Summary

AI provides useful tools capable of adjusting the individual probability of failure of a surgery allows improving the quality of care by assisting in decision making.

Abstract

Background

Partial meniscectomy is the most popular surgical technique for the treatment of meniscal tears and its results have been widely studied. The surgical outcomes worsen with presence of osteoarthritis changes, meniscal root tears, complex tears and narrowing of the articular space. Failure of the procedure has been described over 30% at 5 years so the preoperative identification of patients with high risk of failure is mandatory.
The medical aplication of big data analysis is gaining popularity, being useful for the desing of predictive models since their algorithms can process big amount of data and identify trends in disease development.

Objetive: The main objective is to establish the foundations of an AI tool that assists orthopaedic surgeons in identifying patients at high risk of failure in meniscal surgery.

Methods

Retrospective multicenter study in wich all patients who underwent partial meniscectomy from January 2018 to February 2022 were included. Inclusion criteria were age over 45 years and first episode of meniscal surgery while the exclusion criteria were traumatic tears and less than 12 months of follow up.
As demographic variables we collected age, sex, BMI, type of job and nationality. Personal history of cardiovascular or oncologic disease, hypertension, diabetes, hyperlipidemia, chronic kidney disease, OSAS, Parkinson disease, fibromyalgia, depression or anxiety disorder were collected. Preoperative phisical examination, radiographic and MRI findings were analyzed. As outcome variables we compiled a satisfaction survey, the Lysholm Knne Scoring Scale and the need of a second meniscal surgery.
A total of 350 patients were identified, only 244 (56% male with a mean age of 54 years) answered the satisfaction survey and were included in the final data set analysis.

Results

Outcome analysis revealed a 73.8% of patients with residual knee pain with a 49% of VAS score over 5. 83.2% of the patients returned to work but 37.6% gave up phisical activity because of knee pain. According to the Lysholm Score 21% of the outcomes were considered excellent and 35.3% poor. Only 38.1% of the patients were totally satisfied with the postoperative results.
Data augmentation technique was applied to increase the variability of the dataset resulting in a better representation of the target population avoiding the overfitting and improving the accuracy of the predictive tool. Synthetic data were obtained with two models, GAN and LSTM, resulting in a total of 5000 synthetic data. Decision tree, random forest, gradient boosted tree (GBT) and multilayer perceptron (MLP) were used as predictive algorithms of Lysholm score results. The most accurate models turned to be the decision tree (82% accuracy and 7.95% error) and random forest (80% accuracy and 7.6% error). The best sensitivity for the detection of patients with high risk of failure (Poor Lysholm score) corresponded to the decision tree (96% vs 91%), while the greatest specificity was achieved in the random forest model algorithm (96% vs 89%). The combination of these parameters results in a F1 score of 92% form the decision tree and 94% for the random forest, being the random forest the best model for predicting the results of mensical surgery using artificial inteligence.

Conclusions

Data-driven approach helps us predict the future by using past and current information. Having tools capable of adjusting the individual probability of failure of a surgery allows improving the quality of care by assisting in decision making.