The application of lightweight AI algorithms in postoperative rehabilitation of breast cancer.

The prevalence of breast cancer as a major global cancer has underscored the importance of postoperative recovery for breast cancer patients. Among the issues, postoperative patients are prone to spinal deformities, including scoliosis, which has drawn significant attention from healthcare professionals. The primary aim of this study is to design a postoperative recovery platform for breast cancer patients that can effectively detect posture changes, provide feedback and support to medical staff, assist doctors in formulating recovery plans, and prevent spinal deformities. The feasibility of the recovery platform is also validated through experiments. The development and validation of the experimental recovery platform. The recovery platform includes instrument design, patient data collection, model training and fine-tuning, and postoperative body posture evaluation by comparing preoperative and postoperative conditions. The evaluation results are provided to doctors to facilitate the formulation of personalized postoperative recovery plans. This paper comprehensively designs and implements the recovery platform and verifies its feasibility through simulation experiments. Statistical methods were employed for the validation of the rehabilitation platform in simulated experiments, with a significance level of p < 0.05. In comparison to static assessments like CT scans, this paper introduces a dynamic detection method that provides a more insightful analysis of body posture. The experiments also demonstrate the preventive capability of this method against post-operative spinal deformities, ultimately enhancing patients' self-image, restoring their confidence, and enabling them to lead more fulfilling lives.