Background: Subarachnoid hemorrhage resulting from cerebral aneurysm rupture is a significant cause of morbidity and mortality. Early identification of aneurysms on Computed Tomography Angiography (CTA), a frequently used modality for this purpose, is crucial, and artificial intelligence (AI)-based algorithms can improve the detection rate and minimize the intra- and inter-rater variability. Thus, a systematic review and meta-analysis were conducted to assess the diagnostic accuracy of deep-learning-based AI algorithms in detecting cerebral aneurysms using CTA. Methods: PubMed (MEDLINE), Embase, and the Cochrane Library were searched from January 2015 to July 2023. Eligibility criteria involved studies using fully automated and semi-automatic deep-learning algorithms for detecting cerebral aneurysms on the CTA modality. Eligible studies were assessed using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. A diagnostic accuracy meta-analysis was conducted to estimate pooled lesion-level sensitivity, size-dependent lesion-level sensitivity, patient-level specificity, and the number of false positives per image. An enhanced FROC curve was utilized to facilitate comparisons between the studies. Results: Fifteen eligible studies were assessed. The findings indicated that the methods exhibited high pooled sensitivity (0.87, 95% confidence interval: 0.835 to 0.91) in detecting intracranial aneurysms at the lesion level. Patient-level sensitivity was not reported due to the lack of a unified patient-level sensitivity definition. Only five studies involved a control group (healthy subjects), whereas two provided information on detection specificity. Moreover, the analysis of size-dependent sensitivity reported in eight studies revealed that the average sensitivity for small aneurysms (<3 mm) was rather low (0.56). Conclusions: The studies included in the analysis exhibited a high level of accuracy in detecting intracranial aneurysms larger than 3 mm in size. Nonetheless, there is a notable gap that necessitates increased attention and research focus on the detection of smaller aneurysms, the use of a common test dataset, and an evaluation of a consistent set of performance metrics.

Introduction: Intracranial aneurysms (IAs) are a common vascular pathology and are associated with a risk of rupture, which is often fatal. Aneurysm growth is considered a surrogate of rupture risk; therefore, the study aimed to develop and evaluate prediction models of future artificial intelligence (AI) growth based on baseline aneurysm morphology as a computer-aided treatment decision support.Materials and methods: Follow-up CT angiography (CTA) and magnetic resonance angiography (MRA) angiograms of 39 patients with 44 IAs were classified by an expert as growing and stable (25/19). From the angiograms vascular surface meshes were extracted and the aneurysm shape was characterized by established morphologic features and novel deep shape features. The features corresponding to the baseline aneurysms were used to predict future aneurysm growth using univariate thresholding, multivariate random forest and multi-layer perceptron (MLP) learning, and deep shape learning based on the PointNet++ model.Results: The proposed deep shape feature learning method achieved an accuracy of 0.82 (sensitivity = 0.96, specificity = 0.63), while the multivariate learning and univariate thresholding methods were inferior with an accuracy of up to 0.68 and 0.63, respectively.Conclusion: High-performing classification of future growing IAs renders the proposed deep shape features learning approach as the key enabling tool to manage rupture risk in the “no treatment” paradigm of patient follow-up imaging.

Age estimation plays a crucial role in various fields, including forensic science and anthropology. This study aims to develop and validate DentAge, a deep‐learning model for automated age prediction using panoramic dental X‐ray images. DentAge was trained on a dataset comprising 21,007 panoramic dental X‐ray images sourced from a private dental center in Slovenia. The dataset included subjects aged 4 to 97 years with various dental conditions. Transfer learning was employed, initializing the model with ImageNet weights and fine‐tuning on the dental image dataset. The model was trained using stochastic gradient descent with momentum, and mean absolute error (MAE) served as the objective function. Across the test dataset, DentAge achieved an MAE of 3.12 years, demonstrating its efficacy in age prediction. Notably, the model performed well across different age groups, with MAEs ranging from 1.94 (age group [10–20]) to 13.40 years (age group [90–100]). Visual evaluation revealed factors contributing to prediction errors, including prosthetic restorations, tooth loss, and bone resorption. DentAge represents a significant advancement in automated age prediction within dentistry. The model's robust performance across diverse age groups and dental conditions underscores its potential utility in real‐world scenarios. Our model will be accessible to the public for further adjustments and validation, ensuring DentAge's effectiveness and trustworthiness in practical scenarios.

Background Early detection of intracranial aneurysms (IAs) is crucial for patient outcomes. Typically identified on angiographic scans such as CT angiography (CTA) or MR angiography (MRA), the sensitivity of experts in studies on small IAs (diameter <3 mm) was moderate (64–74.1% for CTAs and 70–92.8% for MRAs), and these figures could be lower in a routine clinical setting. Recent research shows that the expert level of sensitivity might be achieved using deep learning approaches. Methods A large multisite dataset including 1054 MRA and 2174 CTA scans with expert IA annotations was collected. A novel modality-agnostic two-step IA detection approach was proposed. The first step used nnU-Net for segmenting vascular structures, with model training performed separately for each modality. In the second step, segmentations were converted to vascular surface that was parcellated by sampling point clouds and, using a PointNet++ model, each point was labeled as an aneurysm or vessel class. Results Quantitative validation of the test data from different sites than the training data showed that the proposed approach achieved pooled sensitivity of 85% and 90% on 157 MRA scans and 1338 CTA scans, respectively, while the sensitivity for small IAs was 72% and 83%, respectively. The corresponding number of false findings per image was low at 1.54 and 1.57, and 0.4 and 0.83 on healthy subject data. Conclusions The proposed approach achieved a state-of-the-art balance between the sensitivity and the number of false findings, matched the expert-level sensitivity to small (and other) IAs on external data, and therefore seems fit for computer-assisted detection of IAs in a clinical setting.

Intracranial aneurysms (IAs) are common vascular pathologies with a risk of fatal rupture. Human assessment of rupture risk is error prone, and treatment decision for unruptured IAs often rely on expert opinion and institutional policy. Therefore, we aimed to develop a computer-assisted aneurysm rupture prediction framework to help guide the decision-making process and create future decision criteria. This retrospective study included 335 patients with 500 IAs, of the 500 IAs studied, 250 were labeled as ruptured and 250 as unruptured. A skilled radiologist and a neurosurgeon visually examined the computed tomography angiography (CTA) images and labeled the IAs. For external validation we included 24 IAs, 10 ruptured and 15 unruptured, imaged with 3D rotational angiography (3D-RA) from the Aneurisk dataset. The pretrained nnU-net model was used for automated vessel segmentation, which was fed to pretrained PointNet++ models for vessel labeling and aneurysm segmentation. From these the latent keypoint representations were extracted as vessel shape and aneurysm shape features, respectively. Additionally, conventional features such as IAs morphological measurements, location and patient data, such as age, sex, were used for training and testing eight machine learning models for rupture status classification. The top-performing model, a random forest with feature selection, achieved an area under the receiver operating curve of 0.851, an accuracy of 0.782, a sensitivity of 0.804, and a specificity of 0.760. This model used 14 aneurysm shape features, seven conventional features, and one vessel shape feature. On the external dataset, it achieved an AUC of 0.805. While aneurysm shape features consistently contributed significantly across the classification models, vessel shape features contributed a small portion. Our proposed automated artificial intelligence framework could assist in clinical decision-making by assessing aneurysm rupture risk using screening tests, such as CTA and 3D-RA.