The accuracy of digital templating in cementless total hip arthroplasty in dysplastic hips

THA-Net: A Deep Learning Solution for Next-Generation Templating and Patient-specific Surgical Execution

BACKGROUND

This study introduces THA-Net, a deep learning inpainting algorithm for simulating postoperative total hip arthroplasty (THA) radiographs from a single preoperative pelvis radiograph input, while being able to generate predictions either unconditionally (algorithm chooses implants) or conditionally (surgeon chooses implants).

METHODS

The THA-Net is a deep learning algorithm which receives an input preoperative radiograph and subsequently replaces the target hip joint with THA implants to generate a synthetic yet realistic postoperative radiograph. We trained THA-Net on 356,305 pairs of radiographs from 14,357 patients from a single institution's total joint registry and evaluated the validity (quality of surgical execution) and realism (ability to differentiate real and synthetic radiographs) of its outputs against both human-based and software-based criteria.

RESULTS

The surgical validity of synthetic postoperative radiographs was significantly higher than their real counterparts (mean difference: 0.8 to 1.1 points on 10-point Likert scale, P < .001), but they were not able to be differentiated in terms of realism in blinded expert review. Synthetic images showed excellent validity and realism when analyzed with already validated deep learning models.

CONCLUSION

We developed a THA next-generation templating tool that can generate synthetic radiographs graded higher on ultimate surgical execution than real radiographs from training data. Further refinement of this tool may potentiate patient-specific surgical planning and enable technologies such as robotics, navigation, and augmented reality (an online demo of THA-Net is available at: https://demo.osail.ai/tha_net).

Digital health in musculoskeletal care: where are we heading?

BMC Musculoskeletal Disorders launched a Collection on digital health to get a sense of where the wind is blowing, and what impact these technologies are and will have on musculoskeletal medicine. This editorial summarizes findings and focuses on some key topics, which are valuable as digital health establishes itself in patient care. Elements discussed are digital tools for the diagnosis, prognosis and evaluation of rheumatic and musculoskeletal diseases, coupled together with advances in methodologies to analyse health records and imaging. Moreover, the acceptability and validity of these digital advances is discussed. In sum, this editorial and the papers presented in this article collection on Digital health in musculoskeletal care will give the interested reader both a glance towards which future we are heading, and which new challenges these advances bring.

Determining the accuracy of preoperative total hip replacement 2D templating using the mediCAD® software

Background

Templating is a preoperative planning procedure that improves the efficiency of the surgical process and reduces postoperative complications of total hip arthroplasty (THA) by improving the precision of prediction of prosthetic implant size. This study aimed to evaluate the accuracy of the preoperative cup and stem size digital 2D templating of THA with mediCAD^® software and find the factors that influence the accuracy, such as indication for surgery, patients’ demographics, implant brand, and the assessors’ grade of education.

Methods

We retrospectively retrieved 420 patient template images of all patients who underwent THA between March 2018 and March 2021. Templating of all included images was processed using mediCAD^® software a day before surgery by a newcomer physician to hip arthroplasty course (PGY-2 orthopedic resident or hip surgery fellow). Preoperative templating cup and stem sizes were compared with the actual inserted implant sizes.

Result

After excluding ineligible patients, this study included 391 patients, 193 (49.4%) males and 198 (50.6%) females with a mean age of 43.3 ± 14.9. The average cup sizes predicted before and after surgery were 52.12 ± 14.28 and 52.21 ± 15.05 respectively, and the mean delta cup size (before and after surgery) was 2.79 ± 2.94. The delta stem size before and after surgery has a mean value of 1.53 ± 1.49. The acetabular cup components, measured within ± 0, ± 1, and ± 2 sizes, were 28.9%, 63.9%, 83.1% accurate, respectively. The femoral stem design component measured within ± 0, ± 1, and ± 2 sizes were 27.2%, 61.0%, 78.6% accurate, respectively. Wagner Cone^® stem brand, DDH patients, and females showed significantly higher accuracy of stem size templating. Revision THA has the lowest accuracy in terms of cup size templating. The compression of accuracy rate between resident and fellow revealed no significant differences. Also, no significant difference was detected between the accuracy of templating performed in the first months with the second months of the arthroplasty course period.

Conclusion

Our study showed that under mentioned condition, templating using mediCAD^® has acceptable accuracy in predicting the sizes of femoral and acetabular components in THA patients. Digital software like mediCAD^® remains favorable because of the short learning curve, user-friendly features, and low-cost maintenance, leading to level-up patient care and THA efficacy. Further studies are necessary for clarifying the role of the assessor’s experience and expertise in THA preoperative templating.

Level of evidence

Level III (retrospective observational study).