Variation in the Thickness of Knee Cartilage. The Use of a Novel Machine Learning Algorithm for Cartilage Segmentation of Magnetic Resonance Images

Widening of tibial resection boundaries increases the rate of femoral component valgus and internal rotation in functionally aligned TKA

PURPOSE

The purpose of this study was to investigate the influence of increasing the tibial boundaries in functional alignment on femoral component orientation in total knee arthroplasty (TKA).

METHODS

A retrospective review of a database of robotic-assisted TKAs using a digital joint tensioning device was performed (BalanceBot®; Corin). A total of 692 TKAs with correctable deformity were included. Functional alignment with a tibia-first balancing technique was simulated by performing an anatomic tibial resection to recreate the native medial proximal tibial angle within certain boundaries (A, 87-90°; B, 86-90°; C, 84-92°), while accounting for wear. After balancing the knee, the resulting amount of femoral component outliers in the coronal and axial plane was calculated for each group and correlated to the coronal plane alignment of the knee (CPAK) classification.

RESULTS

The proportion of knees with high femoral component varus (>96°) or valgus (<87°) alignment increased from 24.5% (n = 170) in group A to 26.5% (n = 183) in group B and 34.2% (n = 237) in group C (p < 0.05). Similarly, more knees with high femoral component external rotation (>6°) or internal rotation (>3°) were identified in group C (33.4%, n = 231) than in group B (23.7%, n = 164) and A (18.4%, n = 127) (p < 0.05). There was a statistically significant (p < 0.01) overall increase in knees with both femoral component valgus <87° and internal rotation >3° from group A (4.0%, n = 28) to B (7.7%, n = 53) and C (15.8%, n = 109), with CPAK type I and II showing a 12.9- and 2.9-fold increase, respectively.

CONCLUSION

Extending the tibial boundaries when using functional alignment with a tibia-first balancing technique in TKA leads to a statistically significant higher percentage of knees with a valgus lateral distal femoral angle < 87° and >3° internal rotation of the femoral component, especially in CPAK type I and II.

LEVEL OF EVIDENCE

Level IV.

Automated measurement and grading of knee cartilage thickness: a deep learning-based approach

Background

Knee cartilage is the most crucial structure in the knee, and the reduction of cartilage thickness is a significant factor in the occurrence and development of osteoarthritis. Measuring cartilage thickness allows for a more accurate assessment of cartilage wear, but this process is relatively time-consuming. Our objectives encompass using various DL methods to segment knee cartilage from MRIs taken with different equipment and parameters, building a DL-based model for measuring and grading knee cartilage, and establishing a standardized database of knee cartilage thickness.

Methods

In this retrospective study, we selected a mixed knee MRI dataset consisting of 700 cases from four datasets with varying cartilage thickness. We employed four convolutional neural networks-UNet, UNet++, ResUNet, and TransUNet-to train and segment the mixed dataset, leveraging an extensive array of labeled data for effective supervised learning. Subsequently, we measured and graded the thickness of knee cartilage in 12 regions. Finally, a standard knee cartilage thickness dataset was established using 291 cases with ages ranging from 20 to 45 years and a Kellgren-Lawrence grading of 0.

Results

The validation results of network segmentation showed that TransUNet performed the best in the mixed dataset, with an overall dice similarity coefficient of 0.813 and an Intersection over Union of 0.692. The model's mean absolute percentage error for automatic measurement and grading after segmentation was 0.831. The experiment also yielded standard knee cartilage thickness, with an average thickness of 1.98 mm for the femoral cartilage and 2.14 mm for the tibial cartilage.

Conclusion

By selecting the best knee cartilage segmentation network, we built a model with a stronger generalization ability to automatically segment, measure, and grade cartilage thickness. This model can assist surgeons in more accurately and efficiently diagnosing changes in patients' cartilage thickness.

Machine Learning Models for Prediction of Progression of Knee Osteoarthritis: A Comprehensive Analysis

Prediction of the progression of knee osteoarthritis (KOA) is a very challenging task. Early identification of risk factors plays a vital role in diagnosing KOA. Thus, machine learning models are used to predict the progression of KOA. The purpose of the present study is to find out the efficacy of various machine learning models to identify the progression of KOA. A comprehensive literature search was conducted in international databases like Google Scholar, PubMed, Web of Science, and Scopus. Studies published from the year 2010 to May 2023 on the machine learning approach to diagnose KOA were included in the study. A total of 15 studies were selected and analyzed which included machine learning as an approach to diagnose KOA. The present study found that machine learning methods are the best methods to diagnose KOA early. Various methods like deep learning, machine learning, convolutional neural network (CNN), and multi-layer perceptron showed good accuracy in diagnosing its progression. The machine learning approach has attracted significant interest from scientists and researchers and has led to a new automated approach to diagnose KOA, which will help in designing treatment approaches.

