Lu, Yining

Abstract As artificial intelligence (AI) Large Language Models (LLM) like ChatGPT become more common in healthcare, patients increasingly use them to find medical information. While ChatGPT may support health literacy, its readability and accuracy compared to established resources remain unclear. The purpose of this study is to evaluate ChatGPT-4 responses on femoroacetabular impingement (FAI) and its surgical management compared to AAOS OrthoInfo content. We hypothesize that ChatGPT may deliver medically accurate information, but its readability may fall short when compared to OrthoInfo. Nine questions based on the OrthoInfo FAI page were submitted to ChatGPT-4, with and without a readability prompt. Topics included anatomy, pathology, cause, symptoms, workup, imaging, treatment, role of hip arthroscopy, and outcomes. Readability was assessed using validated indices. Accuracy was independently rated using a 4-point scale. Statistical comparisons were made using t-tests and ANOVA (P < 0.01 threshold). OrthoInfo content had a mean reading grade level of 8.0 and a Flesch Reading Ease score of 60.7. Unprompted ChatGPT responses were significantly less readable (grade level 16.2; Flesch score 21.7; P < 0.001). Prompting for readability improved ChatGPT outputs (grade level 10.6; Flesch score 58.2), making them comparable to OrthoInfo (P < 0.09). Accuracy was high across all sources, though OrthoInfo's response on FAI causes scored slightly lower (3.5 versus 4.0; P < 0.46). ChatGPT provides accurate information on FAI. However, without prompting, its complexity may hinder patient understanding. Prompting for readability enables ChatGPT to match established resources like OrthoInfo. Tailored prompting is key to using LLM effectively in patient education and promoting health literacy.

Background: Femoroacetabular impingement (FAI) is a common cause of hip pain in young, active athletes. American football players place high demands on the hip due to the sport's requirements for pivoting, cutting, and loading in deep flexion. Although arthroscopic treatment of FAI in professional National Football League (NFL) players has shown favorable outcomes, football-specific outcomes in non-NFL athletes remain limited. Purpose: To report the return-to-sport (RTS) rates and functional outcomes at a minimum 2-year follow-up for active American football players competing at multiple levels after primary hip arthroscopy for FAI. Study Design: Case series; Level of evidence, 4. Methods: An institutional database was queried to identify all active American football players who underwent primary hip arthroscopy with labral repair between 2010 and 2023. Demographic characteristics, radiographic parameters, procedure details, complications, and reoperations were collected. Follow-up was completed at a minimum of 2 years to obtain updated patient-reported outcome measures (PROMs) as well as football-specific data, including current participation and RTS status. PROMs were reported as mean ± standard deviation, and categorical variables, including RTS, were reported as frequencies. Pre- versus postoperative PROMs were compared using t tests or Mann-Whitney U tests to assess outcomes. Results: In total, 49 hips in 38 male football players (age, 18.1 years; range, 14-26 years) were included. Most athletes played at the high school (74%) or college level (21%). A total of 51% played as linemen. Labral repair was performed in all cases. At a mean 7.6 years, mean modified Harris Hip Score was 90 ± 13, Hip Outcome Score–Activities of Daily Living was 95 ± 7, and Hip Outcome Score–Sport was 89 ± 17. Overall mean surgery satisfaction was 8.7 ± 1.7 (out of 10). Significant postoperative improvements were observed in all PROMs ( P ≤ .002). Twelve players (32%) did not attempt RTS due to other nonmedical factors and were excluded from return to sport analysis. A total of 23 (88%) players achieved RTS, and 3 players (12%) did not. Two players (8%) cited other injuries, and 1 player (4%) continued to have hip limitations. Four hips (8%) underwent reoperation at a mean 4.9 years postoperative. Conclusion: Amateur American football players undergoing primary hip arthroscopy for FAI demonstrated excellent patient-reported outcomes and a high RTS rate of 88%, when excluding those who did not return for nonmedical reasons, at a mean 7.6-year follow-up. Notable positional differences were observed across multiple levels of play. Although outcomes were generally favorable, both medical and nonmedical factors could limit return to sport, particularly among high school and college athletes.

