The composition of urinary calculi serves as a critical determinant for personalized surgical strategies; however, such compositional data are often unavailable preoperatively. This study aims to develop a machine learning-based preoperative prediction model for stone composition and evaluate its clinical utility. A retrospective cohort study design was employed to include patients with urinary calculi admitted to the Department of Urology at the Second Affiliated Hospital of Zhengzhou University from 2019 to 2024. Feature selection was performed using least absolute shrinkage and selection operator (LASSO) regression combined with multivariate logistic regression, and a binary prediction model for urinary calculi was subsequently constructed. Model validation was conducted using metrics such as the area under the curve (AUC), while Shapley Additive Explanations(SHAP) values were applied to interpret the predictive outcomes. Among 708 eligible patients, distinct prediction models were established for four stone types: calcium oxalate stones: Logistic regression achieved optimal performance (AUC = 0.845), with maximum stone CT value, 24-hour urinary oxalate, and stone size as top predictors (SHAP-ranked); infection stones: Logistic regression (AUC = 0.864) prioritized stone size, urinary pH, and recurrence history; uric acid stones: LASSO-ridge-elastic net model demonstrated exceptional accuracy (AUC = 0.961), driven by maximum CT value, 24-hour oxalate, and urinary calcium; calcium-containing stones: Logistic regression attained better prediction (AUC = 0.953), relying on CT value, 24-hour calcium, and stone size. This study developed a machine learning prediction model based on multi-algorithm integration, achieving accurate preoperative discrimination of urinary stone composition. The integration of key imaging features with metabolic indicators enhanced the model's predictive performance.

Fully automated AI-based algorithms can quantify adipose tissue on abdominal CT images. The aim of this study was to investigate the clinical value of these biomarkers by determining the association between adipose tissue measures and all-cause mortality. This retrospective study included 151,141 patients who underwent abdominal CT for any reason between 2000 and 2021. A validated AI-based algorithm quantified subcutaneous (SAT) and visceral (VAT) adipose tissue cross-sectional area. A visceral-to-subcutaneous adipose tissue area ratio (VSR) was calculated. Clinical data (age at the time of CT, sex, date of death, date of last contact) was obtained from a database search of the electronic health record. Hazard ratios (HR) and Kaplan-Meier curves assessed the relationship between adipose tissue measures and mortality. The endpoint of interest was all-cause mortality, with additional subgroup analysis including age and gender. 138,169 patients were included in the final analysis. Higher VSR was associated with increased mortality; this association was strongest in younger women (highest compared to lowest risk quartile HR 3.32 in 18-39y). Lower SAT was associated with increased mortality regardless of sex or age group (HR up to 1.63 in 18-39y). Higher VAT was associated with increased mortality in younger age groups, with the trend weakening and reversing with age; this association was stronger in women. AI-based CT measures of SAT, VAT, and VSR are predictive of mortality, with VSR being the highest performing fat area biomarker overall. These metrics tended to perform better for women and younger patients. Incorporating AI tools can augment patient assessment and management, improving outcome.

Radiology report generation (RRG) tasks leverage computer-aided technology to automatically produce descriptive text reports for medical images, aiming to ease radiologists' workload, reduce misdiagnosis rates, and lessen the pressure on medical resources. However, previous works have yet to focus on enhancing feature extraction of low-quality images, incorporating cross-modal interaction information, and mitigating latency in report generation. We propose an Image-Enhanced Cross-Modal Fusion Network (IFNet) for automatic RRG to tackle these challenges. IFNet includes three key components. First, the image enhancement module enhances the detailed representation of typical and atypical structures in X-ray images, thereby boosting detection success rates. Second, the cross-modal fusion networks efficiently and comprehensively capture the interactions of cross-modal features. Finally, a more efficient transformer report generation module is designed to optimize report generation efficiency while being suitable for low-resource devices. Experimental results on public datasets IU X-ray and MIMIC-CXR demonstrate that IFNet significantly outperforms the current state-of-the-art methods.

