Translating nanotechnology and explainable AI into public health policy for spinal fusion: Population-level risk management

Lumbar fusion surgery and its public health are intervention with significant variability in the outcomes and overall risks and benefits for the patients, therefore requiring state-of-the-art risk assessment models. In this paper, an Explainable Artificial Intelligence (XAI) approach is developed for the application in spinal fusion surgeries and public health with the Machin learning (ML) methods combined with nanotechnology. Consequently, the proposed framework captures patient's demographic, public health index, clinical, and biomechanical characteristics for prediction of surgical outcomes, pos-surgical complications, and recovery curves. Feature importance analysis and SHapley Additive exPlanations (SHAP) are used for decision justification to let clinicians understand which factors contribute to the high-risk score. The proposed XAI model is tested on clinical datasets from previous years and year ended to be more accurate compared to traditional statistical models yet enough compact for interpretation. The results reveal the application of explainable AI combined with nanotechnology in the enhancement of treatment plans and public health for various patients, the minimization of possible surgery complications, as well as, the introduction of improved chances of success. Results show that older patients with osteoporosis face higher physiological strain because it results in delayed postoperative healing along with greater surgical complications because of their decreased hemoglobin levels and insufficient bone repair. The duration of surgeries increases when patients have low hematocrit levels and lower vitamin D concentrations which calls for unique preoperative optimization methods to enhance surgical results.