Background: Treatment decisions in patients with anterior cruciate ligament (ACL) injuries are influenced by multiple factors, such as the desire to return to sports or symptomatic instability. Identifying the differential treatment effect of ACL reconstruction (ACLR) compared with nonoperative management on a patient-specific level can inform surgical decision-making. Hypothesis: Unsupervised machine learning can identify distinct patient subgroups based on outcome achievement after ACL injury, and ACLR will exert a protective effect on the development of posttraumatic osteoarthritis (PTOA) over nonoperative management. Study Design: Cohort study; Level of evidence, 3. Methods: A longitudinal populational registry identified patients with ACL injuries from 1990 to 2016 with a minimum 7.5-year follow-up. An unsupervised random forest algorithm was utilized to develop and validate patient subgroups. Treatment effects of ACLR on outcomes were analyzed using a machine learning causal inference estimator. Patient subgroup membership was incorporated into a multivariable logistic regression to identify factors predictive of optimal outcomes. Results: A total of 923 patients (785 primary ACLR, 138 nonoperative) were included. The random forest algorithm arrived at an optimal partition of 2 subgroups, with 653 patients in the optimal outcome subgroup (368 male [56.4%]; mean age, 26.0 ± 10.2 years; mean body mass index [BMI], 26.5 ± 4.30) and 270 patients in the suboptimal outcome subgroup (152 male [56.3%]; mean age, 35.0 ± 10.1 years; mean BMI, 30.5 ± 5.54). The latter group demonstrated significantly increased rates of secondary meniscal injury, development of symptomatic PTOA, and progression to total knee arthroplasty (TKA) at the final follow-up (all P < .01). In the optimal outcome subgroup, ACLR had significantly protective treatment effects on the risk of secondary meniscal injury (average treatment effect [ATE], 61%), contralateral ACL injury (ATE, 8%), symptomatic PTOA (ATE, 16%), and progression to TKA (ATE, 6%) (all P < .01). Conversely, in the suboptimal outcome subgroup, ACLR only protected against symptomatic PTOA (ATE, 11%) and progression to TKA (ATE, 8%) (both P < .01). Conclusion: Two clinically meaningful subgroups were identified from retrospectively collected data and found to experience differential treatment responses after ACL injuries. ACLR decreased the rate of development of PTOA and TKA in both subgroups but was not as effective in preventing secondary meniscal injuries or contralateral ACL injuries in patients who were older, heavier, or had concomitant medial meniscus injuries.

Background: Nonoperative and operative management techniques after anterior cruciate ligament (ACL) injury are both appropriate treatment options for selected patients. However, the subsequent development of posttraumatic knee osteoarthritis (PTOA) remains an area of active study. Purpose: To compare the risk of PTOA between patients treated without surgery and with ACL reconstruction (ACLR) after primary ACL disruption using a machine learning causal inference model. Study Design: Cohort study; Level of evidence, 3. Methods: A geographic database identified patients undergoing ACLR between 1990 and 2016 with minimum 7.5-year follow-up. Variables collected include age, sex, body mass index, activity level, occupation, relevant comorbid diagnoses, radiographic findings, injury characteristics, and clinical course. Treatment effects of ACLR on the development of PTOA and progression to total knee arthroplasty (TKA) were analyzed with machine learning models (MLMs) in a causal inference estimator (targeted maximum likelihood estimation, TMLE), while controlling for confounders. Results: The study included 1194 patients with a minimum follow-up of 7.5 years, among whom 974 underwent primary reconstruction and 220 underwent nonoperative treatment. A total of 215 (22%) patients developed symptomatic PTOA in the ACLR group compared with 140 (64%) in the nonoperative treatment group ( P < .001), whereas 25 (3%) patients underwent TKA in the ACLR group compared with 50 (23%) in the nonoperative treatment group ( P < .001). Patients in the ACLR group had delayed TKA compared with patients in the nonoperative treatment group (193.4 vs 166.0 months, respectively; P = .02). TMLE evaluation revealed that reconstruction decreased the risk of PTOA by 11% (95% CI, 8%-13%; P < .001) compared with nonoperative treatment but did not demonstrate a significant effect on the rate of progression to TKA. Survival analysis with random forest algorithm demonstrated significant delay to the onset of PTOA as well as time to progression of TKA in patients undergoing ACLR. Additional risk factors for the development of PTOA, irrespective of treatment, included older age at injury, greater body mass index, total number of arthroscopic knee surgeries, and residual laxity at follow-up. Conclusion: MLMs in a causal inference estimator found ACLR to exert a significant treatment effect in reducing the rate of development of PTOA by 11% compared with nonoperative treatment. ACLR also delayed the onset of PTOA and progression to TKA.

