This study presents a fully automated digital workflow using artificial intelligence (AI) to create patient-specific cutting guides for mandible-angle osteotomies in facial feminization surgery (FFS). The goal is to achieve predictable, accurate, and safe results with minimal user input, addressing the time and effort required for conventional guide creation. Three-dimensional CT images of 30 male patients were used to develop and validate a workflow that automates two key processes: (1) segmentation of the mandible using a convolutional neural network (3D U-Net architecture) and (2) virtual design of osteotomy-specific cutting guides. Segmentation accuracy was assessed through comparison with expert manual segmentations using the dice similarity coefficient (DSC) and mean surface distance (MSD). The precision of the cutting guides was evaluated based on osteotomy line accuracy and fit. Workflow efficiency was measured by comparing the time required for automated versus manual planning by expert and novice users. The AI-based workflow achieved a median DSC of 0.966 and a median MSD of 0.212 mm, demonstrating high accuracy. The median planning time was reduced to 1 min and 38 s with the automated system, compared to 19 min and 37 s for an expert and 26 min and 39 s for a novice, representing 10- and 16-fold time reductions, respectively. The AI-based workflow is accurate, efficient, and cost-effective, significantly reducing planning time while maintaining clinical precision. This workflow improves surgical outcomes with precise and reliable cutting guides, enhancing efficiency and accessibility for clinicians, including those with limited experience in designing cutting guides.

Precise 3D segmentation of paranasal sinuses is essential for accurate diagnosis and treatment. This study aimed to develop a fully automated segmentation algorithm for the paranasal sinuses using the nnU-Net v2 architecture. The nnU-Net v2-based segmentation algorithm was developed using Python 3.6.1 and the PyTorch library, and its performance was evaluated on a dataset of 97 cone-beam computed tomography (CBCT) scans. Ground truth annotations were manually generated by expert radiologists using the 3D Slicer software, employing a polygonal labeling technique across sagittal, coronal, and axial planes. Model performance was assessed using several quantitative metrics, including accuracy, Dice Coefficient (DC), sensitivity, precision, Jaccard Index, Area Under the Curve (AUC), and 95% Hausdorff Distance (95% HD). The nnU-Net v2-based algorithm demonstrated high segmentation performance across all paranasal sinuses. Dice Coefficient (DC) values were 0.94 for the frontal, 0.95 for the sphenoid, 0.97 for the maxillary, and 0.88 for the ethmoid sinuses. Accuracy scores exceeded 99% for all sinuses. The 95% Hausdorff Distance (95% HD) values were 0.51 mm for both the frontal and maxillary sinuses, 0.85 mm for the sphenoid sinus, and 1.17 mm for the ethmoid sinus. Jaccard indices were 0.90, 0.91, 0.94, and 0.80, respectively. This study highlights the high accuracy and precision of the nnU-Net v2-based CNN model in the fully automated segmentation of paranasal sinuses from CBCT images. The results suggest that the proposed model can significantly contribute to clinical decision-making processes, facilitating diagnostic and therapeutic procedures.

