An anatomical and osteometric study of the femoral sulcus angle in adult Malawians

Automating Linear and Angular Measurements for the Hip and Knee After Computed Tomography: Validation of a Three-Stage Deep Learning and Computer Vision-Based Pipeline for Pathoanatomic Assessment

Background

Variability in the bony morphology of pathologic hips/knees is a challenge in automating preoperative computed tomography (CT) scan measurements. With the increasing prevalence of CT for advanced preoperative planning, processing this data represents a critical bottleneck in presurgical planning, research, and development. The purpose of this study was to demonstrate a reproducible and scalable methodology for analyzing CT-based anatomy to process hip and knee anatomy for perioperative planning and execution.

Methods

One hundred patients with preoperative CT scans undergoing total knee arthroplasty for osteoarthritis were processed. A two-step deep learning pipeline of classification and segmentation models was developed that identifies landmark images and then generates contour representations. We utilized an open-source computer vision library to compute measurements. Classification models were assessed by accuracy, precision, and recall. Segmentation models were evaluated using dice and mean Intersection over Union (IOU) metrics. Contour measurements were compared against manual measurements to validate posterior condylar axis angle, sulcus angle, trochlear groove-tibial tuberosity distance, acetabular anteversion, and femoral version.

Results

Classifiers identified landmark images with accuracy of 0.91 and 0.88 for hip and knee models, respectively. Segmentation models demonstrated mean IOU scores above 0.95 with the highest dice coefficient of 0.957 [0.954-0.961] (UNet3+) and the highest mean IOU of 0.965 [0.961-0.969] (Attention U-Net). There were no statistically significant differences for the measurements taken automatically vs manually (P > 0.05). Average time for the pipeline to preprocess (48.65 +/- 4.41 sec), classify/retrieve landmark images (8.36 +/- 3.40 sec), segment images (<1 sec), and obtain measurements was 2.58 (+/- 1.92) minutes.

Conclusions

A fully automated three-stage deep learning and computer vision-based pipeline of classification and segmentation models accurately localized, segmented, and measured landmark hip and knee images for patients undergoing total knee arthroplasty. Incorporation of clinical parameters, like patient-reported outcome measures and instability risk, will be important considerations alongside anatomic parameters.

Acquisition of cadavers for anatomy education and research in Malawi

BACKGROUND

The use of human cadaveric dissection forms an essential part of teaching anatomy to health sciences students in Malawi. Despite worldwide struggles in acquiring sufficient human cadavers for anatomy education, the current recommendations on the best anatomy practices require the use of cadavers exclusively from the body donation programs. The current study aims to describe the sources of cadaveric bodies used for anatomy education in the Malawian context and reflect on the feasibility of using cadavers from the body donation program only.

METHODS

A retrospective audit of the cadaveric records for the 2006-2022 academic years at the Kamuzu University of Health Sciences was done. The perceived challenges when sourcing the cadavers were identified and described based on the authors experiences in Malawi.

RESULTS

The majority (97.3%) of the cadavers used between 2006 and 2022 were unclaimed bodies. The mean age of the cadavers was 45 years. Most (95.6%) of the unclaimed bodies were males. All the unclaimed bodies were sourced from hospitals. The body donation program in Malawi was patronized by populations of European descent only. Strongly held sociocultural and religious beliefs as well as economic reasons were perceived as significant barriers to acquiring cadavers of the indigenous Malawians through the body donation program.

CONCLUSION

Cadavers for anatomy education in Malawi are mainly from unclaimed bodies, similar to other African countries. The authors support the transition from using "mostly unclaimed bodies (and, less often, body donation)" to using "mostly body donation (and, less often, unclaimed bodies)" through awareness campaigns that are targeted to address the prevailing challenges.

Gender-related morphological differences in sulcus angle and condylar height for the femoral trochlea using magnetic resonance imaging

PURPOSE

This study aimed to elucidate the primary differences in trochlear morphology between men and women utilizing three-dimensional magnetic resonance image reconstruction of the femoral trochlea.

METHODS

Differences in anthropometric femoral trochlea data of 975 patients (825 women, 150 men) were evaluated. The following morphological parameters were measured at three flexion angles (15°, 30°, and 45°) of the femoral trochlea: the sulcus angle, condylar height, and the trochlear groove orientation and mediolateral groove position.

RESULTS

The sulcus angle was significantly greater in women than in men at 15° and 45° flexions (P < 0.05). However, there was no gender difference found in the sulcus angle at 30° flexion. Medial and lateral condylar height values were greater in men than in women for the three flexion angles (P < 0.01). The trochlear groove orientation and mediolateral groove position showed no gender-related differences.

CONCLUSIONS

Magnetic resonance image reconstruction demonstrated that measurement of trochlear morphology varied significantly between men and women. This study provides guidelines for the design of a suitable femoral component for total knee arthroplasty, considering gender-specific differences in the Korean population. Biomechanical guidelines for total knee arthroplasty in Korean individuals can be optimized using our finding, so as the risk of patellar dislocation to be decreased. Surgeons should be aware of gender differences in femoral trochlear to optimize choice of implant.

LEVEL OF EVIDENCE

III.