Method for predicting difficulty and complications of multi-mode kidney tumor kidney protection operation

Abstract

The invention provides a method for predicting difficulty and complications of a multi-mode kidney tumor kidney protection operation, and belongs to the technical field of medicine. Three imageology comprehensive scoring capabilities of an R.E.N.A.L scoring system, an MAP scoring system and a CSA scoring system are obtained through computer artificial intelligence deep learning; and weighted calculation is performed on basic disease information of the patient, whether anticoagulant drugs, antibiotics and hormones are used or not, preoperative heart, lung and kidney function data and imaging comprehensive scoring ability through computer artificial intelligence deep learning; then, an imaging scoring part of an existing scheme is reserved and fused, more and more comprehensive imaging features are considered compared with all different scoring systems of the existing scheme, the defects of labor consumption, time consumption, instability and the like of manual calculation are overcome, and the result is more accurate and stable.

Description

technical field [0001] The invention relates to the field of medical technology, in particular to a method for predicting the difficulty and complications of multimodal renal tumor preservation surgery. Background technique [0002] For the prediction of the complexity and perioperative complications of renal-sparing surgery for renal tumors, there are 14 different scoring systems for renal-sparing surgery for renal tumors that only rely on imaging feature data; the existing scoring systems are limited by the times and technology, and rely heavily on Manual scoring, the use of scoring systems with many parameters is complicated, laborious, time-consuming, and does not meet the needs of clinical work. There may be errors in the value, resulting in the instability of the final score, and it does not conform to the stable and reliable characteristics of clinical work. The patient's multimodal data includes: hypertension, diabetes, coronary heart disease, malignant tumor histor...

AI Technical Summary

Problems solved by technology

[0003] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide a method for predicting the complexity and perioperative complications of multi-modal renal tumor conserving surgery, which retains and integrates the existing schemes that rely on imaging scores, which is better than the existing ones. All the different scoring systems of the scheme consider more imaging features, are more accurate and stable, and overcome the laborious, time-consuming, unstable and other shortcomings of manual calculations; at the same time, considering the multimodal clinical data of patients, through artificial intelligence deep learning The method overcomes the characteristic that manual calculation cannot carry out weighted scoring of multimodal clinical data, making the results more accurate and stable

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