Radiological diagnosis of adrenal lesions can be challenging due to the overlap between benign and malignant imaging features. The primary challenge in managing adrenal lesions is to accurately identify and characterize them to minimize unnecessary diagnostic examinations and interventions. However, there are substantial risks of underdiagnosis and misdiagnosis. This review article provides a comprehensive overview of typical, atypical, and overlapping imaging features of both common and rare adrenal lesions and explores emerging applications of artificial intelligence powered analysis of CT and MRI, which could play a pivotal role in distinguishing benign from malignant and functioning from non-functioning adrenal lesions with significant diagnostic accuracy, thereby enhancing diagnostic confidence and potentially reducing unnecessary interventions.

Dental implants have become more straightforward and convenient with advancements of digital technology in dentistry. Implant planning utilizing artificial intelligence (AI) has been attempted, yet its clinical efficacy remains underexplored. We aimed to assess the clinical applicability of AI-based implant planning software as a decision-support tool in comparison with those placed by clinicians which were clinically appropriate in their three-dimensional positions. Overall, 350 implants from 228 patients treated at four university hospitals were analyzed. The AI algorithm was developed using enhanced deep convolutional neural networks. Implant positions planned by the AI were compared with those placed freehand by clinicians. Three-dimensional deviations were measured and analyzed according to clinical factors, including the presence of opposing or contralateral teeth, jaw, and side. Independent sample t-test and two-way ANOVA were employed for statistical analysis. The mean coronal, apical, and angular deviations were 2.99 ± 1.56 mm, 3.66 ± 1.68 mm, and 7.56 ± 4.67°, respectively. Angular deviation was significantly greater in the absence of contralateral teeth (p=0.039), and apical deviation was significantly greater in the mandible (p<0.001). The AI-based 3D implant planning tool demonstrated potential as a decision-support system by providing valuable guidance in clinical scenarios. However, discrepancies between AI-generated and actual implant positions indicate that further research and development are needed to enhance its predictive accuracy. AI-based implant planning may serve as a supportive tool under clinician supervision, potentially improving workflow efficiency and contributing to more standardized implant treatment planning as the technology advances.

Current diagnostic methods for autism spectrum disorder (ASD) are based on subjective behavioral assessments, which present challenges to an accurate and early diagnosis. This paper proposes a hybrid deep learning framework, ASD-HybridNet, which integrates both region of interest (ROI) time series data and functional connectivity (FC) maps derived from functional magnetic resonance imaging (fMRI) data to improve ASD detection. Experiments on the ABIDE dataset demonstrate the effectiveness of the proposed method compared to existing approaches.

Early detection of lung cancer is crucial for effective treatment and relies on accurate volumetric assessment of pulmonary nodules in CT scans. Traditional methods, such as consolidation-to-tumor ratio (CTR) and spherical approximation, are limited by inconsistent estimates due to variability in nodule shape and density. We propose an advanced framework that combines a multi-scale 3D convolutional neural network (CNN) with subtype-specific bias correction for precise volume estimation. The model was trained and evaluated on a dataset of 364 cases from Shanghai Chest Hospital. Our approach achieved a mean absolute deviation of 8.0 percent compared to manual nonlinear regression, with inference times under 20 seconds per scan. This method outperforms existing deep learning and semi-automated pipelines, which typically have errors of 25 to 30 percent and require over 60 seconds for processing. Our results show a reduction in error by over 17 percentage points and a threefold acceleration in processing speed. These advancements offer a highly accurate, efficient, and scalable tool for clinical lung nodule screening and monitoring, with promising potential for improving early lung cancer detection.

