Magnetic Resonance Imaging (MRI) based three-dimensional analysis of knee cartilage has evolved to become fully automatic. However, when implementing these measurements across multiple clinical centers, scanner variability becomes a critical consideration. Our purposes were to quantify and compare same-model variability (between repeated scans on the same MRI system) and cross-model variability (across different MRI systems) in knee cartilage thickness measurements using MRI scanners from five manufacturers, as analyzed with a specific 3D volume analysis software. Ten healthy volunteers (eight males and two females, aged 22-60 years) underwent two scans of their right knee on 3T MRI systems from five manufacturers (Canon, Fujifilm, GE, Philips, and Siemens). The imaging protocol included fat-suppressed spoiled gradient echo and proton density weighted sequences. Cartilage regions were automatically segmented into 7 subregions using a specific deep learning-based 3D volume analysis software. This resulted in 350 measurements for same-model variability and 2,800 measurements for cross-model variability. For same-model variability, 82% of measurements showed variability ≤0.10 mm, and 98% showed variability ≤0.20 mm. For cross-model variability, 51% showed variability ≤0.10 mm, and 84% showed variability ≤0.20 mm. The mean same-model variability (0.06 ± 0.05 mm) was significantly lower than cross-model variability (0.11 ± 0.09 mm) (p < 0.001). This study demonstrates that knee cartilage thickness measurements exhibit significantly higher variability across different MRI systems compared to repeated measurements on the same system, when analyzed using this specific software. This finding has important implications for multi-center studies and longitudinal assessments using different MRI systems and highlights the software-dependent nature of such variability assessments.

Advancements in medical imaging technology have facilitated the acquisition of high-quality brain images through computed tomography (CT) or magnetic resonance imaging (MRI), enabling professional brain specialists to diagnose brain tumors more effectively. However, manual diagnosis is time-consuming, which has led to the growing importance of automatic detection and classification through brain imaging. Conventional object detection models for brain tumor detection face limitations in brain tumor detection owing to the significant differences between medical images and natural scene images, as well as challenges such as complex backgrounds, noise interference, and blurred boundaries between cancerous and normal tissues. This study investigates the application of deep learning to brain tumor detection, analyzing the effect of three factors, the number of model parameters, input data batch size, and the use of anchor boxes, on detection performance. Experimental results reveal that an excessive number of model parameters or the use of anchor boxes may reduce detection accuracy. However, increasing the number of brain tumor samples improves detection performance. This study, introduces a backbone network built using RepConv and RepC3, along with FGConcat feature map splicing module to optimize the brain tumor detection model. The experimental results show that the proposed RepConv-RepC3-FGConcat Network (RRFNet) can learn underlying semantic information about brain tumors during training stage, while maintaining a low number of parameters during inference, which improves the speed of brain tumor detection. Compared with YOLOv8, RRFNet achieved a higher accuracy in brain tumor detection, with a mAP value of 79.2%. This optimized approach enhances both accuracy and efficiency, which is essential in clinical settings where time and precision are critical.

This study investigated perspectives and experiences of artificial intelligence (AI) developers, clinicians and patients about the use of AI-based software in cardiac healthcare. A qualitative study took place at two hospitals in England that had trialled AI-based software use in stress echocardiography, a scan that uses ultrasound to assess heart function. Semi-structured interviews were conducted with: patients (<i>n = </i>9), clinicians (<i>n = </i>16) and AI software developers (<i>n = </i>5). Data were analysed using thematic analysis. Potential benefits identified were increasing consistency and reliability through reducing human error, and greater efficiency. Concerns included over-reliance on the AI technology, and data security. Participants discussed the need for human input and empathy within healthcare, transparency about AI use, and issues around trusting AI. Participants considered AI's role as assisting diagnosis but not replacing clinician involvement. Clinicians and patients emphasised holistic diagnosis that involves more than the scan. Clinicians considered their diagnostic ability as superior and discrepancies were managed in line with clinicians' diagnoses rather than AI reports. The practicalities of using the AI software concerned image acquisition to meet AI processing requirements and workflow integration. There was positivity towards AI use, but the AI software was considered an adjunct to clinicians rather than replacing their input. Clinicians' experiences were that their diagnostic ability remained superior to the AI, and acquiring images acceptable to AI was sometimes problematic. Despite hopes for increased efficiency through AI use, clinicians struggled to identify fit with clinical workflow to bring benefit.