Polycystic ovary syndrome (PCOS) has a significant impact on endocrine metabolism, reproductive function, and mental health in women of reproductive age. Ultrasound remains an essential diagnostic tool for PCOS, particularly in individuals presenting with oligomenorrhea or ovulatory dysfunction accompanied by polycystic ovaries, as well as hyperandrogenism associated with polycystic ovaries. However, the accuracy of ultrasound in identifying polycystic ovarian morphology remains variable. To develop a deep learning model capable of rapidly and accurately identifying PCOS using ovarian ultrasound images. Prospective diagnostic accuracy study. This prospective study included data from 1,751 women with suspected PCOS who presented at two affiliated hospitals at Central South University, with clinical and ultrasound information collected and archived. Patients from center 1 were randomly divided into a training set and an internal validation set in a 7:3 ratio, while patients from center 2 served as the external validation set. Using the YOLOv11 deep learning framework, an automated recognition model for ovarian ultrasound images in PCOS cases was constructed, and its diagnostic performance was evaluated. Ultrasound images from 933 patients (781 from center 1 and 152 from center 2) were analyzed. The mean average precision of the YOLOv11 model in detecting the target ovary was 95.7%, 97.6%, and 97.8% for the training, internal validation, and external validation sets, respectively. For diagnostic classification, the model achieved an F1 score of 95.0% in the training set and 96.9% in both validation sets. The area under the curve values were 0.953, 0.973, and 0.967 for the training, internal validation, and external validation sets respectively. The model also demonstrated significantly faster evaluation of a single ovary compared to clinicians (doctor, 5.0 seconds; model, 0.1 seconds; <i>p</i> < 0.01). The YOLOv11-based automatic recognition model for PCOS ovarian ultrasound images exhibits strong target detection and diagnostic performance. This approach can streamline the follicle counting process in conventional ultrasound and enhance the efficiency and generalizability of ultrasound-based PCOS assessment.

This study aimed to objectively and quantitatively analyze fetal motor behavior using DeepLabCut (DLC), a markerless posture estimation tool based on deep learning, applied to four-dimensional ultrasound (4DUS) data collected during the second trimester. We propose a novel clinical method for precise assessment of fetal neurodevelopment. Fifty 4DUS video recordings of normal singleton fetuses aged 12 to 22 gestational weeks were analyzed. Eight fetal joints were manually labeled in 2% of each video to train a customized DLC model. The model's accuracy was evaluated using likelihood scores. Intra- and inter-rater reliability of manual labeling were assessed using intraclass correlation coefficients (ICC). Angular velocity time series derived from joint coordinates were analyzed to quantify fetal movement patterns and developmental coordination. Manual labeling demonstrated excellent reproducibility (inter-rater ICC = 0.990, intra-rater ICC = 0.961). The trained DLC model achieved a mean likelihood score of 0.960, confirming high tracking accuracy. Kinematic analysis revealed developmental trends: localized rapid limb movements were common at 12-13 weeks; movements became more coordinated and systemic by 18-20 weeks, reflecting advancing neuromuscular maturation. Although a modest increase in tracking accuracy was observed with gestational age, this trend did not reach statistical significance (p < 0.001). DLC enables precise quantitative analysis of fetal motor behavior from 4DUS recordings. This AI-driven approach offers a promising, noninvasive alternative to conventional qualitative assessments, providing detailed insights into early fetal neurodevelopmental trajectories and potential early screening for neurodevelopmental disorders.

Recent innovations in medical imaging have markedly improved brain tumor identification, surpassing conventional diagnostic approaches that suffer from low resolution, radiation exposure, and limited contrast. Magnetic Resonance Imaging (MRI) is pivotal in precise and accurate tumor characterization owing to its high-resolution, non-invasive nature. This study investigates the synergy among multiple feature representation schemes such as local Binary Patterns (LBP), Gabor filters, Discrete Wavelet Transform, Fast Fourier Transform, Convolutional Neural Networks (CNN), and Gray-Level Run Length Matrix alongside five learning algorithms namely: k-nearest Neighbor, Random Forest, Support Vector Classifier (SVC), and probabilistic neural network (PNN), and CNN. Empirical findings indicate that LBP in conjunction with SVC and CNN obtained high specificity and accuracy, rendering it a promising method for MRI-based tumor diagnosis. Further to investigate the contribution of LBP, Statistical analysis chi-square and p-value tests are used to confirm the significant impact of LBP feature space for identification of brain Tumor. In addition, The SHAP analysis was used to identify the most important features in classification. In a small dataset, CNN obtained 97.8% accuracy while SVC yielded 98.06% accuracy. In subsequent analysis, a large benchmark dataset is also utilized to evaluate the performance of learning algorithms in order to investigate the generalization power of the proposed model. CNN achieves the highest accuracy of 98.9%, followed by SVC at 96.7%. These results highlight CNN's effectiveness in automated, high-precision tumor diagnosis. This achievement is ascribed with MRI-based feature extraction by combining high resolution, non-invasive imaging capabilities with the powerful analytical abilities of CNN. CNN demonstrates superiority in medical imaging owing to its ability to learn intricate spatial patterns and generalize effectively. This interaction enhances the accuracy, speed, and consistency of brain tumor detection, ultimately leading to better patient outcomes and more efficient healthcare delivery. https://github.com/asifrahman557/BrainTumorDetection .

Microinvasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) require distinct treatment strategies and are associated with different prognoses, underscoring the importance of accurate differentiation. This study aims to develop a predictive model that combines radiomics and deep learning to effectively distinguish between MIA and IAC. In this retrospective study, 252 pathologically confirmed cases of ground-glass nodules (GGNs) were included, with 177 allocated to the training set and 75 to the testing set. Radiomics, 2D deep learning, and 3D deep learning models were constructed based on CT images. In addition, two fusion strategies were employed to integrate these modalities: early fusion, which concatenates features from all modalities prior to classification, and late fusion, which ensembles the output probabilities of the individual models. The predictive performance of all five models was evaluated using the area under the receiver operating characteristic curve (AUC), and DeLong's test was performed to compare differences in AUC between models. The radiomics model achieved an AUC of 0.794 (95% CI: 0.684-0.898), while the 2D and 3D deep learning models achieved AUCs of 0.754 (95% CI: 0.594-0.882) and 0.847 (95% CI: 0.724-0.945), respectively, in the testing set. Among the fusion models, the late fusion strategy demonstrated the highest predictive performance, with an AUC of 0.898 (95% CI: 0.784-0.962), outperforming the early fusion model, which achieved an AUC of 0.857 (95% CI: 0.731-0.936). Although the differences were not statistically significant, the late fusion model yielded the highest numerical values for diagnostic accuracy, sensitivity, and specificity across all models. The fusion of radiomics and deep learning features shows potential in improving the differentiation of MIA and IAC in GGNs. The late fusion strategy demonstrated promising results, warranting further validation in larger, multicenter studies.

Acute ischemic stroke (AIS) is a leading cause of death and long-term disability worldwide, where rapid reperfusion remains critical for salvaging brain tissue. Although CT perfusion (CTP) imaging provides essential hemodynamic information, its limitations-including extended processing times, additional radiation exposure, and variable software outputs-can delay treatment. In contrast, non-contrast head CT (NCHCT) is ubiquitously available in acute stroke settings. This study explores a generative artificial intelligence approach to predict key perfusion parameters (relative cerebral blood flow [rCBF] and time-to-maximum [Tmax]) directly from NCHCT, potentially streamlining stroke imaging workflows and expanding access to critical perfusion data. We retrospectively identified patients evaluated for AIS who underwent NCHCT, CT angiography, and CTP. Ground truth perfusion maps (rCBF and Tmax) were extracted from VIZ.ai post-processed CTP studies. A modified pix2pix-turbo generative adversarial network (GAN) was developed to translate co-registered NCHCT images into corresponding perfusion maps. The network was trained using paired NCHCT-CTP data, with training, validation, and testing splits of 80%:10%:10%. Performance was assessed on the test set using quantitative metrics including the structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and Fréchet inception distance (FID). Out of 120 patients, studies from 99 patients fitting our inclusion and exclusion criteria were used as the primary cohort (mean age 73.3 ± 13.5 years; 46.5% female). Cerebral occlusions were predominantly in the middle cerebral artery. GAN-generated Tmax maps achieved an SSIM of 0.827, PSNR of 16.99, and FID of 62.21, while the rCBF maps demonstrated comparable performance (SSIM 0.79, PSNR 16.38, FID 59.58). These results indicate that the model approximates ground truth perfusion maps to a moderate degree and successfully captures key cerebral hemodynamic features. Our findings demonstrate the feasibility of generating functional perfusion maps directly from widely available NCHCT images using a modified GAN. This cross-modality approach may serve as a valuable adjunct in AIS evaluation, particularly in resource-limited settings or when traditional CTP provides limited diagnostic information. Future studies with larger, multicenter datasets and further model refinements are warranted to enhance clinical accuracy and utility.