Ultrasound only has low-to-moderate accuracy for sarcopenia. We aimed to investigate whether ultrasound radiomics combined with machine learning enhances sarcopenia diagnostic accuracy compared with conventional ultrasound parameters among older adults in long-term care. Diagnostic accuracy study. A total of 628 residents from 15 nursing homes in China. Sarcopenia diagnosis followed AWGS 2019 criteria. Ultrasound of thigh muscles (rectus femoris [ReF], vastus intermedius [VI], and quadriceps femoris [QF]) was performed. Conventional parameters (muscle thickness [MT], echo intensity [EI]) and radiomic features were extracted. Participants were split into training (70%)/validation (30%) sets. Conventional (muscle thickness + EI), radiomics, and integrated (MT, echo intensity, radiomics, basic clinical data including age, sex, and body mass index) models were built using 5 machine learning algorithms (including logistic regression [LR]). Performance was assessed in the validation set using the area under the receiver operating characteristic curve (AUC), calibration, and decision curve analysis (DCA). Sarcopenia prevalence was 61.9%. The LR algorithm consistently exhibited superior performance. The diagnostic accuracy of the ultrasound radiomic models was superior to that of the models based on conventional ultrasound parameters, regardless of muscle group. The integrated models further improved the accuracy, achieving AUCs (95% CIs) of 0.85 (0.79-0.91) for ReF, 0.81 (0.75-0.87) for VI, and 0.83 (0.77-0.90) for QF. In the validation set, the AUCs (95% CIs) for the conventional ultrasound models were 0.70 (0.63-0.78) for ReF, 0.73 (0.65-0.80) for VI, and 0.75 (0.68-0.82) for QF. The corresponding AUCs (95% CIs) for the radiomics models were 0.76 (0.69-0.83) for ReF, 0.76 (0.69-0.83) for VI, and 0.78 (0.71-0.85) for QF. The integrated models demonstrated good calibration and net benefit in DCA. Ultrasound radiomics, especially when integrated with conventional parameters and clinical data using LR, significantly improves sarcopenia diagnostic accuracy in nursing home residents. This accessible, noninvasive approach holds promise for enhancing sarcopenia screening and early detection in long-term care settings.

Despite recent advances in brain-computer interfaces (BCIs) for speech restoration, existing systems remain invasive, costly, and inaccessible to individuals with congenital mutism or neurodegenerative disease. We present a proof-of-concept pipeline that synthesizes personalized speech directly from real-time magnetic resonance imaging (rtMRI) of the vocal tract, without requiring acoustic input. Segmented rtMRI frames are mapped to articulatory class representations using a Pix2Pix conditional GAN, which are then transformed into synthetic audio waveforms by a convolutional neural network modeling the articulatory-to-acoustic relationship. The outputs are rendered into audible form and evaluated with speaker-similarity metrics derived from Resemblyzer embeddings. While preliminary, our results suggest that even silent articulatory motion encodes sufficient information to approximate a speakers vocal characteristics, offering a non-invasive direction for future speech restoration in individuals who have lost or never developed voice.

This study reveals that pulmonary nodules exhibit distinct multifractal characteristics, with malignant nodules demonstrating significantly higher fractal dimensions at larger scales. Based on this fundamental finding, an automatic benign-malignant classification method for pulmonary nodules in CT images was developed using fractal spectrum analysis. By computing continuous three-dimensional fractal dimensions on 121 nodule samples from the LIDC-IDRI database, a 201-dimensional fractal feature spectrum was extracted, and a simplified multilayer perceptron neural network (with only 6x6 minimal neural network nodes in the intermediate layers) was constructed for pulmonary nodule classification. Experimental results demonstrate that this method achieved 96.69% accuracy in distinguishing benign from malignant pulmonary nodules. The discovery of scale-dependent multifractal properties enables fractal spectrum analysis to effectively capture the complexity differences in multi-scale structures of malignant nodules, providing an efficient and interpretable AI-aided diagnostic method for early lung cancer diagnosis.