Optimizing resource allocation for Parkinson's disease (PD) motor rehabilitation necessitates identifying biomarkers of responsiveness and dynamic neuroplasticity signatures underlying efficacy. A cohort study of 52 early-stage PD patients undergoing 2-week multidisciplinary intensive rehabilitation therapy (MIRT) was conducted, which stratified participants into responders and nonresponders. A multimodal analysis of resting-state electroencephalography (EEG) microstates and functional magnetic resonance imaging (fMRI) coactivation patterns was performed to characterize MIRT-induced spatiotemporal network reorganization. Responders demonstrated clinically meaningful improvement in motor symptoms, exceeding the minimal clinically important difference threshold of 3.25 on the Unified PD Rating Scale part III, alongside significant reductions in bradykinesia and a significant enhancement in quality-of-life scores at the 3-month follow-up. Resting-state EEG in responders showed a significant attenuation in microstate C and a significant enhancement in microstate D occurrences, along with significantly increased transitions from microstate A/B to D, which significantly correlated with motor function, especially in bradykinesia gains. Concurrently, fMRI analyses identified a prolonged dwell time of the dorsal attention network coactivation/ventral attention network deactivation pattern, which was significantly inversely associated with microstate C occurrence and significantly linked to motor improvement. The identified brain spatiotemporal neural markers were validated using machine learning models to assess the efficacy of MIRT in motor rehabilitation for PD patients, achieving an average accuracy rate of 86%. These findings suggest that MIRT may facilitate a shift in neural networks from sensory processing to higher-order cognitive control, with the dynamic reallocation of attentional resources. This preliminary study validates the necessity of integrating cognitive-motor strategies for the motor rehabilitation of PD and identifies novel neural markers for assessing treatment efficacy.

To investigate the efficacy of CT measurement parameters combined with AI-assisted 3D planning for personalized femoral prosthesis selection in total hip arthroplasty (THA). A retrospective analysis was conducted on clinical data from 247 patients with unilateral hip or knee joint disorders treated at Renmin Hospital of Hubei University of Medicine between April 2021 and February 2024. All patients underwent preoperative full-pelvis and bilateral full-length femoral CT scans. The raw CT data were imported into Mimics 19.0 software to reconstruct a three-dimensional (3D) model of the healthy femur. Using 3-matic Research 11.0 software, the femoral head rotation center was located, and parameters including femoral head diameter (FHD), femoral neck length (FNL), femoral neck-shaft angle (FNSA), femoral offset (FO), femoral neck anteversion angle (FNAA), tip-apex distance (TAD), and tip-apex angle (TAA) were measured. AI-assisted THA 3D planning system AIJOINT V1.0.0.0 software was used for preoperative planning and design, enabling personalized selection of femoral prostheses with varying neck-shaft angles and surgical simulation. Groups were compared by gender, age, and parameters. ROC curves evaluated prediction efficacy. Females exhibited smaller FHD, FNL, FO, TAD, TAA but larger FNSA/FNAA vs males (P<0.05). Patients >65 years had higher FO, TAD, TAA (P<0.05). TAD-TAA correlation was strong (r=0.954), while FNSA negatively correlated with TAD/TAA (r=-0.773/-0.701). ROC analysis demonstrated high predictive accuracy: TAD (AUC=0.891, sensitivity=91.7%, specificity=87.6%) and TAA (AUC=0.882, sensitivity=100%, specificity=88.8%). CT parameters (TAA, TAD, FNSA, FO) are interrelated and effective predictors for femoral prosthesis selection. Integration with AI-assisted planning optimizes personalized THA, reducing biomechanical mismatch risks.

This narrative review outlines the current applications and considerations of artificial intelligence (AI) for diagnosis, management, and prognosis in rheumatoid arthritis (RA), axial spondyloarthritis (axSpA), and psoriatic arthritis (PsA). Advances in AI, mainly in machine learning and deep learning, have significantly influenced medical research and clinical practice over the past decades by offering precisions in data understanding and treatment approaches. AI applications have enhanced risk prediction models, early diagnosis, and better management in RA. Predictive models have guided treatment decisions such as-response to methotrexate and biologics-while wearable devices and electronic health records (EHR) improve disease activity monitoring. In addition, AI applications are reported as promising for the early identification of extra-articular involvements, prediction, detection, and assessment of comorbidities. In axSpA, AI-driven models using imaging techniques such as sacroiliac radiography, magnetic resonance imaging, and computed tomography have increased diagnostic accuracy, especially for early inflammatory changes. Predictive algorithms help stratify and predict disease outcomes, while clinical decision support systems integrate clinical and imaging data for optimized management. For PsA, AI has also allowed for early detection among psoriasis patients using genetic markers, immune profiling, and EHR-based natural language processing systems. Overall, AI models may predict diagnosis, disease severity, treatment response, and comorbidities to improve care in patients with RA, axSpA, and PsA. As a rapidly developing and improving area, AI has the potential to change our current perspective of medical practice by offering better diagnostic evaluation and treatments and improved patient follow-up. Multimodal AI, focusing on collaboration, reliability, transparency, and patient-centered innovation, looks like the future of medical practice. However, data quality, model interpretability, and ethical considerations must be addressed to ensure reliable and equitable applications in clinical practice.

