This review explores the significant progress and applications of artificial intelligence (AI) in obstetrics and gynecological MRI, charting its development from foundational algorithmic techniques to deep learning strategies and advanced radiomics. This review features research published over the last few years that has used AI with MRI to identify specific conditions such as uterine leiomyosarcoma, endometrial cancer, cervical cancer, ovarian tumors, and placenta accreta. In addition, it covers studies on the application of AI for segmentation and quality improvement in obstetrics and gynecology MRI. The review also outlines the existing challenges and envisions future directions for AI research in this domain. The growing accessibility of extensive datasets across various institutions and the application of multiparametric MRI are significantly enhancing the accuracy and adaptability of AI. This progress has the potential to enable more accurate and efficient diagnosis, offering opportunities for personalized medicine in the field of obstetrics and gynecology.

This paper presents a novel motion feature guided diffusion model for unpaired video-to-video translation (MFD-V2V), designed to synthesize dynamic, high-contrast cine cardiac magnetic resonance (CMR) from lower-contrast, artifact-prone displacement encoding with stimulated echoes (DENSE) CMR sequences. To achieve this, we first introduce a Latent Temporal Multi-Attention (LTMA) registration network that effectively learns more accurate and consistent cardiac motions from cine CMR image videos. A multi-level motion feature guided diffusion model, equipped with a specialized Spatio-Temporal Motion Encoder (STME) to extract fine-grained motion conditioning, is then developed to improve synthesis quality and fidelity. We evaluate our method, MFD-V2V, on a comprehensive cardiac dataset, demonstrating superior performance over the state-of-the-art in both quantitative metrics and qualitative assessments. Furthermore, we show the benefits of our synthesized cine CMRs improving downstream clinical and analytical tasks, underscoring the broader impact of our approach. Our code is publicly available at https://github.com/SwaksharDeb/MFD-V2V.

Medical digitization has been intensively developed in the last decade, leading to paving the path for computer-aided medical diagnosis research. Thus, anomaly detection based on machine and deep learning techniques has been extensively employed in healthcare applications, such as medical imaging classification and monitoring of patients' vital signs. To effectively leverage digitized medical records for identifying challenges in healthcare, this manuscript presents a smart Clinical Decision Support System (CDSS) dedicated for medical multimodal data automated diagnosis. A smart healthcare system necessitating medical data management and decision-making is proposed. To deliver timely rapid diagnosis, thread-level parallelism (TLP) is utilized for parallel distribution of classification tasks on three edge computing devices, each employing an AI module for on-device AI classifications. In comparison to existing machine and deep learning classification techniques, the proposed multithreaded architecture realizes a hybrid (ML and DL) processing module on each edge node. In this context, the presented edge computing-based parallel architecture captures a high level of parallelism, tailored for dealing with multiple categories of medical records. The cluster of the proposed architecture encompasses three edge computing Raspberry Pi devices and an edge server. Furthermore, lightweight neural networks, such as MobileNet, EfficientNet, and ResNet18, are trained and optimized based on genetic algorithms to provide classification of brain tumor, pneumonia, and colon cancer. Model deployment was conducted based on Python programming, where PyCharm is run on the edge server whereas Thonny is installed on edge nodes. In terms of accuracy, the proposed GA-based optimized ResNet18 for pneumonia diagnosis achieves 93.59% predictive accuracy and reduces the classifier computation complexity by 33.59%, whereas an outstanding accuracy of 99.78% and 100% were achieved with EfficientNet-v2 for brain tumor and colon cancer prediction, respectively, while both models preserving a reduction of 25% in the model's classifier. More importantly, an inference speedup of 28.61% and 29.08% was obtained by implementing parallel 2 DL and 3 DL threads configurations compared to the sequential implementation, respectively. Thus, the proposed multimodal-multithreaded architecture offers promising prospects for comprehensive and accurate anomaly detection of patients' medical imaging and vital signs. To summarize, our proposed architecture contributes to the advancement of healthcare services, aiming to improve patient medical diagnosis and therapy outcomes.