Does robotic-assisted unicompartmental knee arthroplasty restore native joint line more accurately than with conventional instruments?

The study's primary aim is the restoration of native joint line in patients having robotic-assisted unicondylar knee arthroplasty and conventional unicondylar knee arthroplasty. Literature in the past has demonstrated that reducing the joint line can result in greater failure rates. This is a prospective cohort investigation of patients who had medial UKA between March 2017 and March 2022.All patient's pre-operative and post-operative radiological joint line assessments were examined by two observers by Weber's methods. Robotic-assisted UKA performed with hand-held image-free robots was compared to conventional UKA groups. The distal position of the femoral component was higher in Group B utilizing conventional tools than in Group A employing robotic-assisted UKA. This positional difference was statistically significant. The mean difference among the pre-operative and post-operative joint lines in Group A was 1.6 ± 0.49 (range 0.8 mm-2.4 mm), while it was 2.47 ± 0.51 (range 1.6 mm-3.9 mm) (p 0.005) in Group B. In Group A, a greater percentage of the subjects (64%) attained a femoral component position within two millimeters from the joint line, whereas just 18% in Group B did. When compared with the conventional UKA technique, the meticulous attention to detail and planning for ligament rebalancing when using the robotic-assisted UKA technique not solely enhance surgical precision for implant placing but additionally provides excellent native joint line restoration and balancing. For validation of its longevity and survivability, the cohort must be tracked for a longer period of time.

The knee connectome: A novel tool for studying spatiotemporal change in cartilage thickness

Cartilage thickness change is a well-documented biomarker of osteoarthritis pathogenesis. However, there is still much to learn about the spatial and temporal patterns of cartilage thickness change in health and disease. In this study, we develop a novel analysis method for elucidating such patterns using a functional connectivity approach. Descriptive statistics are reported for 1186 knees that did not develop osteoarthritis during the 8 years of observation, which we present as a model of cartilage thickness change related to healthy aging. Within the control population, patterns vary greatly between male and female subjects, while body mass index (BMI) has a more moderate impact. Finally, several differences are shown between knees that did and did not develop osteoarthritis. Some but not all significance appears to be accounted for by differences in sex, BMI, and knee alignment. With this work, we present the connectome as a novel tool for studying spatiotemporal dynamics of tissue change.

Unrestricted kinematic alignment corrects fixed flexion contracture in robotically aligned total knees without raising the joint line in extension

PURPOSE

Mechanically Aligned Total Knee Arthroplasty (MA TKA) typically addresses fixed flexion contractures (FFC) by raising the joint line during extension. However, in unrestricted Kinematically Aligned TKA (KA TKA) utilizing a caliper-based resection technique, the joint line is not raised. This study aims to determine the efficacy of KA TKA in restoring full extension in patients with FFC without increasing distal femoral resection, considering tibial bone resection and sagittal component positioning.

METHODS

A retrospective study was conducted by a single surgeon, involving patients who underwent primary robotically assisted cruciate retaining unrestricted KA TKA between June 1, 2021, and December 1, 2022. Complete intraoperative resection and alignment data were recorded, including the thickness of distal femoral and proximal tibial bone cuts. Patients with a preoperative FFC ≥ 5° (study group) were compared to those with FFC < 5° (control group). The impact of variations in tibial resection and sagittal component positioning was assessed by comparing the heights of medial and lateral resections, sagittal femoral component flexion, and tibial slope. Group comparisons were analyzed using the Wilcoxon Signed Rank Test, with a significance level set at p < 0.05.

