Distinguishing risk of curve progression in adolescent idiopathic scoliosis with bone microarchitecture phenotyping - a 6-year longitudinal study.

Low bone mineral density and impaired bone qualities have been shown to be important prognostic factors for curve progression in Adolescent Idiopathic Scoliosis (AIS). There is no evidence-based integrative interpretation method to analyse high-resolution peripheral quantitative computed tomography (HR-pQCT) data in AIS. This study aimed to (a) utilize unsupervised machine learning to cluster bone microarchitecture phenotypes on HR-pQCT parameters in AIS girls, (b) assess the phenotypes' risk of curve progression and progression to surgical threshold at skeletal maturity (primary cohort), and (c) investigate risk of curve progression in a separate cohort of mild AIS girls whose curve severity did not reach bracing threshold at recruitment (secondary cohort). Patients were followed up prospectively for 6.22 ± 0.33 years in the primary cohort (N = 101). Three bone microarchitecture phenotypes were clustered by Fuzzy C-Means at time of peripubertal peak height velocity (PHV). Phenotype-1 had normal bone characteristics. Phenotype-2 was characterized by low bone volume and high cortical bone density, and Phenotype-3 had low cortical and trabecular bone density and impaired trabecular microarchitecture. The difference in bone qualities amongst the phenotypes was significant at peripubertal PHV and continued to skeletal maturity. Phenotype-3 had significantly increased risk of curve progression to surgical threshold at skeletal maturity (Odd Ratios (OR) = 4.88; 95% Confidence Interval (CI): 1.03-28.63). In the secondary cohort (N = 106), both Phenotype-2 (adjusted OR = 5.39; 95%CI: 1.47-22.76) and Phenotype-3 (adjusted OR = 3.67; 95%CI: 1.05-14.29) had increased risk of curve progression ≥6° with mean follow-up of 3.03 ± 0.16 years. In conclusion, three distinct bone microarchitecture phenotypes could be clustered by unsupervised machine learning on HR-pQCT generated bone parameters at peripubertal PHV in AIS. The bone qualities reflected by these phenotypes were found to have significant differentiating risk of curve progression and progression to surgical threshold at skeletal maturity in AIS.

Adolescent Idiopathic Scoliosis (AIS) is an abnormal spinal curvature commonly presents during puberty growth. Evidence has shown that low bone mineral density and impaired bone qualities are important risk factors for curve progression in AIS. High-resolution peripheral quantitative computed tomography (HR-pQCT) has improved our understanding of bone qualities in AIS. It generates a large amount of quantitative and qualitative bone parameters from a single measurement, but the data are not easy for clinicians to interpret and analyse. This study enrolled AIS girls and used unsupervised machine learning model to analyse their HR-pQCT data at first clinic visit. The model clustered the patients into 3 bone microarchitecture phenotypes (i.e. Phenotype-1: normal, Phenotype-2: low bone volume and high cortical bone density, and Phenotype-3: low cortical and trabecular bone density and impaired trabecular microarchitecture). They were longitudinally followed up for 6 years until skeletal maturity. We observed the three phenotypes were persistent, and Phenotype-3 had a significantly increased risk of curve progression to severity that requires invasive spinal surgery (Odds Ratio = 4.88, P = 0.029). The difference in bone qualities reflected by these 3 distinct phenotypes could aid clinicians to differentiate risk of curve progression and surgery at early stages of AIS.

Reconsidering "Critical" Bone Loss in Shoulder Instability: 17-Year Follow-Up Study following Arthroscopic Bankart Repair.

