Can Deep Learning Using Weight Bearing Knee Anterio-Posterior Radiograph Alone Replace a Whole-Leg Radiograph in the Interpretation of Weight Bearing Line Ratio?

Moon, Hyun Doo; Choi, Han Gyeol; Lee, Kyong Joon; Choi, Dong Jun; Yoo, Hyun Jin; Lee, Yong Seuk

doi:10.3390/jcm10081772

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…The inter- and intra-observer reliabilities of the measurements were analyzed using ICC, with ICC < 0.40 indicating poor agreement, in the range 0.40–0.75 indicating fair to good (moderate) agreement, and in the range 0.76–1.00 indicating excellent agreement. MAE was used as a measure to determine how well the CNN fit the WBL ratio [ 13 , 14 , 15 ]. MAE is a measure that indicates the difference between the actual labeled WBL ratio by A (AL) using WLR and the WBL ratio predicted by the CNN using simple knee radiographs,

, with

being the estimated WBL ratio of the

th data, and

being the ground-truth WBL ratio of the

th data [ 18 ].…”

Section: Methodsmentioning

confidence: 99%

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Key-Point Detection Algorithm of Deep Learning Can Predict Lower Limb Alignment with Simple Knee Radiographs

Nam

Park

et al. 2023

JCM

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(1) Background: There have been many attempts to predict the weight-bearing line (WBL) ratio using simple knee radiographs. Using a convolutional neural network (CNN), we focused on predicting the WBL ratio quantitatively. (2) Methods: From March 2003 to December 2021, 2410 patients with 4790 knee AP radiographs were randomly selected using stratified random sampling. Our dataset was cropped by four points annotated by a specialist with a 10-pixel margin. The model predicted our interest points, which were both plateau points, i.e., starting WBL point and exit WBL point. The resulting value of the model was analyzed in two ways: pixel units and WBL error values. (3) Results: The mean accuracy (MA) was increased from around 0.5 using a 2-pixel unit to around 0.8 using 6 pixels in both the validation and the test sets. When the tibial plateau length was taken as 100%, the MA was increased from approximately 0.1, using 1%, to approximately 0.5, using 5% in both the validation and the test sets. (4) Conclusions: The DL-based key-point detection algorithm for predicting lower limb alignment through labeling using simple knee AP radiographs demonstrated comparable accuracy to that of the direct measurement using whole leg radiographs. Using this algorithm, the WBL ratio prediction with simple knee AP radiographs could be useful to diagnose lower limb alignment in osteoarthritis patients in primary care.

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, with

being the estimated WBL ratio of the

th data, and

being the ground-truth WBL ratio of the

th data [ 18 ].…”

Section: Methodsmentioning

confidence: 99%

“…However, a limitation of this study was that the prediction was only possible within intervals. Therefore, a quantitative assessment that can predict the WBL ratio and translate it to an accurate point on the tibial plateau may be more intuitive for clinical use [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Key-Point Detection Algorithm of Deep Learning Can Predict Lower Limb Alignment with Simple Knee Radiographs

Nam

Park

et al. 2023

JCM

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“…33 Recent studies have demonstrated deep learning to be faster, as accurate, and more efficient compared with experts with automated measurements in orthopaedic imaging. [28][29][30][31] One of the issues in measuring the PTS is that the process is relatively laborintensive and time-consuming for busy clinicians. This also makes it more difficult to perform research on large numbers of patients that all require manual calculation of the PTS.…”

mentioning

confidence: 99%

Deep Learning Artificial Intelligence Tool for Automated Radiographic Determination of Posterior Tibial Slope in Patients With ACL Injury

Lu,

Pareek,

Yang

et al. 2023

Orthopaedic Journal of Sports Medicine

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Background: An increased posterior tibial slope (PTS) corresponds with an increased risk of graft failure after anterior cruciate ligament (ACL) reconstruction (ACLR). Validated methods of manual PTS measurements are subject to potential interobserver variability and can be inefficient on large datasets. Purpose/Hypothesis: To develop a deep learning artificial intelligence technique for automated PTS measurement from standard lateral knee radiographs. It was hypothesized that this deep learning tool would be able to measure the PTS on a high volume of radiographs expeditiously and that these measurements would be similar to previously validated manual measurements. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: A deep learning U-Net model was developed on a cohort of 300 postoperative short-leg lateral radiographs from patients who underwent ACLR to segment the tibial shaft, tibial joint surface, and tibial tuberosity. The model was trained via a random split after an 80 to 20 train-validation scheme. Masks for training images were manually segmented, and the model was trained for 400 epochs. An image processing pipeline was then deployed to annotate and measure the PTS using the predicted segmentation masks. Finally, the performance of this combined pipeline was compared with human measurements performed by 2 study personnel using a previously validated manual technique for measuring the PTS on short-leg lateral radiographs on an independent test set consisting of both pre- and postoperative images. Results: The U-Net semantic segmentation model achieved a mean Dice similarity coefficient of 0.885 on the validation cohort. The mean difference between the human-made and computer-vision measurements was 1.92° (σ = 2.81° [ P = .24]). Extreme disagreements between the human and machine measurements, as defined by ≥5° differences, occurred <5% of the time. The model was incorporated into a web-based digital application front-end for demonstration purposes, which can measure a single uploaded image in Portable Network Graphics format in a mean time of 5 seconds. Conclusion: We developed an efficient and reliable deep learning computer vision algorithm to automate the PTS measurement on short-leg lateral knee radiographs. This tool, which demonstrated good agreement with human annotations, represents an effective clinical adjunct for measuring the PTS as part of the preoperative assessment of patients with ACL injuries.

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Deciphering Knee Osteoarthritis Diagnostic Features With Explainable Artificial Intelligence: A Systematic Review

Xin Teoh,

Othmani,

Li Goh

et al. 2024

IEEE Access

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Can Deep Learning Using Weight Bearing Knee Anterio-Posterior Radiograph Alone Replace a Whole-Leg Radiograph in the Interpretation of Weight Bearing Line Ratio?

Cited by 4 publications

References 30 publications

Key-Point Detection Algorithm of Deep Learning Can Predict Lower Limb Alignment with Simple Knee Radiographs

Key-Point Detection Algorithm of Deep Learning Can Predict Lower Limb Alignment with Simple Knee Radiographs

Deep Learning Artificial Intelligence Tool for Automated Radiographic Determination of Posterior Tibial Slope in Patients With ACL Injury

Deciphering Knee Osteoarthritis Diagnostic Features With Explainable Artificial Intelligence: A Systematic Review

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