RESULTS

A total of 48 KA TKA procedures met the inclusion criteria, with 24 performed on women. The mean preoperative FFC in the study group was 11.2° (range: 5-25°), while the control group exhibited 1° (range: 0-4°) (p < 0.001). There were no statistically significant differences observed between the study and control groups in terms of distal femoral resections, both medially (p = 0.14) and laterally (p = 0.23), as well as tibial resection heights, both medially (p = 0.66) and laterally (p = 0.74). The alignment of the femoral component flexion and tibial slope was comparable between the two groups (p = 0.31 and p = 0.54, respectively). All patients achieved within 5 degrees of full extension at closure.

CONCLUSION

Robotic arm-assisted unrestricted KA TKA effectively restores full extension without raising the joint line during extension for patients with a preoperative fixed flexion contracture.

LEVEL OF EVIDENCE

III.

Associations between weight change, knee subcutaneous fat and cartilage thickness in overweight and obese individuals: 4-Year data from the osteoarthritis initiative

OBJECTIVE

To assess (i) the impact of changes in body weight on changes in joint-adjacent subcutaneous fat (SCF) and cartilage thickness over 4 years and (ii) the relation between changes in joint-adjacent SCF and knee cartilage thickness.

DESIGN

Individuals from the Osteoarthritis Initiative (total=399) with > 10% weight gain (n=100) and > 10% weight loss (n=100) over 4 years were compared to a matched control cohort with less than 3% change in weight (n=199). 3.0T Magnetic Resonance Imaging (MRI) of the right knee was performed at baseline and after 4 years to quantify joint-adjacent SCF and cartilage thickness. Linear regression models were used to evaluate the associations between the (i) weight change group and 4-year changes in both knee SCF and cartilage thickness, and (ii) 4-year changes in knee SCF and in cartilage thickness. Analyses were adjusted for age, sex, baseline body mass index (BMI), tibial diameter (and weight change group in analysis (ii)).

RESULTS

Individuals who lost weight over 4-years had significantly less joint-adjacent SCF (beta range, medial/lateral joint sides: 2.2-4.2 mm, p < 0.001) than controls; individuals who gained weight had significantly greater joint-adjacent SCF than controls (beta range: -1.4 to -3.9 mm, p < 0.001). No statistically significant associations were found between weight change and cartilage thickness change. However, increases in joint-adjacent SCF over 4 years were significantly associated with decreases in cartilage thickness (p = 0.04).

CONCLUSIONS

Weight change was associated with joint-adjacent SCF, but not with change in cartilage thickness. However, 4-year increases in joint-adjacent SCF were associated with decreases in cartilage thickness independent of baseline BMI and weight change group.

Injectable and biodegradable piezoelectric hydrogel for osteoarthritis treatment

Osteoarthritis affects millions of people worldwide but current treatments using analgesics or anti-inflammatory drugs only alleviate symptoms of this disease. Here, we present an injectable, biodegradable piezoelectric hydrogel, made of short electrospun poly-L-lactic acid nanofibers embedded inside a collagen matrix, which can be injected into the joints and self-produce localized electrical cues under ultrasound activation to drive cartilage healing. In vitro, data shows that the piezoelectric hydrogel with ultrasound can enhance cell migration and induce stem cells to secrete TGF-β1, which promotes chondrogenesis. In vivo, the rabbits with osteochondral critical-size defects receiving the ultrasound-activated piezoelectric hydrogel show increased subchondral bone formation, improved hyaline-cartilage structure, and good mechanical properties, close to healthy native cartilage. This piezoelectric hydrogel is not only useful for cartilage healing but also potentially applicable to other tissue regeneration, offering a significant impact on the field of regenerative tissue engineering.

Charting Aging Trajectories of Knee Cartilage Thickness for Early Osteoarthritis Risk Prediction: An MRI Study from the Osteoarthritis Initiative Cohort

