Objective To establish a deep learning thrombosis risk model for patients undergoing orthopaedic surgery,and to analyze its clinical efficacy by using a decision curve.Methods A total of 180 orthopaedic patients admitted to Linping Campus in School of Medicine,the Second Affiliated Hospital of Zhejiang University from February 2022 to Feb-ruary 2024 were retrospectively selected and divided into training group(n=126)and verification group(n=54)accord-ing to the ratio of 7:3.The patients were subdivided into two groups on the basis of the results of the deep vein thrombo-sis(DVT)examination.A predictive model for DVT risk in orthopaedic surgery patients was developed using Python software.The clinical efficacy of the model was evaluated through the construction of a decision curve.Results The training group,the age,BMI,operation time and bed rest time of the group with DVT were higher than those of the group without DVT.Furthermore,the proportions of diabetes,hypertension,spinal surgery history,lower limb injury and gen-eral anaesthesia were higher than those of the group without DVT(P＜0.05).The independent risk factors for DVT in or-thopaedic surgery patients,as identified through statistical analysis,were age,BMI,diabetes,hypertension,disease dis-tribution(lower limb injury)and bed rest time exceeding five days(P＜0.05).The AUC for the training set and verifi-cation set is 0.887 and 0.903,respectively.When the threshold probabilities of the training set and verification set are 18％-56％and 19％-58％respectively,the implementation of effective intervention measures can facilitate the optimal clinical benefits for patients undergoing orthopaedic surgery.Conclusion The following factors have been identified as independent risk factors for DVT in patients undergoing orthopaedic surgery:age,BMI,diabetes,hypertension,disease distribution(lower limb injury)and bed rest time exceeding five days.The ANN model constructed using these influen-cing factors is an effective method for predicting the risk of DVT in orthopedic surgery patients.This is beneficial in order to ensure that the clinical benefits of DVT prevention and treatment are optimized.
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