Deep learning (DL) reconstruction has been successful in realizing otherwise impracticable acceleration factors and improving image quality in conventional MRI field strengths; however, there has been limited application to ultra-high field MRI.The objective of this study was to evaluate the performance of a prototype DL-based image reconstruction technique in 7T MRI of the brain utilizing MP2RAGE and SPACE acquisitions, in comparison to reconstructions in conventional compressed sensing (CS) and controlled aliasing in parallel imaging (CAIPIRINHA) techniques. This retrospective study involved 60 patients who underwent 7T brain MRI between June 2024 and October 2024, comprised of 30 patients with MP2RAGE data and 30 patients with SPACE FLAIR data. Each set of raw data was reconstructed with DL-based reconstruction and conventional reconstruction. Image quality was independently assessed by two neuroradiologists using a 5-point Likert scale, which included overall image quality, artifacts, sharpness, structural conspicuity, and noise level. Inter-observer agreement was determined using top-box analysis. Contrast-to-noise ratio (CNR) and noise levels were quantitatively evaluated and compared using the Wilcoxon signed-rank test. DL-based reconstruction resulted in a significant increase in overall image quality and a reduction in subjective noise level for both MP2RAGE and SPACE FLAIR data (all P<0.001), with no significant differences in image artifacts (all P>0.05). When compared to standard reconstruction, the implementation of DL-based reconstruction yielded an increase in CNR of 49.5% [95% CI 33.0-59.0%] for MP2RAGE data and 90.6% [95% CI 73.2-117.7%] for SPACE FLAIR data, along with a decrease in noise of 33.5% [95% CI 23.0-38.0%] for MP2RAGE data and 47.5% [95% CI 41.9-52.6%] for SPACE FLAIR data. DL-based reconstruction of 7T MRI significantly enhanced image quality compared to conventional reconstruction without introducing image artifacts. The achievable high acceleration factors have the potential to substantially improve image quality and resolution in 7T MRI. CAIPIRINHA = Controlled Aliasing In Parallel Imaging Results IN Higher Acceleration; CNR = contrast-to-noise ratio; CS = compressed sensing; DL = deep learning; MNI = Montreal Neurological Institute; MP2RAGE = Magnetization-Prepared 2 Rapid Acquisition Gradient Echoes; SPACE = Sampling Perfection with Application-Optimized Contrasts using Different Flip Angle Evolutions.

Pathological myopia (PM) has emerged as a leading cause of global visual impairment, early detection and precise grading of PM are crucial for timely intervention. The atrophy, traction and neovascularisation (ATN) system is applied to define PM progression and stages with precision. This study focuses on constructing a comprehensive PM image dataset comprising both fundus and optical coherence tomography (OCT) images and developing a bimodal artificial intelligence (AI) classification model for ATN grading in PM. This single-centre retrospective cross-sectional study collected 2760 colour fundus photographs and matching OCT images of PM from January 2019 to November 2022 at Peking Union Medical College Hospital. Ophthalmology specialists labelled and inspected all paired images using the ATN grading system. The AI model used a ResNet-50 backbone and a multimodal multi-instance learning module to enhance interaction across instances from both modalities. Performance comparisons among single-modality fundus, OCT and bimodal AI models were conducted for ATN grading in PM. The bimodality model, dual-deep learning (DL), demonstrated superior accuracy in both detailed multiclassification and biclassification of PM, which aligns well with our observation from instance attention-weight activation maps. The area under the curve for severe PM using dual-DL was 0.9635 (95% CI 0.9380 to 0.9890), compared with 0.9359 (95% CI 0.9027 to 0.9691) for the solely OCT model and 0.9268 (95% CI 0.8915 to 0.9621) for the fundus model. Our novel bimodal AI multiclassification model for PM ATN staging proves accurate and beneficial for public health screening and prompt referral of PM patients.

Breast cancer represents a significant global health challenge, which makes it essential to detect breast cancer early and accurately to improve patient prognosis and reduce mortality rates. However, traditional diagnostic processes relying on manual analysis of medical images are inherently complex and subject to variability between observers, highlighting the urgent need for robust automated breast cancer detection systems. While deep learning has demonstrated potential, many current models struggle with limited accuracy and lack of interpretability. This research introduces the Deep Neural Breast Cancer Detection (DNBCD) model, an explainable AI-based framework that utilizes deep learning methods for classifying breast cancer using histopathological and ultrasound images. The proposed model employs Densenet121 as a foundation, integrating customized Convolutional Neural Network (CNN) layers including GlobalAveragePooling2D, Dense, and Dropout layers along with transfer learning to achieve both high accuracy and interpretability for breast cancer diagnosis. The proposed DNBCD model integrates several preprocessing techniques, including image normalization and resizing, and augmentation techniques to enhance the model's robustness and address class imbalances using class weight. It employs Grad-CAM (Gradient-weighted Class Activation Mapping) to offer visual justifications for its predictions, increasing trust and transparency among healthcare providers. The model was assessed using two benchmark datasets: Breakhis-400x (B-400x) and Breast Ultrasound Images Dataset (BUSI) containing 1820 and 1578 images, respectively. We systematically divided the datasets into training (70%), testing (20%,) and validation (10%) sets, ensuring efficient model training and evaluation obtaining accuracies of 93.97% for B-400x dataset having benign and malignant classes and 89.87% for BUSI dataset having benign, malignant, and normal classes for breast cancer detection. Experimental results demonstrate that the proposed DNBCD model significantly outperforms existing state-of-the-art approaches with potential uses in clinical environments. We also made all the materials publicly accessible for the research community at: https://github.com/romzanalom/XAI-Based-Deep-Neural-Breast-Cancer-Detection .

