Automated Analysis of Alignment in Long-Leg Radiographs by Using a                     Fully Automated Support System Based on Artificial Intelligence

Schock, Justus; Truhn, Daniel; Abrar, D; Merhof, Dorit; Conrad, Stefan; Post, Manuel; Mittelstrass, Felix; Kuhl, Christiane K.; Nebelung, Sven

doi:10.1148/ryai.2020200198

Cited by 52 publications

(38 citation statements)

References 29 publications

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“…However, the high temporal resolution resulted in more than 10 images being acquired per second, which rendered manual segmentation and evaluation impractical, even in controlled research contexts, and necessitated further automatization. As suggested by earlier studies that indicated the potential of CNNs in the automated and reliable segmentation of clinical MRI datasets in recent years [27,28,45,46], this study confirmed the high accuracy and reliability of CNN-based semantic segmentations in the context of dynamic wrist imaging.…”

Section: Discussionsupporting

confidence: 86%

“…Consequently, there is a clear need to automatize carpal bone segmentations (that allow subsequent algorithm-based configurational measures to be derived) to position real-time MRI as a clinically useful technique. Convolutional neural networks (CNNs) lend themselves to such automatic segmentation tasks and have been applied in various medical fields by providing automatic segmentations of different entities and structures [22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Post-Processing of Real-Time MRI to Assess and Quantify Dynamic Wrist Movement in Health and Disease

et al. 2021

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While morphologic magnetic resonance imaging (MRI) is the imaging modality of choice for the evaluation of ligamentous wrist injuries, it is merely static and incapable of diagnosing dynamic wrist instability. Based on real-time MRI and algorithm-based image post-processing in terms of convolutional neural networks (CNNs), this study aims to develop and validate an automatic technique to quantify wrist movement. A total of 56 bilateral wrists (28 healthy volunteers) were imaged during continuous and alternating maximum ulnar and radial abduction. Following CNN-based automatic segmentations of carpal bone contours, scapholunate and lunotriquetral gap widths were quantified based on dedicated algorithms and as a function of wrist position. Automatic segmentations were in excellent agreement with manual reference segmentations performed by two radiologists as indicated by Dice similarity coefficients of 0.96 ± 0.02 and consistent and unskewed Bland–Altman plots. Clinical applicability of the framework was assessed in a patient with diagnosed scapholunate ligament injury. Considerable increases in scapholunate gap widths across the range-of-motion were found. In conclusion, the combination of real-time wrist MRI and the present framework provides a powerful diagnostic tool for dynamic assessment of wrist function and, if confirmed in clinical trials, dynamic carpal instability that may elude static assessment using clinical-standard imaging modalities.

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Section: Discussionsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Post-Processing of Real-Time MRI to Assess and Quantify Dynamic Wrist Movement in Health and Disease

et al. 2021

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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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“…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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Automated Analysis of Alignment in Long-Leg Radiographs by Using a Fully Automated Support System Based on Artificial Intelligence

Cited by 52 publications

References 29 publications

Deep Learning-Based Post-Processing of Real-Time MRI to Assess and Quantify Dynamic Wrist Movement in Health and Disease

Deep Learning-Based Post-Processing of Real-Time MRI to Assess and Quantify Dynamic Wrist Movement in Health and Disease

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

Artificial intelligence and machine learning: an introduction for orthopaedic surgeons

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