Knee osteoarthritis (OA), a prevalent joint disease in the U.S., poses challenges in terms of predicting of its early progression. Although high-resolution knee magnetic resonance imaging (MRI) facilitates more precise OA diagnosis, the heterogeneous and multifactorial aspects of OA pathology remain significant obstacles for prognosis. MRI-based scoring systems, while standardizing OA assessment, are both time-consuming and labor-intensive. Current AI technologies facilitate knee OA risk scoring and progression prediction, but these often focus on the symptomatic phase of OA, bypassing initial-stage OA prediction. Moreover, their reliance on complex algorithms can hinder clinical interpretation. To this end, we make this effort to construct a computationally efficient, easily-interpretable, and state-of-the-art approach aiding in the radiographic OA (rOA) auto-classification and prediction of the incidence and progression, by contrasting an individual's cartilage thickness with a similar demographic in the rOA-free cohort. To better visualize, we have developed the toolset for both prediction and local visualization. A movie demonstrating different subtypes of dynamic changes in local centile scores during rOA progression is available at https://tli3.github.io/KneeOA/. Specifically, we constructed age-BMI-dependent reference charts for knee OA cartilage thickness, based on MRI scans from 957 radiographic OA (rOA)-free individuals from the Osteoarthritis Initiative cohort. Then we extracted local and global centiles by contrasting an individual's cartilage thickness to the rOA-free cohort with a similar age and BMI. Using traditional boosting approaches with our centile-based features, we obtain rOA classification of KLG ≤ 1 versus KLG = 2 (AUC = 0.95, F1 = 0.89), KLG ≤ 1 versus KLG ≥ 2 (AUC = 0.90, F1 = 0.82) and prediction of KLG2 progression (AUC = 0.98, F1 = 0.94), rOA incidence (KLG increasing from < 2 to ≥ 2; AUC = 0.81, F1 = 0.69) and rOA initial transition (KLG from 0 to 1; AUC = 0.64, F1 = 0.65) within a future 48-month period. Such performance in classifying KLG ≥ 2 matches that of deep learning methods in recent literature. Furthermore, its clinical interpretation suggests that cartilage changes, such as thickening in lateral femoral and anterior femoral regions and thinning in lateral tibial regions, may serve as indicators for prediction of rOA incidence and early progression. Meanwhile, cartilage thickening in the posterior medial and posterior lateral femoral regions, coupled with a reduction in the central medial femoral region, may signify initial phases of rOA transition.

Comparable accuracy of femoral joint line reconstruction in different kinematic and functional alignment techniques

PURPOSE

A key part of kinematic alignment (KA) and functional alignment (FA) is to restore the natural femoral joint line, in particular the medial joint line. KA is known to reproduce the femoral joint line accurately; however, direct comparisons with other surgical techniques such as FA are currently lacking. The purpose of this study was to evaluate differences of alignment parameters in KA and FA techniques with a special focus given to the femoral joint line.

METHODS

We performed a retrospective radiological analysis of pre- and postoperative long leg radiographs of 221 consecutive patients with varus or neutral leg alignment, who underwent primary total knee arthroplasty (TKA) procedures from 2018 to 2020. Patients were assigned to one of four groups: (1) FA: image-based robotic-assisted TKA, (2) FA: imageless robotic-assisted TKA, (3): restricted KA: 3D cutting block-assisted (patient-specific instruments, PSI) TKA, (4): unrestricted KA: calipered technique. Patients' radiographs were (re)-analyzed for overall limb alignment, medial proximal tibial angle (MPTA), lateral distal femoral angle (LDFA), as well as medial and lateral femoral joint line alteration. Statistical significance was determined using unpaired t testing (FA vs. KA group) and one-way ANOVA (subgroup analyses).

RESULTS

Comparisons of KA vs. FA, as well as individual subgroups of KA and FA did not show any differences in the accuracy of medial joint line reconstruction (< 2 mm, p = 0.384, p = 0.744, respectively) and LDFA alteration (< 2°, p = 0.997, 0.921, respectively). Correction of MPTA (3.4° vs. 2.2°) and lateral femoral joint line (2.1 mm vs. 1.5 mm) was higher for FA and FA subgroups compared to KA and KA subgroups (both p < 0.001).

CONCLUSION

Kinematic and functional alignments showed a comparable accuracy in reconstruction of the medial femoral joint line and femoral joint line orientation. Increased correction of MPTA and lateral femoral joint line was recorded with FA techniques.

LEVEL OF EVIDENCE

III.

Radiofrequency-Based Chondroplasty Creates a Precise Area of Targeted Chondrocyte Death With Minimal Necrosis Outside the Target Zone: A Systematic Review

Purpose

To systematically examine the effects of radiofrequency (RF) ablation or coblation (controlled ablation) on chondrocyte viability following knee chondroplasty in preclinical literature to determine the effectiveness and safety of RF-based techniques.

