Artificial intelligence (AI), particularly deep learning, has demonstrated high diagnostic performance in detecting intracranial aneurysms on computed tomography angiography (CTA) and magnetic resonance angiography (MRA). However, the clinical translation of these technologies remains limited due to methodological limitations and concerns about generalizability. This scoping review comprehensively evaluates 36 studies that applied deep learning to intracranial aneurysm detection on CTA or MRA, focusing on study design, validation strategies, reporting practices, and reference standards. Key findings include inconsistent handling of ruptured and previously treated aneurysms, underreporting of coexisting brain or vascular abnormalities, limited use of external validation, and an almost complete absence of prospective study designs. Only a minority of studies employed diagnostic cohorts that reflect real-world aneurysm prevalence, and few reported all essential performance metrics, such as patient-wise and lesion-wise sensitivity, specificity, and false positives per case. These limitations suggest that current studies remain at the stage of technical validation, with high risks of bias and limited clinical applicability. To facilitate real-world implementation, future research must adopt more rigorous designs, representative and diverse validation cohorts, standardized reporting practices, and greater attention to human-AI interaction.

The determination of the mid-sagittal plane (MSP) on three-dimensional (3D) head imaging is key to the assessment of facial asymmetry. The aim of this study was to evaluate the reliability of an automated landmark-based MSP to quantify mandibular asymmetry on head computed tomography (CT) scans. A dataset of 368 CT scans, including orthognathic surgery patients, was automatically annotated with 3D cephalometric landmarks via a previously published deep learning-based method. Five of these landmarks were used to automatically construct an MSP orthogonal to the Frankfurt horizontal plane. The reliability of automatic MSP construction was compared with the reliability of manual MSP construction based on 6 manual localizations by 3 experienced operators on 19 randomly selected CT scans. The mandibular asymmetry of the 368 CT scans with respect to the MSP was calculated and compared with clinical expert judgment. The construction of the MSP was found to be highly reliable, both manually and automatically. The manual reproducibility 95% limit of agreement was less than 1 mm for -y translation and less than 1.1° for -x and -z rotation, and the automatic measurement lied within the confidence interval of the manual method. The automatic MSP construction was shown to be clinically relevant, with the mandibular asymmetry measures being consistent with the expertly assessed levels of asymmetry. The proposed automatic landmark-based MSP construction was found to be as reliable as manual construction and clinically relevant in assessing the mandibular asymmetry of 368 head CT scans. Once implemented in a clinical software, fully automated landmark-based MSP construction could be clinically used to assess mandibular asymmetry on head CT scans.

Deep learning has been utilized to segment teeth from computed tomography (CT) or cone-beam CT (CBCT). However, the performance of deep learning is unknown due to multiple models and diverse evaluation metrics. This systematic review and meta-analysis aims to evaluate the evolution and performance of deep learning in tooth segmentation. We systematically searched PubMed, Web of Science, Scopus, IEEE Xplore, arXiv.org, and ACM for studies investigating deep learning in human tooth segmentation from CT/CBCT. Included studies were assessed using the Quality Assessment of Diagnostic Accuracy Study (QUADAS-2) tool. Data were extracted for meta-analyses by random-effects models. A total of 30 studies were included in the systematic review, and 28 of them were included for meta-analyses. Various deep learning algorithms were categorized according to the backbone network, encompassing single-stage convolutional models, convolutional models with U-Net architecture, Transformer models, convolutional models with attention mechanisms, and combinations of multiple models. Convolutional models with U-Net architecture were the most commonly used deep learning algorithms. The integration of attention mechanism within convolutional models has become a new topic. 29 evaluation metrics were identified, with Dice Similarity Coefficient (DSC) being the most popular. The pooled results were 0.93 [0.93, 0.93] for DSC, 0.86 [0.85, 0.87] for Intersection over Union (IoU), 0.22 [0.19, 0.24] for Average Symmetric Surface Distance (ASSD), 0.92 [0.90, 0.94] for sensitivity, 0.71 [0.26, 1.17] for 95% Hausdorff distance, and 0.96 [0.93, 0.98] for precision. No significant difference was observed in the segmentation of single-rooted or multi-rooted teeth. No obvious correlation between sample size and segmentation performance was observed. Multiple deep learning algorithms have been successfully applied to tooth segmentation from CT/CBCT and their evolution has been well summarized and categorized according to their backbone structures. In future, studies are needed with standardized protocols and open labelled datasets.