Deep learning, a subset of machine learning, is widely utilized in medical applications. Identifying maxillary sinus pathologies before surgical interventions is crucial for ensuring successful treatment outcomes. Cone beam computed tomography (CBCT) is commonly employed for maxillary sinus evaluations due to its high resolution and lower radiation exposure. This study aims to assess the accuracy of artificial intelligence (AI) algorithms in detecting maxillary sinus pathologies from CBCT scans. A dataset comprising 1000 maxillary sinuses (MS) from 500 patients was analyzed using CBCT. Sinuses were categorized based on the presence or absence of pathology, followed by segmentation of the maxillary sinus. Manual segmentation masks were generated using the semiautomatic software ITK-SNAP, which served as a reference for comparison. A convolutional neural network (CNN)-based machine learning model was then implemented to automatically segment maxillary sinus pathologies from CBCT images. To evaluate segmentation accuracy, metrics such as the Dice similarity coefficient (DSC) and intersection over union (IoU) were utilized by comparing AI-generated results with human-generated segmentations. The automated segmentation model achieved a Dice score of 0.923, a recall of 0.979, an IoU of 0.887, an F1 score of 0.970, and a precision of 0.963. This study successfully developed an AI-driven approach for segmenting maxillary sinus pathologies in CBCT images. The findings highlight the potential of this method for rapid and accurate clinical assessment of maxillary sinus conditions using CBCT imaging.

The development of larger models for medical image analysis has led to increased performance. However, it also affected our ability to explain and validate model decisions. Models can use non-relevant parts of images, also called spurious correlations or shortcuts, to obtain high performance on benchmark datasets but fail in real-world scenarios. In this work, we challenge the capacity of convolutional neural networks (CNN) to classify chest X-rays and eye fundus images while masking out clinically relevant parts of the image. We show that all models trained on the PadChest dataset, irrespective of the masking strategy, are able to obtain an area under the curve (AUC) above random. Moreover, the models trained on full images obtain good performance on images without the region of interest (ROI), even superior to the one obtained on images only containing the ROI. We also reveal a possible spurious correlation in the Chákṣu dataset while the performances are more aligned with the expectation of an unbiased model. We go beyond the performance analysis with the usage of the explainability method SHAP and the analysis of embeddings. We asked a radiology resident to interpret chest X-rays under different masking to complement our findings with clinical knowledge.

A layman in health systems is a person who doesn't have any knowledge about health data i.e., X-ray, MRI, CT scan, and health examination reports, etc. The motivation behind the proposed invention is to help laymen to make medical images understandable. The health model is trained using a neural network approach that analyses user health examination data; predicts the type and level of the disease and advises precaution to the user. Cellular Automata (CA) technology has been integrated with the neural networks to segment the medical image. The CA analyzes the medical images pixel by pixel and generates a robust threshold value which helps to efficiently segment the image and identify accurate abnormal spots from the medical image. The proposed method has been trained and experimented using 10000+ medical images which are taken from various open datasets. Various text analysis measures i.e., BLEU, ROUGE, and WER are used in the research to validate the produced report. The BLEU and ROUGE calculate a similarity to decide how the generated text report is closer to the original report. The BLEU and ROUGE scores of the experimented images are approximately 0.62 and 0.90, claims that the produced report is very close to the original report. The WER score 0.14, claims that the generated report contains the most relevant words. The overall summary of the proposed research is that it provides a fruitful medical report with accurate disease and precautions to the laymen.

The aim of this study was to develop a hybrid diagnostic framework integrating radiomic and deep features for accurate and reproducible detection and classification of wrist fractures using X-ray images. A total of 3,537 X-ray images, including 1,871 fracture and 1,666 non-fracture cases, were collected from three healthcare centers. Radiomic features were extracted using the PyRadiomics library, and deep features were derived from the bottleneck layer of an autoencoder. Both feature modalities underwent reliability assessment via Intraclass Correlation Coefficient (ICC) and cosine similarity. Feature selection methods, including ANOVA, Mutual Information (MI), Principal Component Analysis (PCA), and Recursive Feature Elimination (RFE), were applied to optimize the feature set. Classifiers such as XGBoost, CatBoost, Random Forest, and a Voting Classifier were used to evaluate diagnostic performance. The dataset was divided into training (70%) and testing (30%) sets, and metrics such as accuracy, sensitivity, and AUC-ROC were used for evaluation. The combined radiomic and deep feature approach consistently outperformed standalone methods. The Voting Classifier paired with MI achieved the highest performance, with a test accuracy of 95%, sensitivity of 94%, and AUC-ROC of 96%. The end-to-end model achieved competitive results with an accuracy of 93% and AUC-ROC of 94%. SHAP analysis and t-SNE visualizations confirmed the interpretability and robustness of the selected features. This hybrid framework demonstrates the potential for integrating radiomic and deep features to enhance diagnostic performance for wrist and forearm fractures, providing a reliable and interpretable solution suitable for clinical applications.