Background: Glenoid bone loss is a risk factor leading to the failure of arthroscopic Bankart repair. While 20-25% glenoid bone loss has long been considered the level to necessitate bony augmentation, recent studies indicate that 13.5% has a "subcritical" glenoid bone loss level, which is associated with decreased short- and medium-term functional scores. Few researchers worked on the long-term effect of "subcritical" or even less severe degrees of glenoid bone loss on redislocation rates and functional outcomes after arthroscopic Bankart repair. This study aimed to evaluate the effect of subcritical or less severe glenoid bone loss on redislocation rates and function after arthroscopic Bankart repair. Methods: A patient cohort who had undergone computed tomography (CT) of glenoid bone loss and arthroscopic Bankart repair over 15 years ago was reviewed. Western Ontario Shoulder Instability (WOSI) score, Single Assessment Numeric Evaluation (SANE) score, redislocation after operation, mechanism of recurrence, and revision details were reviewed. Results: Seventy-five patients were reassessed 17.6 ± 1.9 years following initial surgery. The age at enrolment was 26.8 ± 8.3 years. Twenty-two (29%) patients of the 75 patients had a redislocation on long-term follow-up, though this was not related to glenoid bone loss severity. The impaired functional score was found in patients with initial glenoid bone loss of 7% or more on long-term follow-up: WOSI (physical symptoms): 0.98 ± 2.00 vs 2.25 ± 4.01, p=0.04 and WOSI (total): 0.79 ± 1.43 vs 1.88 ± 3.56, p=0.04. Conclusions: At a mean of 17.5 years following arthroscopic Bankart repair, redislocation occurs in over a quarter of 75 patients, and they are not related to initial glenoid bone loss severity. Impaired functional outcome is apparent in patients with initial glenoid bone loss of >7%, though this impairment does not seem sufficiently severe to warrant an alternative treatment approach.

Investigation on the site of coronal deformities in Hallux valgus.

Hallux valgus (HV) is a common foot deformity that is more prevalent in females, characterised by abnormal adduction of the first metatarsal (MT) and valgus deviation of the phalanx on the transverse plane. Increasing evidence indicates that HV is more than a 2D deformity but a 3D one with rotational malalignment. Pronation deformity is seen during clinical examination for HV patients, but the exact origin of this rotational deformity is still unknown. Some attribute it to first tarsometatarsal (TMT) joint rotation, while others attribute it to intra-metatarsal bony torsion. In addition, the correlation between the rotational and transverse plane deformity is inconclusive. Identifying the origin of the rotational deformity will help surgeons choose the optimal surgical procedure while also enhancing our understanding of the pathophysiology of HV. This study aims to (1) develop an objective method for measuring the first MT torsion and first TMT joint rotation; (2) investigate the exact location of the coronal deformity in HV; (3) investigate the relationship between the severity of deformity on the transverse and coronal planes as well as the correlation between deformity severity and foot function/symptoms in HV. Age-matched females with and without HV were recruited at the Foot and Ankle Clinic of the Department of Orthopaedics and Traumatology. Computed tomography was conducted for all subjects with additional weight-bearing dorsal-plantar X-ray examination for HV subjects. Demographic information of all subjects was recorded, with symptoms and functions related to HV evaluated. The intra-class correlation was used to explore the relationship between deformities on different planes and the deformity severity and functional outcomes, respectively. An Independent t-test was used to compare joint rotation and bone torsion degrees. TMT joint rotation is significantly correlated with foot function. HV patients had more TMT joint rotation but not MT torsion compared to normal controls. No relationship was found between the coronal rotation and the 1,2-intermetatarsal angle (IMA) or Hallux valgus angle (HVA) on the transverse plane. Our results indicate that coronal deformities in HV may originate from TMT joint rotation. In addition, the severity of the TMT joint coronal rotation correlates with worse foot function; thus, multi-plane assessment and examination will be necessary for more precise surgical correction.

Deep learning based ultrasonic visualization of distal humeral cartilage for image-guided therapy: a pilot validation study.

