Deep Learning-Based Automatic Diagnosis System for Developmental Dysplasia of the Hip

• URL: http://arxiv.org/abs/2209.03440v1
• Date: Wed, 7 Sep 2022 19:50:30 GMT
• Title: Deep Learning-Based Automatic Diagnosis System for Developmental Dysplasia of the Hip
• Authors: Yang Li, Leo Yan Li-Han, Hua Tian
• Abstract summary: This study proposes a deep learning-based system that automatically detects 14 keypoints from a radiograph. It measures three anatomical angles (center-edge, T"onnis, and Sharp angles), and classifies DDH hips as grades I-IV based on the Crowe criteria.
• Score: 5.673030999857323
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As the first-line diagnostic imaging modality, radiography plays an essential role in the early detection of developmental dysplasia of the hip (DDH). Clinically, the diagnosis of DDH relies on manual measurements and subjective evaluation of different anatomical features from pelvic radiographs. This process is inefficient and error-prone and requires years of clinical experience. In this study, we propose a deep learning-based system that automatically detects 14 keypoints from a radiograph, measures three anatomical angles (center-edge, T\"onnis, and Sharp angles), and classifies DDH hips as grades I-IV based on the Crowe criteria. Moreover, a novel data-driven scoring system is proposed to quantitatively integrate the information from the three angles for DDH diagnosis. The proposed keypoint detection model achieved a mean (95% confidence interval [CI]) average precision of 0.807 (0.804-0.810). The mean (95% CI) intraclass correlation coefficients between the center-edge, Tonnis, and Sharp angles measured by the proposed model and the ground-truth were 0.957 (0.952-0.962), 0.947 (0.941-0.953), and 0.953 (0.947-0.960), respectively, which were significantly higher than those of experienced orthopedic surgeons (p<0.0001). In addition, the mean (95% CI) test diagnostic agreement (Cohen's kappa) obtained using the proposed scoring system was 0.84 (0.83-0.85), which was significantly higher than those obtained from diagnostic criteria for individual angle (0.76 [0.75-0.77]) and orthopedists (0.71 [0.63-0.79]). To the best of our knowledge, this is the first study for objective DDH diagnosis by leveraging deep learning keypoint detection and integrating different anatomical measurements, which can provide reliable and explainable support for clinical decision-making.