Considerable socioeconomic disparities exist among pediatric traumatic brain injury (TBI) patients. This study aims to analyze the effects of social determinants of health on head injury outcomes and to create a novel machine-learning algorithm (MLA) that incorporates socioeconomic factors to predict the likelihood of a positive or negative trauma-related finding on head computed tomography (CT). A cohort of blunt trauma patients under age 15 who presented to the largest safety net hospital in New England between January 2006 and December 2013 (n=211) was included in this study. Patient socioeconomic data such as race, language, household income, and insurance type were collected alongside other parameters like Injury Severity Score (ISS), age, sex, and mechanism of injury. Multivariable analysis was performed to identify significant factors in predicting a positive head CT outcome. The cohort was split into 80% training (168 samples) and 20% testing (43 samples) datasets using stratified sampling. Twenty-two multi-parametric MLAs were trained with 5-fold cross-validation and hyperparameter tuning via GridSearchCV, and top-performing models were evaluated on the test dataset. Significant factors associated with pediatric head CT outcome included ISS, age, and insurance type (p<0.05). The age of the subjects with a clinically relevant trauma-related head CT finding (median= 1.8 years) was significantly different from the age of patients without such findings (median= 9.1 years). These predictors were utilized to train the machine learning models. With ISS, the Fine Gaussian SVM achieved the highest test AUC (0.923), with accuracy=0.837, sensitivity=0.647, and specificity=0.962. The Coarse Tree yielded accuracy=0.837, AUC=0.837, sensitivity=0.824, and specificity=0.846. Without ISS, the Narrow Neural Network performed best with accuracy=0.837, AUC=0.857, sensitivity=0.765, and specificity=0.885. Key predictors of clinically relevant head CT findings in pediatric TBI include ISS, age, and social determinants of health, with children under 5 at higher risk. A novel Fine Gaussian SVM model outperformed other MLA, offering high accuracy in predicting outcomes. This tool shows promise for improving clinical decisions while minimizing radiation exposure in children. TBI = Traumatic Brain Injury; ISS = Injury Severity Score; MLA = Machine Learning Algorithm; CT = Computed Tomography; AUC = Area Under the Curve.

To evaluate the post-deployment performance of an artificial intelligence (AI) system (Aidoc) for intracranial hemorrhage (ICH) detection and assess the utility of ChatGPT-4 Turbo for automated AI monitoring. This retrospective study evaluated 332,809 head CT examinations from 37 radiology practices across the United States (December 2023-May 2024). Of these, 13,569 cases were flagged as positive for ICH by the Aidoc AI system. A HIPAA (Health Insurance Portability and Accountability Act) -compliant version of ChatGPT-4 Turbo was used to extract data from radiology reports. Ground truth was established through radiologists' review of 200 randomly selected cases. Performance metrics were calculated for ChatGPT, Aidoc and radiologists. ChatGPT-4 Turbo demonstrated high diagnostic accuracy in identifying intracranial hemorrhage (ICH) from radiology reports, with a positive predictive value of 1 and a negative predictive value of 0.988 (AUC:0.996). Aidoc's false positive classifications were influenced by scanner manufacturer, midline shift, mass effect, artifacts, and neurologic symptoms. Multivariate analysis identified Philips scanners (OR: 6.97, p=0.003) and artifacts (OR: 3.79, p=0.029) as significant contributors to false positives, while midline shift (OR: 0.08, p=0.021) and mass effect (OR: 0.18, p=0.021) were associated with a reduced false positive rate. Aidoc-assisted radiologists achieved a sensitivity of 0.936 and a specificity of 1. This study underscores the importance of continuous performance monitoring for AI systems in clinical practice. The integration of LLMs offers a scalable solution for evaluating AI performance, ensuring reliable deployment and enhancing diagnostic workflows.