BackgroundSphericity is a measurement of how closely an object approximates a globe. The sphericity of the blood pool of the left ventricle (LV), is an emerging measure linked to myocardial dysfunction. MethodsVideo-based deep learning models were trained for semantic segmentation (pixel labeling) in cardiac magnetic resonance imaging in 84,327 UK Biobank participants. These labeled pixels were co-oriented in 3D and used to construct surface meshes. LV ejection fraction, mass, volume, surface area, and sphericity were calculated. Epidemiologic and genetic analyses were conducted. Polygenic score validation was performed in All of Us. Results3D LV sphericity was found to be more strongly associated (HR 10.3 per SD, 95% CI 6.1-17.3) than LV ejection fraction (HR 2.9 per SD reduction, 95% CI 2.4-3.6) with dilated cardiomyopathy (DCM). Paired with whole genome sequencing, these measurements linked LV structure and function to 366 distinct common and low-frequency genetic loci--and 17 genes with rare variant burden--spanning a 25-fold range of effect size. The discoveries included 22 out of the 26 loci that were recently associated with DCM. LV genome-wide polygenic scores were equivalent to, or outperformed, dedicated hypertrophic cardiomyopathy (HCM) and DCM polygenic scores for disease prediction. In All of Us, those in the polygenic extreme 1% had an estimated 6.6% risk of DCM by age 80, compared to 33% for carriers of rare truncating variants in the gene TTN. Conclusions3D sphericity is a distinct, heritable LV measurement that is intricately linked to risk for HCM and DCM. The genetic findings from this study raise the possibility that the majority of common genetic loci that will be discovered in future large-scale DCM analyses are present in the current results.

Artificial intelligence in medical imaging has grown rapidly in the past decade, driven by advances in deep learning and widespread access to computing resources. Applications cover diverse imaging modalities, including those based on electromagnetic radiation (e.g., X-rays), subatomic particles (e.g., nuclear imaging), and acoustic waves (ultrasound). Each modality features and limitations are defined by its underlying physics. However, many artificial intelligence practitioners lack a solid understanding of the physical principles involved in medical image acquisition. This gap hinders leveraging the full potential of deep learning, as incorporating physics knowledge into artificial intelligence systems promotes trustworthiness, especially in limited data scenarios. This work reviews the fundamental physical concepts behind medical imaging and examines their influence on recent developments in artificial intelligence, particularly, generative models and reconstruction algorithms. Finally, we describe physics-informed machine learning approaches to improve feature learning in medical imaging.

Stroke poses a significant health challenge, with ischemic and hemorrhagic subtypes requiring timely and accurate diagnosis for effective management. Traditional imaging techniques like CT have limitations, particularly in early ischemic stroke detection. Recent advancements in artificial intelligence (AI) offer potential improvements in stroke diagnosis by enhancing imaging interpretation. This meta-analysis aims to evaluate the diagnostic accuracy of AI systems compared to human experts in detecting ischemic and hemorrhagic strokes. The review was conducted following PRISMA-DTA guidelines. Studies included stroke patients evaluated in emergency settings using AI-Based models on CT or MRI imaging, with human radiologists as the reference standard. Databases searched were MEDLINE, Scopus, and Cochrane Central, up to January 1, 2024. The primary outcome measured was diagnostic accuracy, including sensitivity, specificity, and AUROC and the methodological quality was assessed using QUADAS-2. Nine studies met the inclusion criteria and were included. The pooled analysis for ischemic stroke revealed a mean sensitivity of 86.9% (95% CI: 69.9%-95%) and specificity of 88.6% (95% CI: 77.8%-94.5%). For hemorrhagic stroke, the pooled sensitivity and specificity were 90.6% (95% CI: 86.2%-93.6%) and 93.9% (95% CI: 87.6%-97.2%), respectively. The diagnostic odds ratios indicated strong diagnostic efficacy, particularly for hemorrhagic stroke (DOR: 148.8, 95% CI: 79.9-277.2). AI-Based systems exhibit high diagnostic accuracy for both ischemic and hemorrhagic strokes, closely approaching that of human radiologists. These findings underscore the potential of AI to improve diagnostic precision and expedite clinical decision-making in acute stroke settings.