ArticleKorean journal of radiology2021
Diagnostic Performance of a New Convolutional Neural Network Algorithm for Detecting Developmental Dysplasia of the Hip on Anteroposterior Radiographs.
Article in Korean journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it, 39 citations in OpenAlex.
- Artificial intelligence in the diagnostic imaging of developmental dysplasia of the hip: a systematic review.EFORT open reviews · 2026Pooled it
- Artificial Intelligence to Detect Developmental Dysplasia of Hip: A Systematic Review.Journal of paediatrics and child health · 2025Pooled it
- The diagnostic value of artificial intelligence-assisted imaging for developmental dysplasia of the hip: a systematic review and meta-analysis.Journal of orthopaedic surgery and research · 2024Pooled it
- Development and Application of Automated Preoperative Planning System for Developmental Dysplasia of the Hip via Keypoint Selective Integration.Journal of imaging informatics in medicine · 2026Article
- Assessing deep learning accuracy in the measurement of radiographic parameters in pediatric hip X-rays.BMC medical imaging · 2025Article
- Current and Emerging Applications of Artificial Intelligence in Medical Imaging for Paediatric Hip Disorders-A Scoping Review.Children (Basel, Switzerland) · 2025Review
- Deep Learning-Based Automatic Diagnosis System for Developmental Dysplasia of the Hip.IEEE journal of translational engineering in health and medicine · 2025Article
- Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade.Skeletal radiology · 2024Review
- Radiographic Findings Associated With Mild Hip Dysplasia in 3869 Patients Using a Deep Learning Measurement Tool.Arthroplasty today · 2024Article
- Survey on Value Elements Provided by Artificial Intelligence and Their Eligibility for Insurance Coverage With an Emphasis on Patient-Centered Outcomes.Korean journal of radiology · 2024Article
- Enhancing YOLO5 for the Assessment of Irregular Pelvic Radiographs with Multimodal Information.Journal of imaging informatics in medicine · 2024Article
- Reporting Quality of Research Studies on AI Applications in Medical Images According to the CLAIM Guidelines in a Radiology Journal With a Strong Prominence in Asia.Korean journal of radiology · 2023Review
- Assessing Acetabular Index Angle in Infants: A Deep Learning-Based Novel Approach.Journal of imaging · 2023Article
- Automatic detection of early osteonecrosis of the femoral head from various hip pathologies using deep convolutional neural network: a multi-centre study.International orthopaedics · 2023Article
- Diagnostic accuracy of a deep learning model using YOLOv5 for detecting developmental dysplasia of the hip on radiography images.Scientific reports · 2023Article
- Clinical thought-based software for diagnosing developmental dysplasia of the hip on pediatric pelvic radiographs.Frontiers in pediatrics · 2023Article
- Diagnosis of nasal bone fractures on plain radiographs via convolutional neural networks.Scientific reports · 2022Article
- A Deep Learning Algorithm for Radiographic Measurements of the Hip in Adults-A Reliability and Agreement Study.Diagnostics (Basel, Switzerland) · 2022Article
- A Deep-Learning Aided Diagnostic System in Assessing Developmental Dysplasia of the Hip on Pediatric Pelvic Radiographs.Frontiers in pediatrics · 2021Article
Corrections and comments
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Authors and funding
12 authors at 5 institutions in 1 country.
Funding
Abstract
objectiveTo evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs. MATERIALS AND
methodsOf 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience.
resultsThe area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988-0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618-0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (
conclusionThe proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.
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