PurposePrecise quantification of myocardial blood flow (MBF) and flow reserve (MFR) in 18F-flurpiridaz PET significantly relies on motion correction (MC). However, the manual frame-by-frame correction leads to significant inter-observer variability, time-consuming, and requires significant experience. We propose a deep learning (DL) framework for automatic MC of 18F-flurpiridaz PET. MethodsThe method employs a 3D ResNet based architecture that takes 3D PET volumes and outputs motion vectors. It was validated using 5-fold cross-validation on data from 32 sites of a Phase III clinical trial (NCT01347710). Manual corrections from two experienced operators served as ground truth, and data augmentation using simulated vectors enhanced training robustness. The study compared the DL approach to both manual and standard non-AI automatic MC methods, assessing agreement and diagnostic accuracy using minimal segmental MBF and MFR. ResultsThe area under the receiver operating characteristic curves (AUC) for significant CAD were comparable between DL-MC MBF, manual-MC MBF from Operators (AUC=0.897,0.892 and 0.889, respectively; p>0.05), standard non-AI automatic MC (AUC=0.877; p>0.05) and significantly higher than No-MC (AUC=0.835; p<0.05). Similar findings were observed with MFR. The 95% confidence limits for agreement with the operator were {+/-}0.49ml/g/min (mean difference = 0.00) for MFR and {+/-}0.24ml/g/min (mean difference = 0.00) for MBF. ConclusionDL-MC is significantly faster but diagnostically comparable to manual-MC. The quantitative results obtained with DL-MC for MBF and MFR are in excellent agreement with those manually corrected by experienced operators compared to standard non-AI automatic MC in patients undergoing 18F-flurpiridaz PET-MPI.

This paper presents a novel motion feature guided diffusion model for unpaired video-to-video translation (MFD-V2V), designed to synthesize dynamic, high-contrast cine cardiac magnetic resonance (CMR) from lower-contrast, artifact-prone displacement encoding with stimulated echoes (DENSE) CMR sequences. To achieve this, we first introduce a Latent Temporal Multi-Attention (LTMA) registration network that effectively learns more accurate and consistent cardiac motions from cine CMR image videos. A multi-level motion feature guided diffusion model, equipped with a specialized Spatio-Temporal Motion Encoder (STME) to extract fine-grained motion conditioning, is then developed to improve synthesis quality and fidelity. We evaluate our method, MFD-V2V, on a comprehensive cardiac dataset, demonstrating superior performance over the state-of-the-art in both quantitative metrics and qualitative assessments. Furthermore, we show the benefits of our synthesized cine CMRs improving downstream clinical and analytical tasks, underscoring the broader impact of our approach. Our code is publicly available at https://github.com/SwaksharDeb/MFD-V2V.

Medical Image Grounding (MIG), which involves localizing specific regions in medical images based on textual descriptions, requires models to not only perceive regions but also deduce spatial relationships of these regions. Existing Vision-Language Models (VLMs) for MIG often rely on Supervised Fine-Tuning (SFT) with large amounts of Chain-of-Thought (CoT) reasoning annotations, which are expensive and time-consuming to acquire. Recently, DeepSeek-R1 demonstrated that Large Language Models (LLMs) can acquire reasoning abilities through Group Relative Policy Optimization (GRPO) without requiring CoT annotations. In this paper, we adapt the GRPO reinforcement learning framework to VLMs for Medical Image Grounding. We propose the Spatial-Semantic Rewarded Group Relative Policy Optimization to train the model without CoT reasoning annotations. Specifically, we introduce Spatial-Semantic Rewards, which combine spatial accuracy reward and semantic consistency reward to provide nuanced feedback for both spatially positive and negative completions. Additionally, we propose to use the Chain-of-Box template, which integrates visual information of referring bounding boxes into the <think> reasoning process, enabling the model to explicitly reason about spatial regions during intermediate steps. Experiments on three datasets MS-CXR, ChestX-ray8, and M3D-RefSeg demonstrate that our method achieves state-of-the-art performance in Medical Image Grounding. Ablation studies further validate the effectiveness of each component in our approach. Code, checkpoints, and datasets are available at https://github.com/bio-mlhui/MedGround-R1

Deep learning models have proven to be effective on medical datasets for accurate diagnostic predictions from images. However, medical datasets often contain noisy, mislabeled, or poorly generalizable images, particularly for edge cases and anomalous outcomes. Additionally, high quality datasets are often small in sample size that can result in overfitting, where models memorize noise rather than learn generalizable patterns. This in particular, could pose serious risks in medical diagnostics where the risk associated with mis-classification can impact human life. Several data-efficient training strategies have emerged to address these constraints. In particular, coreset selection identifies compact subsets of the most representative samples, enabling training that approximates full-dataset performance while reducing computational overhead. On the other hand, curriculum learning relies on gradually increasing training difficulty and accelerating convergence. However, developing a generalizable difficulty ranking mechanism that works across diverse domains, datasets, and models while reducing the computational tasks and remains challenging. In this paper, we introduce Iterative Misclassification Error Training (IMET), a novel framework inspired by curriculum learning and coreset selection. The IMET approach is aimed to identify misclassified samples in order to streamline the training process, while prioritizing the model's attention to edge case senarious and rare outcomes. The paper evaluates IMET's performance on benchmark medical image classification datasets against state-of-the-art ResNet architectures. The results demonstrating IMET's potential for enhancing model robustness and accuracy in medical image analysis are also presented in the paper.

