Retuve: Automated Multi-Modality Analysis of Hip Dysplasia with Open Source AI
- URL: http://arxiv.org/abs/2504.06422v1
- Date: Tue, 08 Apr 2025 20:41:21 GMT
- Title: Retuve: Automated Multi-Modality Analysis of Hip Dysplasia with Open Source AI
- Authors: Adam McArthur, Stephanie Wichuk, Stephen Burnside, Andrew Kirby, Alexander Scammon, Damian Sol, Abhilash Hareendranathan, Jacob L. Jaremko,
- Abstract summary: Developmental of the hip (DDH) poses significant diagnostic challenges, hindering timely intervention.<n>To address these limitations, we introduce Retuve, an open-source framework for multi-modality DDH analysis.<n>By adhering to open-source principles, Retuve promotes transparency, collaboration, and accessibility in DDH research.
- Score: 35.088124182314075
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Developmental dysplasia of the hip (DDH) poses significant diagnostic challenges, hindering timely intervention. Current screening methodologies lack standardization, and AI-driven studies suffer from reproducibility issues due to limited data and code availability. To address these limitations, we introduce Retuve, an open-source framework for multi-modality DDH analysis, encompassing both ultrasound (US) and X-ray imaging. Retuve provides a complete and reproducible workflow, offering open datasets comprising expert-annotated US and X-ray images, pre-trained models with training code and weights, and a user-friendly Python Application Programming Interface (API). The framework integrates segmentation and landmark detection models, enabling automated measurement of key diagnostic parameters such as the alpha angle and acetabular index. By adhering to open-source principles, Retuve promotes transparency, collaboration, and accessibility in DDH research. This initiative has the potential to democratize DDH screening, facilitate early diagnosis, and ultimately improve patient outcomes by enabling widespread screening and early intervention. The GitHub repository/code can be found here: https://github.com/radoss-org/retuve
Related papers
- A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis [51.07114445705692]
neurodegenerative diseases (NDs) traditionally require extensive healthcare resources and human effort for medical diagnosis and monitoring.<n>As a crucial disease-related motor symptom, human gait can be exploited to characterize different NDs.<n>The current advances in artificial intelligence (AI) models enable automatic gait analysis for NDs identification and classification.
arXiv Detail & Related papers (2024-05-21T06:44:40Z) - 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 Reinforcement Learning Framework for Thoracic Diseases
Classification via Prior Knowledge Guidance [49.87607548975686]
The scarcity of labeled data for related diseases poses a huge challenge to an accurate diagnosis.
We propose a novel deep reinforcement learning framework, which introduces prior knowledge to direct the learning of diagnostic agents.
Our approach's performance was demonstrated using the well-known NIHX-ray 14 and CheXpert datasets.
arXiv Detail & Related papers (2023-06-02T01:46:31Z) - Extracting Diagnosis Pathways from Electronic Health Records Using Deep
Reinforcement Learning [2.0191844627740254]
We aim to learn the optimal sequence of actions to perform in order to obtain a correct diagnosis from electronic health records.
We apply various deep reinforcement learning algorithms to this task and experiment on a synthetic but realistic dataset to differentially diagnose anemia.
arXiv Detail & Related papers (2023-05-10T16:36:54Z) - Scalable Online Disease Diagnosis via Multi-Model-Fused Actor-Critic
Reinforcement Learning [9.274138493400436]
For those seeking healthcare advice online, AI based dialogue agents capable of interacting with patients to perform automatic disease diagnosis are a viable option.
This can be formulated as a problem of sequential feature (symptom) selection and classification for which reinforcement learning (RL) approaches have been proposed as a natural solution.
We propose a Multi-Model-Fused Actor-Critic (MMF-AC) RL framework that consists of a generative actor network and a diagnostic critic network.
arXiv Detail & Related papers (2022-06-08T03:06:16Z) - Generative Residual Attention Network for Disease Detection [51.60842580044539]
We present a novel approach for disease generation in X-rays using a conditional generative adversarial learning.
We generate a corresponding radiology image in a target domain while preserving the identity of the patient.
We then use the generated X-ray image in the target domain to augment our training to improve the detection performance.
arXiv Detail & Related papers (2021-10-25T14:15:57Z) - The pitfalls of using open data to develop deep learning solutions for
COVID-19 detection in chest X-rays [64.02097860085202]
Deep learning models have been developed to identify COVID-19 from chest X-rays.
Results have been exceptional when training and testing on open-source data.
Data analysis and model evaluations show that the popular open-source dataset COVIDx is not representative of the real clinical problem.
arXiv Detail & Related papers (2021-09-14T10:59:11Z) - Learning from Subjective Ratings Using Auto-Decoded Deep Latent
Embeddings [23.777855250882244]
Managing subjectivity in labels is a fundamental problem in medical imaging analysis.
We introduce auto-decoded deep latent embeddings (ADDLE)
ADDLE explicitly models the tendencies of each rater using an auto-decoder framework.
arXiv Detail & Related papers (2021-04-12T15:40:42Z) - Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for
Thoracic Disease Identification [83.6017225363714]
deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance.
For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming.
In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
arXiv Detail & Related papers (2021-02-26T02:29:30Z) - Evaluating the Clinical Realism of Synthetic Chest X-Rays Generated
Using Progressively Growing GANs [0.0]
Chest x-rays are a vital tool in the workup of many patients.
There is an ever pressing need for greater quantities of labelled data to develop new diagnostic tools.
Previous work has sought to address these concerns by creating class-specific GANs that synthesise images to augment training data.
arXiv Detail & Related papers (2020-10-07T11:47:22Z) - Learning Invariant Feature Representation to Improve Generalization
across Chest X-ray Datasets [55.06983249986729]
We show that a deep learning model performing well when tested on the same dataset as training data starts to perform poorly when it is tested on a dataset from a different source.
By employing an adversarial training strategy, we show that a network can be forced to learn a source-invariant representation.
arXiv Detail & Related papers (2020-08-04T07:41:15Z) - A Deep Learning Pipeline for Patient Diagnosis Prediction Using
Electronic Health Records [0.5672132510411464]
We develop and publish a Python package to transform public health dataset into easy to access universal format.
We propose two novel model architectures to predict multiple diagnoses simultaneously.
Both models can predict multiple diagnoses simultaneously with high accuracy.
arXiv Detail & Related papers (2020-06-23T14:58:58Z)
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.