Thoracic diseases, including pneumonia, tuberculosis, lung cancer, and others, pose significant health risks and require timely and accurate diagnosis to ensure proper treatment. Thus, in this research, a model for thorax disease classification using Chest X-rays is proposed by considering deep learning model. The input is pre-processed by resizing, normalizing pixel values, and applying data augmentation to address the issue of imbalanced datasets and improve model generalization. Significant features are extracted from the images using an Enhanced Auto-Encoder (EnAE) model, which combines a stacked auto-encoder architecture with an attention module to enhance feature representation and classification accuracy. To further improve feature selection, we utilize the Chaotic Whale Optimization (ChWO) Algorithm, which optimally selects the most relevant attributes from the extracted features. Finally, the disease classification is performed using the novel Improved Swin Transformer (IMSTrans) model, which is designed to efficiently process high-dimensional medical image data and achieve superior classification performance. The proposed EnAE + ChWO+IMSTrans model for thorax disease classification was evaluated using extensive Chest X-ray datasets and the Lung Disease Dataset. The proposed method demonstrates enhanced Accuracy, Precision, Recall, F-Score, MCC and MAE of 0.964, 0.977, 0.9845, 0.964, 0.9647, and 0.184 respectively indicating the reliable and efficient solution for thorax disease classification.

<b>Background:</b> No robust biomarkers have been identified to predict the efficacy of programmed cell death protein 1 (PD-1) inhibitors in patients with locoregionally advanced nasopharyngeal carcinoma (LANPC). We aimed to develop radiomic models using pre-immunotherapy MRI to predict the response to PD-1 inhibitors and the patient prognosis. <b>Methods:</b> This study included 246 LANPC patients (training cohort, <i>n</i> = 117; external test cohort, <i>n</i> = 129) from 10 centers. The best-performing machine learning classifier was employed to create the radiomic models. A combined model was constructed by integrating clinical and radiomic data. A radiomic interpretability study was performed with whole slide images (WSIs) stained with hematoxylin and eosin (H&E) and immunohistochemistry (IHC). A total of 150 patient-level nuclear morphological features (NMFs) and 12 cell spatial distribution features (CSDFs) were extracted from WSIs. The correlation between the radiomic and pathological features was assessed using Spearman correlation analysis. <b>Results:</b> The radiomic model outperformed the clinical and combined models in predicting treatment response (area under the curve: 0.760 vs. 0.559 vs. 0.652). For overall survival estimation, the combined model performed comparably to the radiomic model but outperformed the clinical model (concordance index: 0.858 vs. 0.812 vs. 0.664). Six treatment response-related radiomic features correlated with 50 H&E-derived (146 pairs, |<i>r</i>|= 0.31 to 0.46) and 2 to 26 IHC-derived NMF, particularly for CD45RO (69 pairs, |<i>r</i>|= 0.31 to 0.48), CD8 (84, |<i>r</i>|= 0.30 to 0.59), PD-L1 (73, |<i>r</i>|= 0.32 to 0.48), and CD163 (53, |<i>r</i>| = 0.32 to 0.59). Eight prognostic radiomic features correlated with 11 H&E-derived (16 pairs, |<i>r</i>|= 0.48 to 0.61) and 2 to 31 IHC-derived NMF, particularly for PD-L1 (80 pairs, |<i>r</i>|= 0.44 to 0.64), CD45RO (65, |<i>r</i>|= 0.42 to 0.67), CD19 (35, |<i>r</i>|= 0.44 to 0.58), CD66b (61, |<i>r</i>| = 0.42 to 0.67), and FOXP3 (21, |<i>r</i>| = 0.41 to 0.71). In contrast, fewer CSDFs exhibited correlations with specific radiomic features. <b>Conclusion:</b> The radiomic model and combined model are feasible in predicting immunotherapy response and outcomes in LANPC patients. The radiology-pathology correlation suggests a potential biological basis for the predictive models.

The use of artificial intelligence (AI) in medicine has shown promise to improve the quality of healthcare decisions. However, AI can be biased in a manner that produces unfair predictions for certain demographic subgroups. In MIMIC-CXR, a publicly available dataset of over 300,000 chest X-ray images, diagnostic AI has been shown to have a higher false negative rate for racial minorities. We evaluated the capacity of synthetic data augmentation, oversampling, and demographic-based corrections to enhance the fairness of AI predictions. We show that adjusting unfair predictions for demographic attributes, such as race, is ineffective at improving fairness or predictive performance. However, using oversampling and synthetic data augmentation to modify disease prevalence reduced such disparities by 74.7% and 10.6%, respectively. Moreover, such fairness gains were accomplished without reduction in performance (95% CI AUC: [0.816, 0.820] versus [0.810, 0.819] versus [0.817, 0.821] for baseline, oversampling, and augmentation, respectively).

Machine learning (ML) algorithms play a crucial role in the early and accurate diagnosis of Alzheimer's Disease (AD), which is essential for effective treatment planning. However, existing methods are not well-suited for identifying Mild Cognitive Impairment (MCI), a critical transitional stage between normal aging and AD. This inadequacy is primarily due to label imbalance and bias from different sensitve attributes in MCI classification. To overcome these challenges, we have designed an end-to-end fairness-aware approach for label-imbalanced classification, tailored specifically for neuroimaging data. This method, built on the recently developed FACIMS framework, integrates into STREAMLINE, an automated ML environment. We evaluated our approach against nine other ML algorithms and found that it achieves comparable balanced accuracy to other methods while prioritizing fairness in classifications with five different sensitive attributes. This analysis contributes to the development of equitable and reliable ML diagnostics for MCI detection.