Recent advancements in four-dimensional (4D) ultrasonography have enabled detailed observation of fetal behavior <i>in utero</i>, including facial movements, limb gestures, and stimulus responses. These developments have prompted renewed inquiry into whether such behaviors are merely reflexive or represent early signs of integrated neural function. However, the relationship between fetal movement patterns and conscious awareness remains scientifically uncertain and ethically contested. A systematic review was conducted in accordance with PRISMA 2020 guidelines. Four databases (PubMed, Scopus, Embase, Web of Science) were searched for English-language articles published from 2000 to 2025, using keywords including "fetal behavior," "4D ultrasound," "neurodevelopment," and "consciousness." Studies were included if they involved human fetuses, used 4D ultrasound or functional imaging modalities, and offered interpretation relevant to neurobehavioral or ethical analysis. A structured appraisal using AMSTAR-2 was applied to assess study quality. Data were synthesized narratively to map fetal behaviors onto developmental milestones and evaluate their interpretive limits. Seventy-four studies met inclusion criteria, with 23 rated as high-quality. Fetal behaviors such as yawning, hand-to-face movement, and startle responses increased in complexity between 24-34 weeks gestation. These patterns aligned with known neurodevelopmental events, including thalamocortical connectivity and cortical folding. However, no study provided definitive evidence linking observed behaviors to conscious experience. Emerging applications of artificial intelligence in ultrasound analysis were found to enhance pattern recognition but lack external validation. Fetal behavior observed via 4D ultrasound may reflect increasing neural integration but should not be equated with awareness. Interpretations must remain cautious, avoiding anthropomorphic assumptions. Ethical engagement requires attention to scientific limits, sociocultural diversity, and respect for maternal autonomy as imaging technologies continue to evolve.

<b>BACKGROUND</b>. Coronary artery calcium (CAC) scoring is used to stratify acute coronary syndrome (ACS) risk. Nonetheless, patients with a CAC score of zero (CAC₀) remain at risk from noncalcified plaque components. <b>OBJECTIVE</b>. The purpose of this study was to explore CTA-derived coronary artery plaque characteristics in symptomatic patients with CAC₀ who subsequently have ACS through comparisons with patients with a CAC score greater than 0 (CAC_{> 0}) who subsequently have ACS as well as with patients with CAC₀ who do not subsequently have ACS. <b>METHODS</b>. This study entailed a secondary retrospective analysis of prior prospective registry data. The international multicenter CONFIRM (Coronary CT Angiography Evaluation for Clinical Outcomes: An International Multicenter) registry collected longitudinal observational data on symptomatic patients who underwent clinically indicated coronary CTA from January 2004 to May 2010. ICONIC (Incident Coronary Syndromes Identified by CT) was a nested cohort study conducted within CONFIRM that identified patients without known coronary artery disease (CAD) at the time of CTA who did and did not subsequently have ACS (i.e., the ACS and control groups, respectively) and who were propensity matched in a 1:1 ratio on the basis of CAD risk factors and CAD severity on CTA. The present ICONIC substudy selected matched patients in the ACS and control groups who both had documented CAC scores. CTA examinations were analyzed using artificial intelligence software for automated quantitative plaque assessment. In the ACS group, invasive angiography findings were used to identify culprit lesions. <b>RESULTS</b>. The present study included 216 patients (mean age, 55.6 years; 91 women and 125 men), with 108 patients in each of the ACS and control groups. In the ACS group, 23% (<i>n</i> = 25) of patients had CAC₀. In the ACS group, culprit lesions in the subsets of patients with CAC₀ and CAC_{> 0} showed no significant differences in fibrous, fibrofatty, or necrotic-core plaque volumes (<i>p</i> > .05). In the CAC₀ subset, patients with ACS, compared with control patients, had greater mean (± SD) fibrous plaque volume (29.4 ± 42.0 vs 5.5 ± 15.2 mm³, <i>p</i> < .001), fibrofatty plaque volume (27.3 ± 52.2 vs 1.3 ± 3.7 mm³, <i>p</i> < .001), and necrotic-core plaque volume (2.8 ± 6.4 vs 0.0 ± 0.1 mm³, <i>p</i> < .001). <b>CONCLUSION</b>. After propensity-score matching, 23% of patients with ACS had CAC₀. Patients with CAC₀ in the ACS and control groups showed significant differences in volumes of noncalcified plaque components. <b>CLINICAL IMPACT</b>. Methods that identify and quantify noncalcified plaque forms may help characterize ACS risk in symptomatic patients with CAC₀.