Abstract Purpose Developing large‐scale, standardized radiographic registries for anterior cruciate ligament (ACL) injuries with artificial intelligence (AI) tools can enhance personalized orthopaedics. We propose deploying Artificial Intelligence for Knee Imaging Registration and Analysis (AKIRA), a trio of deep learning (DL) algorithms, to automatically classify and annotate radiographs. We hypothesize that algorithms can efficiently organize radiographs based on laterality, projection, identify implants and classify osteoarthritis (OA) grade. Methods A collection of 20,836 knee radiographs from all time points of treatment (mean orthopaedic follow‐up 70.7 months; interquartile range [IQR]: 6.8–172 months) were aggregated from 1628 ACL‐injured patients (median age 26 years [IQR: 19–42], 57% male). Three DL algorithms (EfficientNet, YOLO [You Only Look Once] and Residual Network) were employed. Radiograph laterality and projection (anterior‐posterior [AP], lateral, sunrise, posterior‐anterior, hip–knee–ankle and Camp‐Coventry intercondylar [notch]) were labelled by a DL model. Manually provided labels of metal fixation implants were used to develop a DL object detection algorithm. The degree of OA, both as measured by specific Kellgren–Lawrence (KL) grades, as well as based on a binarized label of OA (defined as KL Grade ≥2), on standing AP radiographs were classified using a DL algorithm. Individual model performances were evaluated on a subset of images prior to the deployment of AKIRA to registry construction using all ACL radiographs. Results The classification algorithms showed excellent performance in classifying radiographic laterality (F1 score: 0.962–0.975) and projection (F1 score: 0.941–1.0). The object detection algorithm achieved high precision–recall (area under the precision‐recall curve: 0.695–0.992) for identifying various metal fixations. The KL classifier reached concordances of 0.39–0.40, improving to 0.81–0.82 for binary OA labels. Sequential deployment of AKIRA following internal validation processed and labelled all 20,836 images with the appropriate views, implants, and the presence of OA within 88 min. Conclusion AKIRA effectively automated the classification and object detection in a large radiograph cohort of ACL injuries, creating an AI‐enabled radiographic registry with comprehensive details on laterality, projection, implants and OA. Study Design Cross‐sectional study. Level of Evidence Level IV.

Background: Arthroscopic diagnosis and treatment of femoroacetabular pathology has experienced significant growth in the last 30 years; nevertheless, reduced utilization of orthopaedic procedures has been observed among the underrepresented population. Purpose/Hypothesis: The purpose of this study was to examine racial differences in case incidence rates, outcomes, and complications in patients undergoing hip arthroscopy. It was hypothesized that racial and ethnic minority patients would undergo hip arthroscopy at a decreased rate compared with their White counterparts but that there would be no differences in clinical outcomes. Study Design: Cross-sectional study. Methods: The State Ambulatory Surgery and Services Database and the State Emergency Department Database of New York were queried for patients undergoing hip arthroscopy between 2011 and 2017. Patients were stratified into White and racial and ethnic minority races, and intergroup comparisons were performed for utilization over time, total charges billed per encounter, 90-day emergency department (ED) visits, and revision hip arthroscopy. Temporal trends in the utilization of hip arthroscopy were identified, and racial differences in secondary outcomes were analyzed with a semiparametric method known as targeted maximum likelihood estimation (TMLE) backed by a library of machine learning algorithms. Results: A total of 9745 patients underwent hip arthroscopy during the study period, with 1081 patients of minority race (11.1%). White patients underwent hip arthroscopy at 5.68 (95% CI, 4.98-6.48) times the incidence rate of racial and ethnic minority patients; these incidence rates grew annually at a ratio of 1.11 in White patients compared with 1.03 in racial and ethnic minority patients ( P < .001). Based on the TMLE, racial and ethnic minority patients were significantly more likely to incur higher costs ( P < .001) and visit the ED within 90 days ( P = .049) but had negligible differences in reoperation rates at a 2-year follow-up ( P = .53). Subgroup analysis identified that higher likelihood for 90-day ED admissions among racial and ethnic minority patients compared with White patients was associated with Medicare insurance ( P = .002), median income in the lowest quartile ( P = .012), and residence in low-income neighborhoods ( P = .006). Conclusion: Irrespective of insurance status, racial and ethnic minority patients undergo hip arthroscopy at a lower incidence and incur higher costs per surgical encounter.