In cone beam computed tomography(CBCT)-guided adaptive radiotherapy, rapid and precise segmentation of organs-at-risk(OARs)is essential for accurate dose verification and online replanning. The quality of CBCT images obtained with current onboard CBCT imagers and clinical imaging protocols, however, is often compromised by artifacts such as scatter and motion, particularly for thoracic CBCTs. These artifacts not only degrade image contrast but also obscure anatomical boundaries, making accurate segmentation on CBCT images significantly more challenging compared to planning CT images. To address these persistent challenges, we propose a novel multi-channel conditional consistency diffusion model(MCCDM)for segmentation of OARs in thoracic CBCT images (CBCT-MCCDM), which harnesses its domain transfer capabilities to improve segmentation accuracy across different imaging modalities. By jointly training the MCCDM with CT images and their corresponding masks, our framework enables an end-to-end mapping learning process that generates accurate segmentation of OARs.&#xD;This CBCT-MCCDM was used to delineate esophagus, heart, the left and right lungs, and spinal cord on CBCT images from each patient with lung cancer. We quantitatively evaluated our approach by comparing model-generated contours with ground truth contours from 33 patients with lung cancer treated with 5-fraction stereotactic body radiation therapy (SBRT), demonstrating its potential to enhance segmentation accuracy despite the presence of challenging CBCT artifacts. The proposed method was evaluated using average Dice similarity coefficients (DSC), sensitivity, specificity, 95th Percentile Hausdorff Distance (HD95), and mean surface distance (MSD) for each of the five OARs. The method achieved average DSC values of 0.82, 0.88, 0.95, 0.96, and 0.96 for the esophagus, heart, left lung, right lung, and spinal cord, respectively. Sensitivity values were 0.813, 0.922, 0.956, 0.958, and 0.929, respectively, while specificity values were 0.991, 0.994, 0.996, 0.996, and 0.995, respectively. We compared the proposed method with two state-of-art methods, CBCT-only method and U-Net, and demonstrated that the proposed CBCT-MCCDM.

Ultrasound imaging is widely applied in clinical practice, yet ultrasound videos often suffer from low signal-to-noise ratios (SNR) and limited resolutions, posing challenges for diagnosis and analysis. Variations in equipment and acquisition settings can further exacerbate differences in data distribution and noise levels, reducing the generalizability of pre-trained models. This work presents a self-supervised ultrasound video super-resolution algorithm called Deep Ultrasound Prior (DUP). DUP employs a video-adaptive optimization process of a neural network that enhances the resolution of given ultrasound videos without requiring paired training data while simultaneously removing noise. Quantitative and visual evaluations demonstrate that DUP outperforms existing super-resolution algorithms, leading to substantial improvements for downstream applications.

The International Society of Ultrasound advocates Intrapartum Ultrasound (US) Imaging in Obstetrics and Gynecology (ISUOG) to monitor labour progression through changes in fetal head position. Two reliable ultrasound-derived parameters that are used to predict outcomes of instrumental vaginal delivery are the angle of progression (AoP) and head-symphysis distance (HSD). In this work, as part of the Intrapartum Ultrasounds Grand Challenge (IUGC) 2024, we propose an automated fetal biometry measurement pipeline to reduce intra- and inter-observer variability and improve measurement reliability. Our pipeline consists of three key tasks: (i) classification of standard planes (SP) from US videos, (ii) segmentation of fetal head and pubic symphysis from the detected SPs, and (iii) computation of the AoP and HSD from the segmented regions. We perform sparse sampling to mitigate class imbalances and reduce spurious correlations in task (i), and utilize ensemble-based deep learning methods for task (i) and (ii) to enhance generalizability under different US acquisition settings. Finally, to promote robustness in task iii) with respect to the structural fidelity of measurements, we retain the largest connected components and apply ellipse fitting to the segmentations. Our solution achieved ACC: 0.9452, F1: 0.9225, AUC: 0.983, MCC: 0.8361, DSC: 0.918, HD: 19.73, ASD: 5.71, $\Delta_{AoP}$: 8.90 and $\Delta_{HSD}$: 14.35 across an unseen hold-out set of 4 patients and 224 US frames. The results from the proposed automated pipeline can improve the understanding of labour arrest causes and guide the development of clinical risk stratification tools for efficient and effective prenatal care.