Development and evaluation of deep-learning measurement of leg length discrepancy: bilateral iliac crest height difference measurement

Kim, Min Jong; Choi, Young Hun; Lee, Seul Bi; Cho, Yeon Jin; Lee, Seul Bi; Shin, Chang Ho; Shin, Su-Mi; Cheon, Jung Eun

doi:10.1007/s00247-022-05499-0

Cited by 5 publications

(1 citation statement)

References 25 publications

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“…In the following, some applications of deep learning with DR images reported by other researchers are described. Kim, Min Jong et al [20] used a deep-learning model, U-Net, with convolutional networks for biomedical image segmentation in combination with DR images to measure differences in human leg length. The Pearson correlation coefficient of the final result ranged from 0.880 to 0.996.…”

Section: Related Workmentioning

confidence: 99%

Tibia-YOLO: An AssistedDetection System Combined with Industrial CT Equipment for Leg Diseases in Broilers

Sun,

Yang,

Chen

et al. 2024

Applied Sciences

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With the continuous improvement of broiler production performance, the frequent occurrence of leg problems has caused serious economic losses in many factories. In order to more efficiently detect and prevent broiler leg diseases, we propose an auxiliary detection system for broiler leg diseases based on deep learning. The system is divided into two parts. First, a digital radiography (DR) image of a broiler is taken through industrial computed tomography (CT), and then the improved deep-learning network Tibia-YOLO is used to detect the tibia; the detected results are then extracted and measured. Our improved Tibia-YOLO network uses the Content-Aware ReAssembly of Features (CARAFE) upsampling operator to avoid checkerboard artifacts and increase the generalization capabilities. Efficient multi-scale attention (EMA) and parallel network attention (ParNet) were added to the Tibia dataset at multiple scales (COCO2016), and there were improvements when testing on the three VOC2012 datasets. The mean average precision of tibia detection reached 90.8%, and the root mean square error (RMSE) for the tibia length was 3.37 mm.

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Section: Related Workmentioning

confidence: 99%

Tibia-YOLO: An AssistedDetection System Combined with Industrial CT Equipment for Leg Diseases in Broilers

Sun,

Yang,

Chen

et al. 2024

Applied Sciences

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Diagnostic performance of deep learning for leg length measurements on radiographs in leg length discrepancy: A systematic review

Lezak,

Pruneski,

Oeding

et al. 2024

J. exp. orthop.

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PurposeTo systematically review the literature regarding machine learning in leg length discrepancy (LLD) and to provide insight into the most relevant manuscripts on this topic in order to highlight the importance and future clinical implications of machine learning in the diagnosis and treatment of LLD.MethodsA systematic electronic search was conducted using PubMed, OVID/Medline and Cochrane libraries in accordance with Preferred Reporting Items for Systematic Review and Meta‐Analysis guidelines. Two observers independently screened the abstracts and titles of potential articles.ResultsA total of six studies were identified in the search. All measurements were calculated using standardized anterior‐posterior long‐leg radiographs. Five (83.3%) of the studies used measurements of the femoral length, tibial length and leg length to assess LLD, whereas one (16.6%) study used the iliac crest height difference to quantify LLD. The deep learning models showed excellent reliability in predicting all length measurements with intraclass correlation coefficients ranging from 0.98 to 1.0 and mean absolute error (MAE) values ranging from 0.11 to 0.45 cm. Three studies reported measurements of LLD, and the convolutional neural network model showed the lowest MAE of 0.13 cm in predicting LLD.ConclusionsMachine learning models are effective and efficient in determining LLD. Implementation of these models may reduce cost, improve efficiency and lead to better overall patient outcomes.Clinical RelevanceThis review highlights the potential of deep learning (DL) algorithms for accurate and reliable measurement of lower limb length and leg length discrepancy (LLD) on long‐leg radiographs. The reported mean absolute error and intraclass correlation coefficient values indicate that the performance of the DL models was comparable to that of radiologists, suggesting that DL‐based assessments could potentially be used to automate the measurement of lower limb length and LLD in clinical practice.Level of EvidenceLevel IV.

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Multicenter survey about leg length discrepancy and total hip arthroplasty: preoperative and intraoperative management

Stimolo,

Lo Giudice,

Matassi

et al. 2024

Musculoskelet Surg

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Background We created a multicenter survey for Italian orthopedic surgeons on how they approach leg length discrepancy (LLD) when dealing with primary total hip arthroplasty. Aim of the study was to show how surgeons manage LLD and follow the literature recommendations during clinical practice. Methods The survey was composed of 25 questions divided into four sections: 1—surgeon’s profile, 2—preoperative and 3—intraoperative evaluation, and 4—postoperative management. In this paper, we report results to answer Sects. 1 and 2. Absolute and relative frequencies of answers to Sects. 2 and 3 are reported. We divided the participants in subgroups based on the “surgeon’s profile” and evaluated difference in the answers given. Results Absolute and relative frequencies demonstrate low agreement among participants in all phases of LLD management. We demonstrated a statistically significant difference based on the surgeon’s profile regarding these questions: radiographic measure of LLD depending on working experience, p = 0.008; digital planning based on surgeons’ age, p < 0.001, and workplace, p = 0.026; intraoperative anatomical landmarks based on numbers of procedures per year, p = 0.020; and use of intraoperative X-rays based on working experience, p = 0.002. Conclusions LLD is a debated topic with no definitive recommendations. Many decisions still depend on tradition and surgeons’ preference.

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Development and evaluation of deep-learning measurement of leg length discrepancy: bilateral iliac crest height difference measurement

Cited by 5 publications

References 25 publications

Tibia-YOLO: An AssistedDetection System Combined with Industrial CT Equipment for Leg Diseases in Broilers

Tibia-YOLO: An AssistedDetection System Combined with Industrial CT Equipment for Leg Diseases in Broilers

Diagnostic performance of deep learning for leg length measurements on radiographs in leg length discrepancy: A systematic review

Multicenter survey about leg length discrepancy and total hip arthroplasty: preoperative and intraoperative management

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