Purpose To use unsupervised machine learning clustering to define the “optimal observed outcome” after surgery for anterior shoulder instability (ASI) and to identify predictors for achieving it. Methods Medical records, images, and operative reports were reviewed for patients <40 years old undergoing surgery for ASI. Four unsupervised machine learning clustering algorithms partitioned subjects into “optimal observed outcome” or “suboptimal outcome” based on combinations of actually observed outcomes. Demographic, clinical, and treatment variables were compared between groups using descriptive statistics and Kaplan‐Meier survival curves. Variables were assessed for prognostic value through multivariate stepwise logistic regression. Results Two hundred patients with a mean follow‐up of 11 years were included. Of these, 146 (64%) obtained the “optimal observed outcome,” characterized by decreased: postoperative pain (23% vs 52%; P < 0.001), recurrent instability (12% vs 41%; P < 0.001), revision surgery (10% vs 24%; P = 0.015), osteoarthritis (OA) (5% vs 19%; P = 0.005), and restricted motion (161° vs 168°; P = 0.001). Forty‐one percent of patients had a “perfect outcome,” defined as ideal performance across all outcomes. Time from initial instability to presentation (odds ratio [OR] = 0.96; 95% confidence interval [CI], 0.92‐0.98; P = 0.006) and habitual/voluntary instability (OR = 0.17; 95% CI, 0.04‐0.77; P = 0.020) were negative predictors of achieving the “optimal observed outcome.” A predilection toward subluxations rather than dislocations before surgery (OR = 1.30; 95% CI, 1.02‐1.65; P = 0.030) was a positive predictor. Type of surgery performed was not a significant predictor. Conclusions After surgery for ASI, 64% of patients achieved the “optimal observed outcome” defined as minimal postoperative pain, no recurrent instability or OA, low revision surgery rates, and increased range of motion, of whom only 41% achieved a “perfect outcome.” Positive predictors were shorter time to presentation and predilection toward preoperative subluxations over dislocations. Level of Evidence Level IV, retrospective cohort.

Background: There is a significant long-term risk of posttraumatic osteoarthritis (PTOA) after anterior cruciate ligament reconstruction (ACLR). Elucidating the risk factors and successfully identifying at-risk patients is challenging. Purpose/Hypothesis: The purpose of this study was to produce machine learning survival models that can identify (1) patients at risk of symptomatic PTOA and (2) patients who are at risk of undergoing total knee arthroplasty (TKA) after ACLR. It was hypothesized that these models would outperform traditional Kaplan-Meier estimators. Study Design: Case-control study; Level of evidence, 3. Methods: A geographic database was used to identify patients undergoing ACLR between 1990 and 2016 with a minimum 7.5-year follow-up. Models were used to analyze various factors to predict the rate and time to (1) symptomatic osteoarthritis and (2) TKA using random survival forest (RSF) algorithms. Performance was measured using out-of-bag (OOB) c-statistic, calibration, and Brier score. The predictive performances of the RSF models were compared with Kaplan-Meier estimators. Model interpretability was enhanced utilizing global variable importance and partial dependence curves. Results: A total of 974 patients with ACLR and a minimum follow-up of 7.5 years were included; among these, 215 (22.1%) developed symptomatic osteoarthritis, and 25 (2.6%) progressed to TKA. The RSF algorithms achieved acceptable good to excellent predictive performance for symptomatic arthritis (OOB c-statistic, 0.75; Brier score, 0.128) and progression to TKA (OOB c-statistic, 0.89; Brier score, 0.026), respectively. Significant predictors of symptomatic PTOA included increased pain scores, older age, increased body mass index, increased time to ACLR, total number of arthroscopic surgeries before the diagnosis of arthritis, positive pivot-shift test after reconstruction, concomitant chondral injury, secondary meniscal tear, early (<250 days) or delayed (>500 days) return to sports or activity, and use of allograft. Significant predictors for TKA included older age, increased pain scores, total number of arthroscopic surgeries, high-demand activity/occupation, hypermobility, higher body mass index, systemic inflammatory disease, increased time to surgery, early (<250 days) or delayed (>500 days) return to sports or activity, and midsubstance tears. The Brier score over time revealed that RSF models outperformed traditional Kaplan-Meier estimators. Conclusion: Machine learning survival models were used to reliably identify patients at risk of developing symptomatic PTOA, and these models consistently outperformed traditional Kaplan-Meier estimators. Strong predictors for the development of PTOA after ACLR included increased pain scores at injury and postoperative visit, older age at injury, total number of arthroscopic procedures, positive postoperative pivot-shift test, and secondary meniscal tear.