Accurate stratification of the activity index of Crohn's disease (CD) using computed tomography enterography (CTE) radiomics and serum markers can aid in predicting disease progression and assist physicians in personalizing therapeutic regimens for patients with CD. This retrospective study enrolled 233 patients diagnosed with CD between January 2019 and August 2024. Patients were divided into training and testing cohorts at a ratio of 7:3 and further categorized into remission, mild active phase, and moderate-severe active phase groups based on simple endoscopic score for CD (SEC-CD). Radiomics features were extracted from CTE venous images, and T-test and least absolute shrinkage and selection operator (LASSO) regression were applied for feature selection. The serum markers were selected based on the variance analysis. We also developed a random forest (RF) model for multi-class stratification of CD. The model performance was evaluated by the area under the receiver operating characteristic curve (AUC) and quantified the contribution of each feature in the dataset to CD activity via Shapley additive exPlanations (SHAP) values. Finally, we enrolled gender, radiomics scores, and serum scores to develop a nomogram model to verify the effectiveness of feature extraction. 14 non-zero coefficient radiomics features and six serum markers with significant differences (P<0.01) were ultimately selected to predict CD activity. The AUC (micro/macro) for the ensemble machine learning model combining the radiomics features and serum markers is 0.931/0.928 for three-class. The AUC for the remission phase, the mild active phase, and the moderate-severe active phase were 0.983, 0.852, and 0.917, respectively. The mean AUC for the nomogram model was 0.940. A radiomics model was developed by integrating radiomics and serum markers of CD patients, achieving enhanced consistency with SEC-CD in grade CD. This model has the potential to assist clinicians in accurate diagnosis and treatment.

As technological innovation in computers has advanced, radiologists may now diagnose brain tumors (BT) with the use of artificial intelligence (AI). In the medical field, early disease identification enables further therapies, where the use of AI systems is essential for time and money savings. The difficulties presented by various forms of Magnetic Resonance (MR) imaging for BT detection are frequently not addressed by conventional techniques. To get around frequent problems with traditional tumor detection approaches, deep learning techniques have been expanded. Thus, for BT segmentation utilizing MR images, a U-Net-based architecture combined with Attention Mechanisms has been developed in this work. Moreover, by fine-tuning essential variables, Hyperparameter Optimization (HPO) is used using the Bayesian Optimization Algorithm to strengthen the segmentation model's performance. Tumor regions are pinpointed for segmentation using Region-Adaptive Thresholding technique, and the segmentation results are validated against ground truth annotated images to assess the performance of the suggested model. Experiments are conducted using the LGG, Healthcare, and BraTS 2021 MRI brain tumor datasets. Lastly, the importance of the suggested model has been demonstrated through comparing several metrics, such as IoU, accuracy, and DICE Score, with current state-of-the-art methods. The U-Net-based method gained a higher DICE score of 0.89687 in the segmentation of MRI-BT.

This research aimed to compare the classification accuracy of three machine learning (ML) methods (random forest (RF), support vector machines (SVM), linear discriminant analysis (LDA)) for sex estimation of sub-adults using cranial computed tomography (CCT) images. A total of 521 CCT scans from sub-adult Malaysians aged 0 to 20 were analysed using Mimics software (Materialise Mimics Ver. 21). Plane-to-plane (PTP) protocol was used for measuring 14 chosen craniometric parameters. A trio of machine learning algorithms RF, SVM, and LDA with GridSearchCV was used to produce classification models for sex estimation. In addition, performance was measured in the form of accuracy, precision, recall, and F1-score, among others. RF produced testing accuracy of 73%, with the best hyperparameters of max_depth = 6, max_samples = 40, and n_estimators = 45. SVM obtained an accuracy of 67% with the best hyperparameters: learning rate (C) = 10, gamma = 0.01, and kernel = radial basis function (RBF). LDA obtained the lowest accuracy of 65% with shrinkage of 0.02. Among the tested ML methods, RF showed the highest testing accuracy in comparison to SVM and LDA. This is the first AI-based classification model that can be used for estimating sex in sub-adults using CCT scans.