Avulsion Fracture of Bicruciate Ligament and Patellar Tendon in Bicruciate-Retaining Total Knee Arthroplasty

Suture Anchor Fixation Has Less Gap Formation and No Difference in Complications or Revisions for Patellar Tendon Repair: A Systematic Review

PURPOSE

To evaluate the literature on suture anchor (SA) usage for patellar tendon repair, summarize the overall biomechanical and clinical outcomes, and assess whether the cumulative research supports the adoption of this technique compared with transosseous (TO) repair.

METHODS

A systematic literature review using the Preferred Reporting Items for Systematic and Meta-Analyses guidelines was performed. Multiple electronic databases were searched to identify studies focusing on surgical outcomes of patellar tendon repair with suture anchor usage. Cadaver and animal biomechanical studies, technical studies, and clinical studies were included.

RESULTS

A total of 29 studies met the inclusion criteria: 6 cadaver, 3 animal, 9 technical, and 11 clinical reports. Four of the 6 cadaver studies and 1 of the 2 animal studies found significantly less gap formation from SA than from TO repair. Average gap formation in human studies ranged from 0.9 to 4.1 mm in the SA group compared with 2.9 to 10.3 mm in TO groups. Load to failure was significantly stronger in 1 of 5 cadaver studies and 2 of 3 animal studies, with human studies SA load to failure ranging from 258 to 868 N and TO load to failure ranging from 287 to 763 N. There were 11 clinical studies that included 133 knees repaired using SA. Nine studies showed no difference between complication rate or risk for reoperation, where one study reported a significantly lower re-rupture rate after SA repair compared with TO repair.

CONCLUSIONS

SA repair is a viable option for patellar tendon repair and could have several advantages over TO repair. Multiple studies indicate that SA repair has less gap formation during biomechanical testing compared with TO repair in human cadaver and animal models. No differences in complications or revisions were found in the majority of clinical studies.

CLINICAL RELEVANCE

Both animal and human models suggest SA fixation has potential biomechanical benefits when compared with TO tunnels for patellar tendon repair, whereas clinical studies show no difference in complications and revisions postoperatively.

Bicruciate retaining total knee arthroplasty: current state and future promise

Total knee arthroplasty (TKA) is a well-known surgical procedure performed to address end stage osteoarthritis. The main goal is to relieve pain, recover articular function and return to normal function as soon as possible. Over the years it is frequently performed in the elderly, but lately there is an increased demand in a younger and more active population. Up to 25% of patients feel dissatisfied about their TKA. The anterior cruciate ligament (ACL) is considered the main anteroposterior stabilizer of the knee; nevertheless the ACL is usually sacrificed during conventional TKA. Research shows this might be an unnecessary sacrifice in certain cases. The considerable dissatisfaction rate in mainly high-demanding patients, together with the literature reports on the importance of the ACL function, were the two main reasons for the development of bicruciate retaining (BCR) total knee arthroplasty. BCR TKA may offer superior knee kinematics and proprioception, through anterior cruciate ligament preservation, but requires a higher level of attention to obtain an accurate and precise component orientation to reach proper ligamentous balancing and restore the native knee biomechanics. Many surgeons abandoned its use due to its challenging technique and inconsistent results. Recent new BCR implant designs are promising. This systematic literature review aims to summarize the current state of BCR TKA and what to expect in the future.

A novel image-based machine learning model with superior accuracy and predictability for knee arthroplasty loosening detection and clinical decision making

Background

Loosening is the leading cause of total knee arthroplasty (TKA) revision. This is a heavy burden toward the healthcare system owing to the difficulty in diagnosis and complications occurring from the delay management. Based on automatic analytical model building, machine learning, may potentially help to automatically recognize the risk of loosening based on radiographs alone. The aim of this study was to build an image-based machine-learning model for detecting TKA loosening.

Methods

Image-based machine-learning model was developed based on ImageNet, Xception model and a TKA patient X-ray image dataset. Based on a dataset with TKA patient clinical parameters, another system was then created for developing the clinical-information-based machine learning model with random forest classifier. In addition, the Xception Model was pre-trained on the ImageNet database with python and TensorFlow deep learning library for the prediction of loosening. Class activation maps were also used to interpret the prediction decision made by model. Two senior orthopaedic specialists were invited to assess loosening from X-ray images for 3 attempts in setting up comparison benchmark.

Result

In the image-based machine learning loosening model, the precision rate and recall rate were 0.92 and 0.96, respectively. While for the accuracy rate, 96.3% for visualization classification was observed. However, the addition of clinical-information-based model, with precision rate of 0.71 and recall rate of 0.20, did not further showed improvement on the accuracy. Moreover, as class activation maps showed corresponding signals over bone-implant interface that is loosened radiographically, this confirms that the current model utilized a similar image recognition pattern as that of inspection by clinical specialists.

Conclusion

The image-based machine learning model developed demonstrated high accuracy and predictability of knee arthroplasty loosening. And the class activation heatmap matched well with the radiographic features used clinically to detect loosening, which highlighting its potential role in assisting clinicians in their daily practice. However, addition of clinical-information-based machine-learning model did not offer further improvement in detection. As far as we know, this is the first report of pure image-based machine learning model with high detection accuracy. Importantly, this is also the first model to show relevant class activation heatmap corresponding to loosening location.

Translational potential

The finding in this study indicated image-based machine learning model can detect knee arthroplasty loosening with high accuracy and predictability, which the class activation heatmap can potentially assist surgeons to identify the sites of loosening.