To develop and validate a machine learning model based on dual-energy computed tomography (DECT) for predicting cervical lymph node metastases (CLNM) in patients diagnosed with nasopharyngeal carcinoma (NPC). This prospective single-center study enrolled patients with NPC and the study assessment included both DECT and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT). Radiomics features were extracted from each region of interest (ROI) for cervical lymph nodes using arterial and venous phase images at 100 keV and 150 keV, either individually as non-fusion models or combined as fusion models on the DECT images. The performance of the random forest (RF) models, combined with radiomics features, was evaluated by area under the receiver operating characteristic curve (AUC) analysis. DeLong's test was employed to compare model performances, while decision curve analysis (DCA) assessed the clinical utility of the predictive models. Sixty-six patients with NPC were included for analysis, which was divided into a training set (n = 42) and a validation set (n = 22). A total of 13 radiomic models were constructed (4 non-fusion models and 9 fusion models). In the non-fusion models, when the threshold value exceeded 0.4, the venous phase at 100 keV (V100) (AUC, 0.9667; 95 % confidence interval [95 % CI], 0.9363-0.9901) model exhibited a higher net benefit than other non-fusion models. The V100 + V150 fusion model achieved the best performance, with an AUC of 0.9697 (95 % CI, 0.9393-0.9907). DECT-based radiomics effectively diagnosed CLNM in patients with NPC and may potentially be a valuable tool for clinical decision-making. This study improved pre-operative evaluation, treatment strategy selection, and prognostic evaluation for patients with nasopharyngeal carcinoma by combining DECT and radiomics to predict cervical lymph node status prior to treatment.

The risks and prognosis of mild intracerebral hemorrhage (ICH) patients were easily overlooked by clinicians. Our goal was to use machine learning (ML) methods to predict mild ICH patients' neurological deterioration (ND) and 90-day prognosis. This prospective study recruited 257 patients with mild ICH for this study. After exclusions, 148 patients were included in the ND study and 144 patients in the 90-day prognosis study. We trained five ML models using filtered data, including clinical, traditional imaging, and radiomics indicators based on non-contrast computed tomography (NCCT). Additionally, we incorporated the Shapley Additive Explanation (SHAP) method to display key features and visualize the decision-making process of the model for each individual. A total of 21 (14.2%) mild ICH patients developed ND, and 35 (24.3%) mild ICH patients had a 90-day poor prognosis. In the validation set, the support vector machine (SVM) models achieved an AUC of 0.846 (95% confidence intervals (CI), 0.627-1.000) and an F1-score of 0.667 for predicting ND, and an AUC of 0.970 (95% CI, 0.928-1.000), and an F1-score of 0.846 for predicting 90-day prognosis. The SHAP analysis results indicated that several clinical features, the island sign, and the radiomics features of the hematoma were of significant value in predicting ND and 90-day prognosis. The ML models, constructed using clinical, traditional imaging, and radiomics indicators, demonstrated good classification performance in predicting ND and 90-day prognosis in patients with mild ICH, and have the potential to serve as an effective tool in clinical practice. Not applicable.

Data regarding the diagnostic efficacy of radial endobronchial ultrasound (R-EBUS) findings obtained via transbronchial needle aspiration (TBNA)/biopsy (TBB) with endobronchial ultrasonography with a guide sheath (EBUS-GS) for peripheral pulmonary lesions (PPLs) are lacking. We evaluated whether intraoperative probe repositioning improves R-EBUS imaging and affects diagnostic yield and safety of EBUS-guided sampling for PPLs. We retrospectively studied 363 patients with PPLs who underwent TBNA/TBB (83 lesions) or TBB (280 lesions) using EBUS-GS. Based on the R-EBUS findings before and after these procedures, patients were categorized into three groups: the improved R-EBUS image (n = 52), unimproved R-EBUS image (n = 69), and initial within-lesion groups (n = 242). The impact of improved R-EBUS findings on diagnostic yield and complications was assessed using multivariable logistic regression, adjusting for lesion size, lesion location, and the presence of a bronchus leading to the lesion on CT. A separate exploratory random-forest model with SHAP analysis was used to explore factors associated with successful repositioning in lesions not initially "within." The diagnostic yield in the improved R-EBUS group was significantly higher than that in the unimproved R-EBUS group (76.9% vs. 46.4%, p = 0.001). The regression model revealed that the improvement in intraoperative R-EBUS findings was associated with a high diagnostic yield (odds ratio: 3.55, 95% confidence interval, 1.57-8.06, p = 0.002). Machine learning analysis indicated that inner lesion location and radiographic visibility were the most influential predictors of successful repositioning. The complication rates were similar across all groups (total complications: 5.8% vs. 4.3% vs. 6.2%, p = 0.943). Improved R-EBUS findings during TBNA/TBB or TBB with EBUS-GS were associated with a high diagnostic yield without an increase in complications, even when the initial R-EBUS findings were inadequate. This suggests that repeated intraoperative probe repositioning can safely boost outcomes.