Related papers

• A Hybrid Artificial Intelligence System for Automated EEG Background Analysis and Report Generation [0.1874930567916036]
  This study proposes an innovative hybrid artificial intelligence (AI) system for automatic interpretation of EEG background activity and report generation. The system combines deep learning models for posterior dominant rhythm (PDR) prediction, unsupervised artifact removal, and expert-designed algorithms for abnormality detection. The AI system significantly outperformed neurologists in detecting generalized background slowing and improved focal abnormality detection.
  arXiv  Detail & Related papers  (2024-11-15T01:49:17Z)
• Deep Radiomics Detection of Clinically Significant Prostate Cancer on Multicenter MRI: Initial Comparison to PI-RADS Assessment [0.0]
  This study analyzed biparametric (T2W and DW) prostate MRI sequences of 615 patients (mean age, 63.1 +/- 7 years) from four datasets acquired between 2010 and 2020. Deep radiomics machine learning model achieved comparable performance to PI-RADS assessment in csPCa detection at the patient-level but not at the lesion-level.
  arXiv  Detail & Related papers  (2024-10-21T17:41:58Z)
• AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans [43.06293430764841]
  This study presents an innovative method for Alzheimer's disease diagnosis using 3D MRI designed to enhance the explainability of model decisions. Our approach adopts a soft attention mechanism, enabling 2D CNNs to extract volumetric representations. With voxel-level precision, our method identified which specific areas are being paid attention to, identifying these predominant brain regions.
  arXiv  Detail & Related papers  (2024-07-02T16:44:00Z)
• A Deep Learning Model for Coronary Artery Segmentation and Quantitative Stenosis Detection in Angiographic Images [2.6879908098704544]
  We propose a novel deep learning-based method for the automatic segmentation of coronary arteries in angiographic images. The model combines the MedSAM and VM-UNet architectures to achieve high-performance results. The stenosis detection algorithm achieved a true positive rate (TPR) of 0.5867 and a positive predictive value (PPV) of 0.5911.
  arXiv  Detail & Related papers  (2024-06-01T16:45:33Z)
• Incorporating Anatomical Awareness for Enhanced Generalizability and Progression Prediction in Deep Learning-Based Radiographic Sacroiliitis Detection [0.8248058061511542]
  The aim of this study was to examine whether incorporating anatomical awareness into a deep learning model can improve generalizability and enable prediction of disease progression. The performance of the models was compared using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.
  arXiv  Detail & Related papers  (2024-05-12T20:02:25Z)
• Detection of subclinical atherosclerosis by image-based deep learning on chest x-ray [86.38767955626179]
  Deep-learning algorithm to predict coronary artery calcium (CAC) score was developed on 460 chest x-ray. The diagnostic accuracy of the AICAC model assessed by the area under the curve (AUC) was the primary outcome.
  arXiv  Detail & Related papers  (2024-03-27T16:56:14Z)
• DDxT: Deep Generative Transformer Models for Differential Diagnosis [51.25660111437394]
  We show that a generative approach trained with simpler supervised and self-supervised learning signals can achieve superior results on the current benchmark. The proposed Transformer-based generative network, named DDxT, autoregressively produces a set of possible pathologies, i.e., DDx, and predicts the actual pathology using a neural network.
  arXiv  Detail & Related papers  (2023-12-02T22:57:25Z)
• Deep learning in computed tomography pulmonary angiography imaging: a dual-pronged approach for pulmonary embolism detection [0.0]
  The aim of this study is to leverage deep learning techniques to enhance the Computer Assisted Diagnosis (CAD) of Pulmonary Embolism (PE) Our classification system includes an Attention-Guided Convolutional Neural Network (AG-CNN) that uses local context by employing an attention mechanism. AG-CNN achieves robust performance on the FUMPE dataset, achieving an AUROC of 0.927, sensitivity of 0.862, specificity of 0.879, and an F1-score of 0.805 with the Inception-v3 backbone architecture.
  arXiv  Detail & Related papers  (2023-11-09T08:23:44Z)
• Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies [62.840338941861134]
  We propose to leverage transfer learning from large datasets annotated by radiologists, to predict the histological score available on a small annex dataset. We compare different pretraining methods, namely weakly-supervised and self-supervised ones, to improve the prediction of the cirrhosis. This method outperforms the baseline classification of the METAVIR score, reaching an AUC of 0.84 and a balanced accuracy of 0.75.
  arXiv  Detail & Related papers  (2023-02-16T17:06:23Z)
• Deep-Learning Tool for Early Identifying Non-Traumatic Intracranial Hemorrhage Etiology based on CT Scan [40.51754649947294]
  The deep learning model was developed with 1868 eligible NCCT scans with non-traumatic ICH collected between January 2011 and April 2018. The model's diagnostic performance was compared with clinicians's performance. The clinicians achieve significant improvements in the sensitivity, specificity, and accuracy of diagnoses of certain hemorrhage etiologies with proposed system augmentation.
  arXiv  Detail & Related papers  (2023-02-02T08:45:17Z)
• A deep learning pipeline for localization, differentiation, and uncertainty estimation of liver lesions using multi-phasic and multi-sequence MRI [15.078841623264543]
  We propose a fully-automatic computer-aided diagnosis (CAD) solution for liver lesion characterization. We enroll 400 patients who had either liver resection or a biopsy and was diagnosed with either liver carcinoma (HCC), intrahepatic cholangiocarcinoma, or secondary metastasis. We propose a fully-automatic deep CAD pipeline that localizes lesions from 3D MRI studies using key-slice parsing and provides a confidence measure for its diagnoses.
  arXiv  Detail & Related papers  (2021-10-17T13:19:00Z)
• Systematic Clinical Evaluation of A Deep Learning Method for Medical Image Segmentation: Radiosurgery Application [48.89674088331313]
  We systematically evaluate a Deep Learning (DL) method in a 3D medical image segmentation task. Our method is integrated into the radiosurgery treatment process and directly impacts the clinical workflow.
  arXiv  Detail & Related papers  (2021-08-21T16:15:40Z)
• Ensemble machine learning approach for screening of coronary heart disease based on echocardiography and risk factors [19.076443235356873]
  We develop a machine learning approach that integrates a number of popular classification methods together by model stacking. We improve the CHD classification accuracy from around 70% to 87.7% on the testing set.
  arXiv  Detail & Related papers  (2021-05-20T11:04:58Z)
• Lung Cancer Diagnosis Using Deep Attention Based on Multiple Instance Learning and Radiomics [13.028105771052376]
  We treat lung cancer diagnosis as a multiple instance learning (MIL) problem in order to better reflect the diagnosis process in the clinical setting. We chose radiomics as the source of input features and deep attention-based MIL as the classification algorithm. The results show that our method can achieve a mean accuracy of 0.807 with a standard error of the mean (SEM) of 0.069, a recall of 0.870 (SEM 0.061), a positive predictive value of 0.928 (SEM 0.078), a negative predictive value of 0.591 (SEM 0.155) and an area under the curve (AUC) of 0.842 (
  arXiv  Detail & Related papers  (2021-04-29T21:04:02Z)
• Identification of Ischemic Heart Disease by using machine learning technique based on parameters measuring Heart Rate Variability [50.591267188664666]
  In this study, 18 non-invasive features (age, gender, left ventricular ejection fraction and 15 obtained from HRV) of 243 subjects were used to train and validate a series of several ANN. The best result was obtained using 7 input parameters and 7 hidden nodes with an accuracy of 98.9% and 82% for the training and validation dataset.
  arXiv  Detail & Related papers  (2020-10-29T19:14:41Z)
• iPhantom: a framework for automated creation of individualized computational phantoms and its application to CT organ dosimetry [58.943644554192936]
  This study aims to develop and validate a novel framework, iPhantom, for automated creation of patient-specific phantoms or digital-twins. The framework is applied to assess radiation dose to radiosensitive organs in CT imaging of individual patients. iPhantom precisely predicted all organ locations with good accuracy of Dice Similarity Coefficients (DSC) >0.6 for anchor organs and DSC of 0.3-0.9 for all other organs.
  arXiv  Detail & Related papers  (2020-08-20T01:50:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.