Cystic fibrosis (CF) is a prevalent autosomal recessive disorder, with lung complications being the primary cause of morbidity and mortality. In paediatric patients, structural lung changes begin early, necessitating prompt detection to guide treatment and delay disease progression. This study evaluates ultra-low-dose CT (ULDCT) versus chest x-rays  (CXR) for children with CF (CwCF) lung disease assessment. ULDCT uses AI-enhanced deep-learning iterative reconstruction to achieve radiation doses comparable to a CXR. This prospective study recruited radiographers and radiologists to assess the image quality (IQ) of ten paired ULDCT and CXR images of CwCF from a single centre. Statistical analyses, including the Wilcoxon Signed Rank test and visual grading characteristic (VGC) analysis, compared diagnostic confidence and anatomical detail. Seventy-five participants were enrolled, 25 radiologists and 50 radiographers. The majority (88%) preferred ULDCT over CXR for monitoring CF lung disease due to higher perceived confidence (p ≤ 0.001) and better IQ ratings (p ≤ 0.05), especially among radiologists (area under the VGC curve and its 95% CI was 0.63 (asymmetric 95% CI: 0.51-0.73; p ≤ 0.05). While ULDCT showed no significant differences in anatomical visualisation compared to CXR, the overall IQ for lung pathology assessment was rated superior. ULDCT offers superior IQ over CXR in CwCF, with similar radiation doses. It also enhances diagnostic confidence, supporting its use as a viable CXR alternative. Standardising CT protocols to optimise IQ and minimise radiation is essential to improve disease monitoring in this vulnerable group. Question How does chest X-ray (CXR) IQ in children compare to ULDCT at similar radiation doses for assessing CF-related lung disease? Findings ULDCT offers superior IQ over CXR in CwCF. Participants preferred ULDCT due to higher perceived confidence levels and superior IQ. Clinical relevance ULDCT can enhance diagnosis in CwCF while maintaining comparable radiation doses. ULDCT also enhances diagnostic confidence, supporting its use as a viable CXR alternative.

Lung cancer is the leading cause of cancer-related mortality worldwide, largely due to late-stage diagnosis. Low-dose computed tomography (LDCT) screening has emerged as a powerful tool for early detection, enabling diagnosis at curable stages and reducing lung cancer mortality. Despite strong evidence, LDCT screening uptake remains suboptimal globally. This review synthesizes current evidence supporting LDCT screening, highlights ongoing global implementation efforts, and discusses key insights from the 1st AGILE conference. Lung cancer screening is gaining global momentum, with many countries advancing plans for national LDCT programs. Expanding eligibility through risk-based models and targeting high-risk never- and light-smokers are emerging strategies to improve efficiency and equity. Technological advancements, including AI-assisted interpretation and image-based biomarkers, are addressing concerns around false positives, overdiagnosis, and workforce burden. Integrating cardiac and smoking-related disease assessment within LDCT screening offers added preventive health benefits. To maximize global impact, screening strategies must be tailored to local health systems and populations. Efforts should focus on increasing awareness, standardizing protocols, optimizing screening intervals, and strengthening multidisciplinary care pathways. International collaboration and shared infrastructure can accelerate progress and ensure sustainability. LDCT screening represents a cost-effective opportunity to reduce lung cancer mortality and premature deaths.

Assessing dimensions of available bone throughout hundreds of cone-beam computed tomography cross-sectional images of the edentulous area is time-consuming, focus-demanding, and prone to variability and mistakes. This study aims for a clinically applicable artificial intelligence-based automation system for available bone quantity assessment and providing possible surgical and nonsurgical treatment options in a real-time manner. YOLOv8-seg, a single-stage convolutional neural network detector, has been used to segment mandibular alveolar bone and the inferior alveolar canal from cross-sectional images of a custom dataset. Measurements from the segmented mask of the bone and canal have been calculated mathematically and compared with manual measurements from 2 different operators, and the time for the measurement task has been compared. Classification of bone dimension with 25 treatment options has been automatically suggested by the system and validated with a team of specialists. The YOLOv8 model achieved significantly accurate improvements in segmenting anatomical structures with a precision of 0.951, recall of 0.915, mAP50 of 0.952, Intersection over Union of 0.871, and dice similarity coefficient of 0.911. The efficiency ratio of that segmentation performed by the artificial intelligence-based system is 2001 times faster in comparison to the human subject. A statistically significant difference in the measurements from the system to operators in height and time is recorded. The system's recommendations matched the clinicians' assessments in 94% of cases (83/88). Cohen κ of 0.89 indicated near-perfect agreement. The YOLOv8 model is an effective tool, providing high accuracy in segmenting dental structures with balanced computational requirements, and even with the challenges presented, the system can be clinically applicable with future improvements, providing less time-consuming and, most importantly, specialist-level accurate implant planning reports.