Background: Surgical and nonoperative management of anterior cruciate ligament (ACL) injuries seek to mitigate the risk of knee instability and secondary meniscal injury. However, the associated risk and timing of secondary meniscal tears have not been completely elucidated. Purpose: To compare risk and timing of secondary meniscal injury between patients receiving nonoperative management, delayed ACL reconstruction (ACLR), and early ACLR using a machine learning survival analysis. Study Design: Cohort study; Level of evidence, 3. Methods: A geographic database was used to identify and review records of patients with a diagnosis of ACL rupture between 1990 and 2016 with minimum 2-year follow-up. Patients undergoing ACLR were matched 1:1 with nonoperatively treated controls. Rate and time to secondary meniscal tear were compared using random survival forest algorithms; independent models were developed and internally validated for predicting injury-free duration in both cohorts. Performance was measured using out-of-bag c-statistic, calibration, and Brier score. Model interpretability was enhanced using global variable importance and partial dependence curves. Results: The study included 1369 patients who underwent ACLR and 294 patients who had nonoperative treatment. After matching, no significant differences in rates of secondary meniscal tear were found ( P = .09); subgroup analysis revealed the shortest periods of meniscal survival in patients undergoing delayed ACLR. The random survival forest algorithm achieved excellent predictive performance for the ACLR cohort, with an out-of-bag c-statistic of 0.80 and a Brier score of 0.11. Significant variables for risk of meniscal tear for the ACLR cohort included time to return to sports or activity ≤350 days, time to surgery ≥50 days, age at injury ≤40 years, and high-impact or rotational landing sports, whereas those in the nonoperative cohort model included time to RTS ≤200 days, visual analog scale pain score >3 at consultation, hypermobility, and noncontact sports. Conclusion: Delayed ACLR demonstrated the greatest long-term risk of meniscal injury compared with nonoperative treatment or early ACLR. Risk factors for decreased meniscal survival after ACLR included increased time to surgery, shorter time to return to sports or activity, older age at injury, and involvement in high-impact or rotational landing sports. Pending careful external validation, these models may be deployed in the clinical space to provide real-time insights and enhance decision making.

Purpose To determine whether conventional logistic regression or machine learning algorithms were more precise in identifying the risk factors for unplanned overnight admission after medial patellofemoral ligament (MPFL) reconstruction. Methods A retrospective review of the prospectively collected National Surgical Quality Improvement Program database was performed to identify patients who underwent outpatient MPFL reconstruction from 2006–2018. Patients admitted overnight were identified as those with length of stay of 1 or more days. Models were generated using random forest, extreme gradient boosting, adaptive boosting, or elastic net penalized logistic regression, and an additional model was produced as a weighted ensemble of the 4 final algorithms. The predictive capacity of these models was compared to that of logistic regression. Results Of the 1307 patients identified, 221 (16.9%) required at least one overnight stay after MPFL reconstruction. Multivariate logistic regression found the following variables to be predictors of inpatient admission: age (odds ratio [OR] = 1.03 [95% confidence interval {CI} 1.02‐1.04]; P <.001), spinal anesthesia (OR = 3.42 [95% CI 1.98‐6.08]; P < .001), American Society of Anesthesiologists (ASA) class 3/4 (OR = 1.96 [95% CI 1.25‐3.06]; P < .001), history of chronic obstructive pulmonary disease (COPD) (OR = 6.44 [95% CI 1.58‐26.17]; P = .02), and body mass index (BMI) (OR = 1.03 [95% CI 1.01‐1.05]; P < .001). The ensemble model achieved the best performance based on discrimination assessed via internal validation (area under the curve = 0.722). The variables determined most important by the ensemble model were increasing BMI, increasing age, ASA class, anesthesia, smoking, hypertension, lateral release, and history of COPD. Conclusions An internally validated machine learning algorithm outperformed logistic regression modeling in predicting the need for unplanned overnight hospitalization after MPFL reconstruction. In this model, the most significant risk factors for admission were age, BMI, ASA class, smoking status, hypertension, lateral release, and history of COPD. This tool can be deployed to augment provider assessment to identify high‐risk candidates and appropriately set postoperative expectations for patients. Clinical Relevance Identifying and mitigating patient risk factors to prevent adverse surgical outcomes and hospitalizations is one of our primary goals. There may be a key role for machine learning algorithms to help successfully and efficiently risk stratify patients to decrease costs, appropriately set postoperative expectations, and increase the quality of delivered care.