Background: Ultrasound is widely used for image-guided therapy (IGT) in many surgical fields, thanks to its various advantages, such as portability, lack of radiation and real-time imaging. This article presents the first attempt to utilize multiple deep learning algorithms in distal humeral cartilage segmentation for dynamic, volumetric ultrasound images employed in minimally invasive surgery. Methods: The dataset, consisting 5,321 ultrasound images were collected from 12 healthy volunteers. These images were randomly split into training and validation sets in an 8:2 ratio. Based on deep learning algorithms, 9 semantic segmentation networks were developed and trained using our dataset at Southern University of Science and Technology Hospital in September 2022. The performance of the networks was evaluated based on their segmenting accuracy and processing efficiency. Furthermore, these networks were implemented in an IGT system to assess their feasibility in 3-dimentional imaging precision. Results: In 2D segmentation, Medical Transformer (MedT) showed the highest accuracy result with a Dice score of 89.4%, however, the efficiency in processing images was relatively lower at 2.6 frames per second (FPS). In 3D imaging, the average root mean square (RMS) between ultrasound (US)-generated models based on the networks and magnetic resonance imaging (MRI)-generated models was no more than 1.12 mm. Conclusions: The findings of this study indicate the technological feasibility of a novel method for real-time visualization of distal humeral cartilage. The increased precision of ultrasound calibration and segmentation are both important approaches to improve the accuracy of 3D imaging.

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.

Tibial cutting guide (resector) holding pins position and subsequent risks of periprosthetic fracture in unicompartmental knee arthroplasty: a finite element analysis study.

BACKGROUND: Periprosthetic fracture of the tibia after unicompartmental knee arthroplasty has been reported to be associated with excessive pin holes created for stabilization of the cutting guide. However, fractures have also been reported in cases using two pins as in the method suggested by the manufacturer. It is currently unclear whether variations in pinhole positions make a difference in proximal tibial fracture risk. METHODS: Finite element models were constructed using Chinese female bone computed tomography images, with bone cuts made according to the surgical steps of implanting a fixed bearing unicompartmental arthroplasty. Four combinations of pinholes (pins placed more closely to the medial tibial cortex or centrally along the mechanical axis as allowed by the tibial cutting guide) created for tibial cutting guide placement were tested by finite element analyses. Testing loads were applied for simulating standing postures. The maximum von Mises stress on the tibial plateau was evaluated. RESULTS: Pinhole placed close to the medial edge of the proximal tibial plateau is associated with the highest stress (27.67 Mpa) and is more likely to result in medial tibial fracture. On the contrary, pinhole placed along the central axis near the tibial tuberosity has the lowest stress (1.71 Mpa) and reflects lower risk of fracture. CONCLUSION: The present study revealed that placing tibial cutting guide holding pins centrally would lower the risks of periprosthetic fracture of the medial tibial plateau by analyzing the associated stress in various pin hole positions using finite element analysis.

Patient-specific instrumentation (PSI) Referencing High Tibial Osteotomy Technological Transfer and Education: protocol for a double-blind, randomised controlled trial (PROTECTED HTO Trial).

INTRODUCTION: High tibial osteotomy (HTO) is a treatment of choice for active adult with knee osteoarthritis. With advancement in CT imaging with three-dimensional (3D) model reconstruction, virtual planning and 3D printing, patient-specific instrumentation (PSI) in form of cutting jigs is employed to improve surgical accuracy and outcome of HTO. The aim of this randomised controlled trial (RCT) is to explore the surgical outcomes of HTO for the treatment of medial compartment knee osteoarthritis with or without a 3D printed patient-specific jig. METHODS AND ANALYSIS: A double-blind RCT will be conducted with patients and outcome assessors blinded to treatment allocation. This meant that neither the patients nor the outcome assessors would know the actual treatment allocated during the trial. Thirty-six patients with symptomatic medial compartment knee osteoarthritis fulfilling our inclusion criteria will be invited to participate the study. Participants will be randomly allocated to one of two groups (1:1 ratio): operation with 3D printed patient-specific jig or operation without jig. Measurements will be taken before surgery (baseline) and at postoperatively (6, 12 and 24 months). The primary outcome includes radiological accuracy of osteotomy. Secondary outcomes include a change in knee function from baseline to postoperatively as measured by three questionnaires: Knee Society Scores (Knee Scores and Functional Scores), Oxford Knee Scores and pain visual analogue scale (VAS) score. ETHICS AND DISSEMINATION: Ethical approval has been obtained from the Joint Chinese University of Hong Kong - New Territories East Cluster Clinical Research Ethics Committee (CREC no. 2019.050), in accordance with the Declaration of Helsinki. The results will be presented at international scientific meetings and through publications in peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT04000672; Pre-results.