To develop and assess the performance of an anatomically based multitask deep learning radiomics nomogram (AMDRN) system to predict implant failure risk before maxillary sinus floor elevation (MSFE) while incorporating automated segmentation of key anatomical structures. We retrospectively collected patients' preoperative cone beam computed tomography (CBCT) images and electronic medical records (EMRs). First, the nn-UNet v2 model was optimized to segment the maxillary sinus (MS), Schneiderian membrane (SM), and residual alveolar bone (RAB). Based on the segmentation mask, a deep learning model (3D-Attention-ResNet) and a radiomics model were developed to extract 3D features from CBCT scans, generating the DL Score, and Rad Score. Significant clinical features were also extracted from EMRs to build a clinical model. These components were then integrated using logistic regression (LR) to create the AMDRN model, which includes a visualization module to support clinical decision-making. Segmentation results for MS, RAB, and SM achieved high DICE coefficients on the test set, with values of 99.50% ± 0.84%, 92.53% ± 3.78%, and 91.58% ± 7.16%, respectively. On an independent test set, the Clinical model, Radiomics model, 3D-DL model, and AMDRN model achieved prediction accuracies of 60%, 76%, 82%, and 90%, respectively, with AMDRN achieving the highest AUC of 93%. The AMDRN system enables efficient preoperative prediction of implant failure risk in MSFE and accurate segmentation of critical anatomical structures, supporting personalized treatment planning and clinical risk management.

The study investigates the correlation between CD3 T-cell expression levels and cervical cancer (CC) while developing a magnetic resonance (MR) imaging-based radiomics model for preoperative prediction of CD3 T-cell expression levels. Prognostic correlations between CD3D, CD3E, and CD3G gene expressions and various cancers were analyzed using the Cancer Genome Atlas (TCGA) database. Protein-protein interaction (PPI) analysis via the STRING database identified associations between these genes and T lymphocyte activity. Gene Set Enrichment Analysis (GSEA) revealed immune pathway enrichment by categorizing genes based on CD3D expression levels. Correlations between immune checkpoint molecules and CD3 complex genes were also assessed. The study retrospectively included 202 patients with pathologically confirmed early-stage CC who underwent preoperative MRI, divided into training and test groups. Radiomic features were extracted from the whole-lesion tumor region of interest (ROI_tumor) and from peritumoral regions with 3 mm and 5 mm margins (ROI_3mm and ROI_5mm, respectively). Various machine learning algorithms, including Support Vector Machine (SVM), Logistic Regression, Random Forest, AdaBoost, and Decision Tree, were used to construct radiomics models based on different ROIs, and diagnostic performances were compared to identify the optimal approach. The best-performing algorithm was combined with intra- and peritumoral features and clinically relevant independent risk factors to develop a comprehensive predictive model. Analysis of the TCGA database demonstrated significant associations between CD3D, CD3E, and CD3G expressions and several cancers, including CC (p < 0.05). PPI analysis highlighted connections between these genes and T lymphocyte function, while GSEA indicated enrichment of immune-related pathways linked to CD3D. Immune checkpoint correlations showed positive associations with CD3 complex genes. Radiomics analysis selected 18 features from ROI_tumor and ROI_3mm across MRI sequences. The SVM algorithm achieved the highest predictive performance for CD3 T-cell expression status, with an area under the curve (AUC) of 0.93 in the training group and 0.92 in the test group. This MR-based radiomics model effectively predicts CD3 expression status in patients with early-stage CC, offering a non-invasive tool for preoperative assessment of CD3 expression, but its clinical utility needs further prospective validation.

Meningitis diagnosis requires a lumbar puncture (LP) to obtain cerebrospinal fluid (CSF) for a laboratory-based analysis. In high-income settings, LPs are part of the systematic approach to screen for meningitis, and most yield negative results. In low- and middle-income settings, LPs are seldom performed, and suspected cases are often treated empirically. The aim of this study was to validate a non-invasive transfontanellar white blood cell (WBC) counter in CSF to screen for meningitis. We conducted a prospective study across three Spanish hospitals, one Mozambican and one Moroccan hospital (2020-2023). We included patients under 24 months with suspected meningitis, an open fontanelle, and a LP performed within 24 h from recruitment. High-resolution-ultrasound (HRUS) images of the CSF were obtained using a customized probe. A deep-learning model was trained to classify CSF patterns based on LPs WBC counts, using a 30cells/mm³ threshold. The algorithm was applied to 3782 images from 76 patients. It correctly classified 17/18 CSFs with <math xmlns="http://www.w3.org/1998/Math/MathML"><mo>≥</mo></math> 30 WBC, and 55/58 controls (sensitivity 94.4%, specificity 94.8%). The only false negative was paired to a traumatic LP with 40 corrected WBC/mm³. This non-invasive device could be an accurate tool for screening meningitis in neonates and young infants, modulating LP indications. Our non-invasive, high-resolution ultrasound device achieved 94% accuracy in detecting elevated leukocyte counts in neonates and infants with suspected meningitis, compared to the gold standard (lumbar punctures and laboratory analysis). This first-in-class screening device introduces the first non-invasive method for neonatal and infant meningitis screening, potentially modulating lumbar puncture indications. This technology could substantially reduce lumbar punctures in low-suspicion cases and provides a viable alternative critically ill patients worldwide or in settings where lumbar punctures are unfeasible, especially in low-income countries).