Methods

A literature search was performed in September 2022 using PubMed and Scopus using the following search terms combined with Boolean operators: "chondroplasty," "radiofrequency," "thermal," "knee," "chondral defect," "articular cartilage," and "cartilage." The inclusion criteria consisted of preclinical studies examining the effect of RF ablation or coblation on chondrocytes during knee chondroplasty. Exclusion criteria consisted of studies reporting chondroplasty in joints other than the knee, clinical studies, in vitro studies using animal models, case reports, non-full-text articles, letters to editors, surveys, review articles, and abstracts. The following data were extracted from the included articles: author, year of publication, chondral defect location within the knee and chondral characteristics, RF probe characteristics, cartilage macroscopic description, microscopic chondrocyte description, and extracellular matrix characteristics.

Results

A total of 17 articles, consisting of 811 cartilage specimens, were identified. The mean specimen age was 63.4 ± 6.0 (range, 37-89) years. Five studies used monopolar RF devices, 7 studies used bipolar RF devices, whereas 4 studies used both monopolar and bipolar RF devices. Time until cell death during ablation at any power was reported in 5 studies (n = 351 specimens), with a mean time to cell death of 54.4 seconds (mean range, 23.1-64) for bipolar RF and 56.3 seconds (mean range, 12.5-64) for monopolar RF devices. Chondrocyte cell death increased with increased wattage, while treatment time was positively correlated with deeper cell death.

Conclusions

In this systematic review, histologic analysis demonstrated that RF-based chondroplasty creates a precise area of targeted chondrocyte death, with minimal evidence of necrosis outside the target zone. Caution must be exercised when performing RF-based chondroplasty due to the risk of cell death with increased application time and wattage.

Clinical Relevance

Although RF ablation has demonstrated favorable results in preliminary trials, including smoother cartilage and less damage to the surrounding healthy tissue, the risks versus benefits of the procedure are largely unknown. Caution must be exercised when performing RF-based chondroplasty in the clinical setting due to the risk of cell death with increased application time and wattage.

Characterization and Advancement of an Evaluation Method for the Treatment of Spontaneous Osteoarthritis in STR/ort Mice: GRGDS Peptides as a Potential Treatment for Osteoarthritis

STR/ort mice spontaneously exhibit the typical osteoarthritis (OA) phenotype. However, studies describing the relationship between cartilage histology, epiphyseal trabecular bone, and age are lacking. We aimed to evaluate the typical OA markers and quantify the subchondral bone trabecular parameters in STR/ort male mice at different weeks of age. We then developed an evaluation model for OA treatment. We graded the knee cartilage damage using the Osteoarthritis Research Society International (OARSI) score in STR/ort male mice with or without GRGDS treatment. We measured the levels of typical OA markers, including aggrecan fragments, matrix metallopeptidase-13 (MMP-13), collagen type X alpha 1 chain (COL10A1), and SRY-box transcription factor 9 (Sox9), and quantified epiphyseal trabecular parameters. Compared to the young age group, elderly mice showed an increased OARSI score, decreased chondrocyte columns of the growth plate, elevated expression of OA markers (aggrecan fragments, MMP13, and COL10A1), and decreased expression of Sox9 at the articular cartilage region in elderly STR/ort mice. Aging also significantly enhanced the subchondral bone remodeling and microstructure change in the tibial plateau. Moreover, GRGDS treatment mitigated these subchondral abnormalities. Our study presents suitable evaluation methods to characterize and measure the efficacy of cartilage damage treatments in STR/ort mice with spontaneous OA.

Concerns surrounding application of artificial intelligence in hip and knee arthroplasty

Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered.

Cite this article: Bone Joint J 2022;104-B(12):1292–1303.

Proximal tibial morphology is associated with risk of trauma to the posteromedial structures during tibial bone resection reproducing the anatomical posterior tibial slope in bicruciate-retaining total knee arthroplasty

BACKGROUND

A tibial cut with the native posterior tibial slope (PTS) is a theoretical prerequisite in bicruciate-retaining total knee arthroplasty (BCRTKA) to regain physiological knee kinematics. The present study reveals tibial morphological risk factors of trauma to the posteromedial structures of the knee during tibial bone resection in BCRTKA.