Since coronary artery disease (CAD) is a common comorbidity in patients with aortic valve stenosis, invasive coronary angiography (ICA) can be avoided if significant CAD can be screened with the non-invasive coronary CT angiography (cCTA). This study aims to evaluate the ability of machine learning-based CT coronary fractional flow reserve (CT-FFR) derived from cCTA to aid in the diagnosis of comorbid CAD in patients undergoing transcatheter aortic valve implantation (TAVI). A total of 100 patients who underwent both cCTA and ICA assessments prior to TAVI procedure between January 2021 and July 2023 were included. Coronary stenosis was assessed using both cCTA data and machine learning-generated CT-FFR image information for patients/major coronary vessels. Coronary lesions with CT-FFR ≤ 0.80 were defined as hemodynamically significant, with ICA serving as the diagnostic gold standard. A total of 400 major coronary vessels were identified in 100 eligible patients who underwent TAVI. CT-FFR was 86.4% sensitive and 66.1% specific to diagnose CAD, with a positive predictive value (PPV) of 66.7% and a negative predictive value (NPV) of 86.0%. The diagnostic accuracy (Acc) was 75.0%, with a false positive rate (FPR) of 33.9%. At the vessel level, CT-FFR showed a sensitivity of 77.6% and a specificity of 76.9%. The PPV was 44.0% and the NPV was 93.6%. The Acc was 77.0% and the FPR was 23.1%. For all patient/vessel units, CT-FFR outperformed cCTA. Machine learning-based CT-FFR can effectively detect coronary hemodynamic abnormalities. Combined with preoperative cCTA in TAVI patients, it is an effective tool to rule out significant CAD, reducing unnecessary coronary angiography in this high-risk population. Not applicable.

To develop a deep learning network for extracting features from the blood-supplying regions of the lenticulostriate artery (LSA) and to establish these features as an imaging biomarker for the comprehensive assessment of the lenticulostriate artery-neural complex (LNC). Automatic segmentation of brain regions on T1-weighted images was performed, followed by the development of the ResNet18 framework to extract and visualize deep learning features from three regions of interest (ROIs). The root mean squared error (RMSE) was then used to assess the correlation between these features and fractional anisotropy (FA) values from diffusion tensor imaging (DTI) and cerebral blood flow (CBF) values from arterial spin labeling (ASL). The correlation of these features with LSA root numbers and three disease categories was further validated using fine-tuning classification (Task1 and Task2). Seventy-nine patients were enrolled and classified into three groups. No significant differences were found in the number of LSA roots between the right and left hemispheres, nor in the FA and CBF values of the ROIs. The RMSE loss, relative to the mean FA and CBF values across different ROI inputs, ranged from 0.154 to 0.213%. The model's accuracy in Task1 and Task2 fine-tuning classification reached 100%. Deep learning features extracted from the basal ganglia nuclei effectively reflect cerebrovascular and neurological functions and reveal the damage status of the LSA. This approach holds promise as a novel imaging biomarker for the comprehensive assessment of the LNC.

Major Depressive Disorder (MDD) is a prevalent mental health condition with significant societal impact. Structural magnetic resonance imaging (sMRI) and machine learning have shown promise in psychiatry, offering insights into brain abnormalities in MDD. However, predicting treatment response remains challenging. This study leverages inter-brain similarity from sMRI as a novel feature to enhance prediction accuracy and explore disease mechanisms. The method's generalizability across adult and adolescent cohorts is also evaluated. The study included 172 participants. Based on remission status, 39 participants from the Hangzhou Dataset and 34 from the Jinan Dataset were selected for further analysis. Three methods were used to extract brain similarity features, followed by a statistical test for feature selection. Six machine learning classifiers were employed to predict treatment response, and their generalizability was tested using the Jinan Dataset. Group analyses between remission and non-remission groups were conducted to identify brain regions associated with treatment response. Brain similarity features outperformed traditional metrics in predicting treatment outcomes, with the highest accuracy achieved by the model using these features. Between-group analyses revealed that the remission group had lower gray matter volume and density in the right precentral gyrus, but higher white matter volume (WMV). In the Jinan Dataset, significant differences were observed in the right cerebellum and fusiform gyrus, with higher WMV and density in the remission group. This study demonstrates that brain similarity features combined with machine learning can predict treatment response in MDD with moderate success across age groups. These findings emphasize the importance of considering age-related differences in treatment planning to personalize care. Clinical trial number: not applicable.