The study assessed the feasibility of using synthetic data to fine-tune various open-source LLMs for free text to structured data conversation in radiology, comparing their performance with GPT models. A training set of 3000 synthetic thyroid nodule dictations was generated to train six open-source models (Starcoderbase-1B, Starcoderbase-3B, Mistral-7B, Llama-3-8B, Llama-2-13B, and Yi-34B). ACR TI-RADS template was the target model output. The model performance was tested on 50 thyroid nodule dictations from MIMIC-III patient dataset and compared against 0-shot, 1-shot, and 5-shot performance of GPT-3.5 and GPT-4. GPT-4 5-shot and Yi-34B showed the highest performance with no statistically significant difference between the models. Various open models outperformed GPT models with statistical significance. Overall, models trained with synthetic data showed performance comparable to GPT models in structured text conversion in our study. Given privacy preserving advantages, open LLMs can be utilized as a viable alternative to proprietary GPT models.

Noninvasive and precise tools for treatment response estimation in patients with high-risk hepatocellular carcinoma (HCC) who could benefit from hepatic arterial infusion chemotherapy (HAIC) plus lenvatinib and humanized programmed death receptor-1 inhibitors (PD-1) (HAIC-LEN-PD1) are lacking. This study aimed to evaluate the predictive potential of intratumoral and peritumoral radiomics for preoperative treatment response assessment to HAIC-LEN-PD1 in high-risk HCC cases. Totally 630 high-risk HCC cases administered HAIC-LEN-PD1 at three institutions were retrospectively identified and assigned to training, validation and external test sets. Totally 1834 radiomic features were, respectively, obtained from intratumoral and peritumoral regions and radiomics models were established using five classifiers. Based on the optimal model, a nomogram was developed and evaluated using areas under the curves (AUCs), calibration curves and decision curve analysis (DCA). Overall survival (OS) and progression-free survival (PFS) were assessed by Kaplan-Meier curves. The Intratumoral + Peritumoral 10 mm (Intra + Peri10) radiomics models were superior to the intratumor models and peritumor models, with AUCs of 0.919 (95%CI 0.889-0.949) in the training set, 0.874 (95%CI 0.812-0.936) in validation set and 0.893 (95%CI 0.839-0.948) in external test sets. The nomogram had good calibration ability and clinical value, with the AUCs of 0.936 (95%CI 0.907-0.965) in the training set, 0.878 (95%CI 0.916-0.940) in validation set and 0.902 (95%CI 0.848-0.957) in external test sets. The Kaplan-Meier analysis showed that high-score patients had significantly shorter OS and PFS than the low-score patients (median OS: 11.7 vs. 29.6 months, the whole set, p < 0.001; median PFS: 6.0 vs. 12.0 months, the whole set, p < 0.001). The Intra + Peri10 model can effectively predict the treatment response of high-risk HCC cases administered HAIC-LEN-PD1. The nomogram could provide an effective tool to evaluate the treatment response and risk stratification.

Detecting Pulmonary Embolism (PE) is critical for effective patient care, and Artificial Intelligence (AI) has shown promise in supporting radiologists in this task. Integrating AI into radiology workflows requires not only evaluation of its diagnostic accuracy but also assessment of its acceptance among clinical staff. This study aims to evaluate the performance of an AI algorithm in detecting pulmonary embolisms (PEs) on contrast-enhanced computed tomography pulmonary angiograms (CTPAs) and to assess the level of acceptance of the algorithm among radiology department staff. This retrospective study analyzed anonymized computed tomography pulmonary angiography (CTPA) data from a university clinic. Surveys were conducted at three and nine months after the implementation of a commercially available AI algorithm designed to flag CTPA scans with suspected PE. A thoracic radiologist and a cardiac radiologist served as the reference standard for evaluating the performance of the algorithm. The AI analyzed 59 CTPA cases during the initial evaluation and 46 cases in the follow-up assessment. In the first evaluation, the AI algorithm demonstrated a sensitivity of 84.6% and a specificity of 94.3%. By the second evaluation, its performance had improved, achieving a sensitivity of 90.9% and a specificity of 96.7%. Radiologists' acceptance of the AI tool increased over time. Nevertheless, despite this growing acceptance, many radiologists expressed a preference for hiring an additional physician over adopting the AI solution if the costs were comparable. Our study demonstrated high sensitivity and specificity of the AI algorithm, with improved performance over time and a reduced rate of unanalyzed scans. These improvements likely reflect both algorithmic refinement and better data integration. Departmental feedback indicated growing user confidence and trust in the tool. However, many radiologists continued to prefer the addition of a resident over reliance on the algorithm. Overall, the AI showed promise as a supportive "second-look" tool in emergency radiology settings. The AI algorithm demonstrated diagnostic performance comparable to that reported in similar studies for detecting PE on CTPA, with both sensitivity and specificity showing improvement over time. Radiologists' acceptance of the algorithm increased throughout the study period, underscoring its potential as a complementary tool to physician expertise in clinical practice.