METHODS

Fifty patients undergoing BCRTKA for varus knee osteoarthritis were analyzed. A three-dimensional tibial bone model was reconstructed using a computed tomography-based preoperative planning system, and the coronal tibial slope (CTS) and medial PTS (MPTS) were measured. Then, we set the simulated tibial cutting plane neutral on the coronal plane, posteriorly inclined in accordance with the MPTS on the sagittal plane, and 9 mm below the surface of the subchondral cortical bone (i.e., 11 mm below the surface of the cartilage) of the lateral tibial plateau. The association between the tibial morphology and the distance from the simulated cutting plane to the semimembranosus (SM) insertion (Dsm) was analyzed.

RESULTS

Of the 50 patients, 19 (38%) had negative Dsm values, indicating a cut into the SM (namely, below the posterior oblique ligament) insertion. The MPTS was negatively correlated with Dsm (r = -0.396, p = 0.004), whereas the CTS was positively correlated with Dsm (r = 0.619, p < 0.001). On multivariate linear regression analysis, the MPTS and CTS were independent predictors of Dsm.

CONCLUSION

In the setting of tibial cuts reproducing the native MPTS in BCRTKA, patients with larger PTS and smaller CTS had more risk of trauma to the posteromedial structures.

Machine learning in orthopaedic surgery

Artificial intelligence and machine learning in orthopaedic surgery has gained mass interest over the last decade or so. In prior studies, researchers have demonstrated that machine learning in orthopaedics can be used for different applications such as fracture detection, bone tumor diagnosis, detecting hip implant mechanical loosening, and grading osteoarthritis. As time goes on, the utility of artificial intelligence and machine learning algorithms, such as deep learning, continues to grow and expand in orthopaedic surgery. The purpose of this review is to provide an understanding of the concepts of machine learning and a background of current and future orthopaedic applications of machine learning in risk assessment, outcomes assessment, imaging, and basic science fields. In most cases, machine learning has proven to be just as effective, if not more effective, than prior methods such as logistic regression in assessment and prediction. With the help of deep learning algorithms, such as artificial neural networks and convolutional neural networks, artificial intelligence in orthopaedics has been able to improve diagnostic accuracy and speed, flag the most critical and urgent patients for immediate attention, reduce the amount of human error, reduce the strain on medical professionals, and improve care. Because machine learning has shown diagnostic and prognostic uses in orthopaedic surgery, physicians should continue to research these techniques and be trained to use these methods effectively in order to improve orthopaedic treatment.

Towards understanding mechanistic subgroups of osteoarthritis: 8-year cartilage thickness trajectory analysis

Many studies have validated cartilage thickness as a biomarker for knee osteoarthritis (OA); however, few studies investigate beyond cross-sectional observations or comparisons across two timepoints. By characterizing the trajectory of cartilage thickness changes over 8 years in healthy individuals from the OA initiative data set, this study discovers associations between the dynamics of cartilage changes and OA incidence. A fully automated cartilage segmentation and thickness measurement method were developed and validated against manual measurements: mean absolute error = 0.11-0.14 mm (n = 4129 knees) and automatic reproducibility = 0.04-0.07 mm (n = 316 knees). The mean thickness for the medial and lateral tibia (MT, LT), central weight-bearing medial and lateral femur (cMF, cLF), and patella (P) cartilage compartments were quantified for 1453 knees at seven timepoints. Trajectory subgroups were defined per cartilage compartment such as stable, thinning to thickening, accelerated thickening, plateaued thickening, thickening to thinning, accelerated thinning, or plateaued thinning. For tibiofemoral compartments, the stable (22%-36%) and plateaued thinning (22%-37%) trajectories were the most common, with an average initial velocity (μm/month), acceleration (μm/month² ) for the plateaued thinning trajectories LT: -2.66, 0.0326; MT: -2.49, 0.0365; cMF: -3.51, 0.0509; and cLF: -2.68, 0.041. In the patella compartment, the plateaued thinning (35%) and thickening to thinning (24%) trajectories were the most common, with an average initial velocity, acceleration for each -4.17, 0.0424; 1.95, -0.0835. Knees with nonstable trajectories had higher adjusted odds of OA incidence than stable trajectories: accelerated thickening, accelerated thinning, and plateaued thinning trajectories of the MT had adjusted odds ratio (OR) of 18.9, 5.48, and 1.47, respectively; in the cMF, adjusted OR of 8.55, 10.1, and 2.61, respectively.

