A Deep Learning Tool for Automated Radiographic Measurement of Acetabular Component Inclination and Version After Total Hip Arthroplasty

Rouzrokh, Pouria; Wyles, Cody C.; Philbrick, Kenneth A.; Ramazanian, Taghi; Weston, Alexander D.; Cai, Jason; Taunton, Michael J.; Lewallen, David G.; Berry, Daniel J.; Erickson, Bradley J.; Kremers, Hilal Maradit

doi:10.1016/j.arth.2021.02.026

Cited by 71 publications

(62 citation statements)

References 19 publications

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“…Through analysis of large databases, machine learning can decipher the complex interactions between variables and generate algorithms capable of outcome prediction. Often, the result is accuracy that is comparable to or better than the prediction of experts in the field [ 5 , 8 , 23 , 25 , 26 , 29 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

Machine learning algorithm to predict anterior cruciate ligament revision demonstrates external validity

Martin

Wastvedt

Pareek

et al. 2022

Knee Surg Sports Traumatol Arthrosc

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Purpose External validation of machine learning predictive models is achieved through evaluation of model performance on different groups of patients than were used for algorithm development. This important step is uncommonly performed, inhibiting clinical translation of newly developed models. Machine learning analysis of the Norwegian Knee Ligament Register (NKLR) recently led to the development of a tool capable of estimating the risk of anterior cruciate ligament (ACL) revision (https://swastvedt.shinyapps.io/calculator_rev/). The purpose of this study was to determine the external validity of the NKLR model by assessing algorithm performance when applied to patients from the Danish Knee Ligament Registry (DKLR). Methods The primary outcome measure of the NKLR model was probability of revision ACL reconstruction within 1, 2, and/or 5 years. For external validation, all DKLR patients with complete data for the five variables required for NKLR prediction were included. The five variables included graft choice, femur fixation device, KOOS QOL score at surgery, years from injury to surgery, and age at surgery. Predicted revision probabilities were calculated for all DKLR patients. The model performance was assessed using the same metrics as the NKLR study: concordance and calibration. Results In total, 10,922 DKLR patients were included for analysis. Average follow-up time or time-to-revision was 8.4 (± 4.3) years and overall revision rate was 6.9%. Surgical technique trends (i.e., graft choice and fixation devices) and injury characteristics (i.e., concomitant meniscus and cartilage pathology) were dissimilar between registries. The model produced similar concordance when applied to the DKLR population compared to the original NKLR test data (DKLR: 0.68; NKLR: 0.68–0.69). Calibration was poorer for the DKLR population at one and five years post primary surgery but similar to the NKLR at two years. Conclusion The NKLR machine learning algorithm demonstrated similar performance when applied to patients from the DKLR, suggesting that it is valid for application outside of the initial patient population. This represents the first machine learning model for predicting revision ACL reconstruction that has been externally validated. Clinicians can use this in-clinic calculator to estimate revision risk at a patient specific level when discussing outcome expectations pre-operatively. While encouraging, it should be noted that the performance of the model on patients undergoing ACL reconstruction outside of Scandinavia remains unknown. Level of evidence III.

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Section: Introductionmentioning

confidence: 99%

Machine learning algorithm to predict anterior cruciate ligament revision demonstrates external validity

Martin

Wastvedt

Pareek

et al. 2022

Knee Surg Sports Traumatol Arthrosc

View full text Add to dashboard Cite

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“…In addition, Rouzrokh and Pouria et al trained a CNN model with 600 hip anteroposterior and 600 hip lateral X-rays taken after total hip arthroplasty and programmed this model to automatically derive the acetabular component inclination and version. Compared with the ground truth, this model showed a difference of 1.35 • for the inclination and 1.39 • for the anteversion [56].…”

Section: Miscellaneousmentioning

confidence: 86%

“…The model automatically calculated the T4-T12 kyphosis, L1-L5 lordosis, Cobb angle of scoliosis, pelvic incidence, sacral slope and pelvic tilt. Among them, the pelvic tilt showed a difference of 2.7 • compared with the ground truth, whereas the L1-L5 lordosis showed a difference of 11.5 • from the ground truth [56].…”

Section: Miscellaneousmentioning

confidence: 92%

Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future

Lee

Chung

2022

Applied Sciences

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Since its development, deep learning has been quickly incorporated into the field of medicine and has had a profound impact. Since 2017, many studies applying deep learning-based diagnostics in the field of orthopedics have demonstrated outstanding performance. However, most published papers have focused on disease detection or classification, leaving some unsatisfactory reports in areas such as segmentation and prediction. This review introduces research published in the field of orthopedics classified according to disease from the perspective of orthopedic surgeons, and areas of future research are discussed. This paper provides orthopedic surgeons with an overall understanding of artificial intelligence-based image analysis and the information that medical data should be treated with low prejudice, providing developers and researchers with insight into the real-world context in which clinicians are embracing medical artificial intelligence.

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“…These programs may be beneficial for primary or urgent care settings to aid in accurate diagnosis and appropriate orthopaedic referral. Programs have also been developed to automate radiographic measurements such as coronal knee alignment [15] and acetabular component inclination and version [14]. Standardizing these measurements not only offers time savings in the clinical setting, but measurement consistency for future studies.…”

Section: Recent Examples In Orthopaedic Surgery and Sports Medicinementioning

confidence: 99%

Artificial intelligence and machine learning: an introduction for orthopaedic surgeons

Martin

Ley

Pareek

et al. 2021

Knee Surg Sports Traumatol Arthrosc

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The application of artificial intelligence (AI) and machine learning to the field of orthopaedic surgery is rapidly increasing. While this represents an important step in the advancement of our specialty, the concept of AI is rich with statistical jargon and techniques unfamiliar to many clinicians. This knowledge gap may limit the impact and potential of these novel techniques. We aim to narrow this gap in a way that is accessible for all orthopaedic surgeons. With this manuscript, we introduce the concept of AI and machine learning and give examples of how it can impact clinical practice and patient care. Level of evidence VI.

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A Deep Learning Tool for Automated Radiographic Measurement of Acetabular Component Inclination and Version After Total Hip Arthroplasty

Cited by 71 publications

References 19 publications

Machine learning algorithm to predict anterior cruciate ligament revision demonstrates external validity

Machine learning algorithm to predict anterior cruciate ligament revision demonstrates external validity

Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future

Artificial intelligence and machine learning: an introduction for orthopaedic surgeons

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