Accurate preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) remains challenging. Current imaging biomarkers show limited predictive performance. To develop a deep learning model based on preoperative multiphase CT images of tumors and lesser omental adipose tissue (LOAT) for predicting MVI status and to analyze associated survival outcomes. This retrospective study included pathologically confirmed HCC patients from two medical centers between 2016 and 2023. A dual-branch feature fusion model based on ResNet18 was constructed, which extracted fused features from dual-phase CT images of both tumors and LOAT. The model's performance was evaluated on both internal and external test sets. Logistic regression was used to identify independent predictors of MVI. Based on MVI status, patients in the training, internal test, and external test cohorts were stratified into high- and low-risk groups, and overall survival differences were analyzed. The model incorporating LOAT features outperformed the tumor-only modality, achieving an AUC of 0.889 (95% CI: [0.882, 0.962], P=0.004) in the internal test set and 0.826 (95% CI: [0.793, 0.872], P=0.006) in the external test set. Both results surpassed the independent diagnoses of three radiologists (average AUC=0.772). Multivariate logistic regression confirmed that maximum tumor diameter and LOAT area were independent predictors of MVI. Further Cox regression analysis showed that MVI-positive patients had significantly increased mortality risks in both the internal test set (Hazard Ratio [HR]=2.246, 95% CI: [1.088, 4.637], P=0.029) and external test set (HR=3.797, 95% CI: [1.262, 11.422], P=0.018). This study is the first to use a deep learning framework integrating LOAT and tumor imaging features, improving preoperative MVI risk stratification accuracy. Independent prognostic value of LOAT has been validated in multicenter cohorts, highlighting its potential to guide personalized surgical planning.

Visceral pleural invasion (VPI) indicates poor prognosis in non-small cell lung cancer (NSCLC), and upgrades T classification of NSCLC from T1 to T2 when accompanied by VPI. This study aimed to develop and validate deep learning models for the accurate prediction of VPI in patients with NSCLC, and to compare the performance of two-dimensional (2D), three-dimensional (3D), and hybrid 3D models. This retrospective study included consecutive patients with pathologically confirmed lung tumor between June 2017 and September 2022. The clinical data and preoperative imaging features of these patients were investigated and their relationships with VPI were statistically compared. Elastic fiber staining analysis results were the gold standard for diagnosis of VPI. The data of non-VPI and VPI patients were randomly divided into training cohort and validation cohort based on 8:2 and 6:4, respectively. The EfficientNet-B0_2D model and Double-head Res2Net/_F6/_F24 models were constructed, optimized and verified using two convolutional neural network model architectures-EfficientNet-B0 and Res2Net, respectively, by extracting the features of original CT images and combining specific clinical-CT features. The receiver operating characteristic curve, the area under the curve (AUC), and confusion matrix were utilized to assess the diagnostic efficiency of models. Delong test was used to compare performance between models. A total of 1931 patients with NSCLC were finally evaluated. By univariate analysis, 20 clinical-CT features were identified as risk predictors of VPI. Comparison of the diagnostic efficacy among the EfficientNet-b0_2D, Double-head Res2Net, Res2Net_F6, and Res2Net_F24 combined models revealed that Double-head Res2Net_F6 model owned the largest AUC of 0.941 among all models, followed by Double-head Res2Net (AUC=0.879), Double-head Res2Net_F24 (AUC=0.876), and EfficientNet-b0_2D (AUC=0.785). The three 3D-based models showed comparable predictive performance in the validation cohort and all outperformed the 2D model (EfficientNet-B0_2D, all P＜0.05). It is feasible to predict VPI in NSCLC with the predictive models based on deep learning, and the Double-head Res2Net_F6 model fused with six clinical-CT features showed greatest diagnostic efficacy.