Knee Cartilage Thickness Differs Alongside Ages: A 3-T Magnetic Resonance Research Upon 2,481 Subjects via Deep Learning

Background: It was difficult to distinguish the cartilage thinning of an entire knee joint and to track the evolution of cartilage morphology alongside ages in the general population, which was of great significance for studying osteoarthritis until big imaging data and artificial intelligence are fused. The purposes of our study are (1) to explore the cartilage thickness in anatomical regions of the knee joint among a large collection of healthy knees, and (2) to investigate the relationship between the thinning pattern of the cartilages and the increasing ages. Methods: In this retrospective study, 2,481 healthy knees (subjects ranging from 15 to 64 years old, mean age: 35 ± 10 years) were recruited. With magnetic resonance images of knees acquired on a 3-T superconducting scanner, we automatically and precisely segmented the cartilage via deep learning and calculated the cartilage thickness in 14 anatomical regions. The thickness readings were compared using ANOVA by considering the factors of age, sex, and side. We further tracked the relationship between the thinning pattern of the cartilage thickness and the increasing ages by regression analysis. Results: The cartilage thickness was always thicker in the femur than corresponding regions in the tibia (p < 0.05). Regression analysis suggested cartilage thinning alongside ages in all regions (p < 0.05) except for medial and lateral anterior tibia in both females and males (p > 0.05). The thinning speed of men was faster than women in medial anterior and lateral anterior femur, yet slower in the medial patella (p < 0.05). Conclusion: We established the calculation method of cartilage thickness using big data and deep learning. We demonstrated that cartilage thickness differed across individual regions in the knee joint. Cartilage thinning alongside ages was identified, and the thinning pattern was consistent in the tibia while inconsistent in patellar and femoral between sexes. These findings provide a potential reference to detect cartilage anomaly.

Brain Tumor Classification & Segmentation by Using Advanced DNN, CNN & ResNet-50 Neural Networks

In the medical domain, brain image classification is an extremely challenging field. Medical images play a vital role in making the doctor's precise diagnosis and in the surgery process. Adopting intelligent algorithms makes it feasible to detect the lesions of medical images quickly, and it is especially necessary to extract features from medical images. Several studies have integrated multiple algorithms toward medical images domain. Concerning feature extraction from the medical image, a vast amount of data is analyzed to achieve processing results, helping physicians deliver more precise case diagnoses. Image processing mechanism becomes extensive usage in medical science to advance the early detection and treatment aspects. In this aspect, this paper takes tumor, and healthy images as the research object and primarily performs image processing and data augmentation process to feed the dataset to the neural networks. Deep neural networks (DNN), to date, have shown outstanding achievement in classification and segmentation tasks. Carrying this concept into consideration, in this study, we adopted a pre-trained model Resnet_50 for image analysis. The paper proposed three diverse neural networks, particularly DNN, CNN, and ResNet-50. Finally, the splitting dataset is individually assigned to each simplified neural network. Once the image is classified as a tumor accurately, the OTSU segmentation is employed to extract the tumor alone. It can be examined from the experimental outcomes that the ResNet-50 algorithm shows high accuracy 0.996, precision 1.00 with best F1 score 1.0, and minimum test losses of 0.0269 in terms of Brain tumor classification. Extensive experiments prove our offered tumor detection segmentation efficiency and accuracy. To this end, our approach is comprehensive sufficient and only requires minimum pre-and post-processing, which allows its adoption in various medical image classification & segmentation tasks.

Robotic-assisted Unicompartmental knee Arthroplasty optimizes joint line restitution better than conventional surgery

Purpose

To compare joint line restoration after unicompartmental knee arthroplasty (UKA) between conventional and robotic-assisted surgery. Previous studies have shown that joint line distalization can lead to higher failure rates. The hypothesis was that robotic-assisted UKA is associated with less femoral component distalization and a precise tibial cut, which allows a more anatomical restitution of the knee joint line.

Methods

Retrospective cohort study of patients undergoing medial or lateral UKA between May 2018 and March 2020. Preoperative and postoperative radiologic assessment of the joint line was performed by two observers, using three different methods, one for tibial slope and one for tibial resection. Robotic assisted UKA and conventional UKA groups were compared.

Results

Sixty UKA were included, of which 48 (77.42%) were medial. Robotic-assisted UKA were 40 (64.52%) and 22(35.48%) were conventional

The distalization of the femoral component was higher in the conventional group despite the method of measurement used In both Weber methods, the difference was statistically different: Conventional 2.3 (0.9 to 5.6) v/s Robotic 1.5 (− 1.1 to 4.1) (p =0.0025*). A higher proportion of patients achieved a femoral component position ≤ two millimeters from the joint line using robotic-assisted UKA compared to the conventional technique .

No statistical difference between robotic-assisted and conventional UKA was found in tibial resection and slope.

Conclusion

Robotic-assisted UKA shows a better rate of joint line restoration due to less femoral component distalization than conventional UKA. No difference was found in the amount of tibial resection between groups in this study.

Level of evidence

III

Iatrogenic Articular Cartilage Injury in Arthroscopic Hip and Knee Videos and the Potential for Cartilage Cell Death When Simulated in a Bovine Model

PURPOSE

To determine the incidence and characterize the severity of iatrogenic cartilage injuries.

METHODS

Technique videos of arthroscopic femoral acetabular impingement procedures and meniscus repairs on VuMedi (n = 85) and Arthroscopy Techniques (n = 45) were reviewed and iatrogenic cartilage injuries were identified and graded (minor, intermediate, and major injury) by 2 independent reviewers. To demonstrate that even minor injuries on a cellular scale result in damage, a bovine osteochondral explant was used to create comparable minor iatrogenic injuries at varied forces that do not disrupt the articular surface (1.5 N, 2.5 N, and 9.8 N). Dead chondrocytes at the site of injury were stained with ethidium homodimer-2 and imaged with an Olympus FV1000 confocal microscope. χ² tests were used for analysis; all results with P < .05 were considered significant.

RESULTS

In total, 130 videos of arthroscopic meniscus and femoral acetabular impingement procedures were analyzed and the incidence of iatrogenic cartilage injury was 73.8%. There were 110 (70.0%) minor, 35 (22.3%) intermediate, and 11 (7.0%) major iatrogenic injuries. All forces tested in the minor injury bovine model resulted in chondrocyte death at the site of contact.

CONCLUSIONS

Iatrogenic articular cartilage injuries are common in arthroscopy, occurring in more than 70% of the surgeon-published instructional videos analyzed. At least some chondrocyte death occurs with minor simulated iatrogenic injuries (1.5 N).

CLINICAL RELEVANCE

The high rate of cartilage damage during arthroscopic technique videos likely under-represents the true incidence in clinical practice. Cell death occurs in the bovine minor injury model with minimal contact forces. This suggests iatrogenic cartilage damage during arthroscopy could contribute to clinical outcomes.

Artificial Intelligence and Orthopaedics: An Introduction for Clinicians

➤Artificial intelligence (AI) provides machines with the ability to perform tasks using algorithms governed by pattern recognition and self-correction on large amounts of data to narrow options in order to avoid errors. ➤The 4 things necessary for AI in medicine include big data sets, powerful computers, cloud computing, and open source algorithmic development. ➤The use of AI in health care continues to expand, and its impact on orthopaedic surgery can already be found in diverse areas such as image recognition, risk prediction, patient-specific payment models, and clinical decision-making. ➤Just as the business of medicine was once considered outside the domain of the orthopaedic surgeon, emerging technologies such as AI warrant ownership, leverage, and application by the orthopaedic surgeon to improve the care that we provide to the patients we serve. ➤AI could provide solutions to factors contributing to physician burnout and medical mistakes. However, challenges regarding the ethical deployment, regulation, and the clinical superiority of AI over traditional statistics and decision-making remain to be resolved.

Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions

PURPOSE OF REVIEW

With the unprecedented advancement of data aggregation and deep learning algorithms, artificial intelligence (AI) and machine learning (ML) are poised to transform the practice of medicine. The field of orthopedics, in particular, is uniquely suited to harness the power of big data, and in doing so provide critical insight into elevating the many facets of care provided by orthopedic surgeons. The purpose of this review is to critically evaluate the recent and novel literature regarding ML in the field of orthopedics and to address its potential impact on the future of musculoskeletal care.

RECENT FINDINGS

Recent literature demonstrates that the incorporation of ML into orthopedics has the potential to elevate patient care through alternative patient-specific payment models, rapidly analyze imaging modalities, and remotely monitor patients. Just as the business of medicine was once considered outside the domain of the orthopedic surgeon, we report evidence that demonstrates these emerging applications of AI warrant ownership, leverage, and application by the orthopedic surgeon to better serve their patients and deliver optimal, value-based care.