Magnetic resonance imaging (MRI) is the gold standard for diagnosing disc displacement in temporomandibular joint (TMJ) disorders, but its high cost and practical challenges limit its accessibility. This study aimed to develop a machine learning (ML) model that can predict TMJ disc displacement using only cone-beam computed tomography (CBCT)-based radiomics features without MRI. CBCT images of 247 mandibular condyles from 134 patients who also underwent MRI scans were analyzed. To conduct three experiments based on the classification of various patient groups, we trained two ML models, random forest (RF) and extreme gradient boosting (XGBoost). Experiment 1 classified the data into three groups: Normal, disc displacement with reduction (DDWR), and disc displacement without reduction (DDWOR). Experiment 2 classified Normal versus disc displacement group (DDWR and DDWOR), and Experiment 3 classified Normal and DDWR versus DDWOR group. The RF model showed higher performance than XGBoost across all three experiments, and in particular, Experiment 3, which differentiated DDWOR from other conditions, achieved the highest accuracy with an area under the receiver operating characteristic curve (AUC) values of 0.86 (RF) and 0.85 (XGBoost). Experiment 2 followed with AUC values of 0.76 (RF) and 0.75 (XGBoost), while Experiment 1, which classified all three groups, had the lowest accuracy of 0.63 (RF) and 0.59 (XGBoost). The RF model, utilizing radiomics features from CBCT images, demonstrated potential as an assistant tool for predicting DDWOR, which requires the most careful management.

Lung cancer is the leading cause of cancer-related mortality. While early detection improves survival, distinguishing malignant from benign pulmonary nodules remains challenging. Artificial intelligence (AI) has been proposed to enhance diagnostic accuracy, but its clinical reliability is still under investigation. Here, we aimed to evaluate the diagnostic performance of AI models in classifying pulmonary nodules. This single-center retrospective study analyzed pulmonary nodules (4-30 mm) detected on CT scans, using three AI software models. Sensitivity, specificity, false-positive and false-negative rates were calculated. The diagnostic accuracy was assessed using the area under the receiver operating characteristic (ROC) curve (AUC), with histopathology serving as the gold standard. Subgroup analyses were based on nodule size and histopathological classification. The impact of imaging parameters was evaluated using regression analysis. A total of 158 nodules (n = 30 benign, n = 128 malignant) were analyzed. One AI model classified most nodules as intermediate risk, preventing further accuracy assessment. The other models demonstrated moderate sensitivity (53.1-70.3%) but low specificity (46.7-66.7%), leading to a high false-positive rate (45.5-52.4%). AUC values were between 0.5 and 0.6 (95% CI). Subgroup analyses revealed decreased sensitivity (47.8-61.5%) but increased specificity (100%), highlighting inconsistencies. In total, up to 49.0% of the pulmonary nodules were classified as intermediate risk. CT scan type influenced performance (p = 0.03), with better classification accuracy on breath-held CT scans. AI-based software models are not ready for standalone clinical use in pulmonary nodule classification due to low specificity, a high false-negative rate and a high proportion of intermediate-risk classifications. Question How accurate are commercially available AI models for the classification of pulmonary nodules compared to the gold standard of histopathology? Findings The evaluated AI models demonstrated moderate sensitivity, low specificity and high false-negative rates. Up to 49% of pulmonary nodules were classified as intermediate risk. Clinical relevance The high false-negative rates could influence radiologists' decision-making, leading to an increased number of interventions or unnecessary surgical procedures.

Photon counting detector CT myelography is an effective modality for the localization of spinal CSF leaks. The initial studies describing this technique employed a relatively smooth Br56 kernel. However, subsequent studies have demonstrated that the use of the sharpest quantitative kernel on photon counting CT (Qr89), particularly when denoised with techniques such as quantum iterative reconstruction or convolutional neural networks, enhances detection of CSF-venous fistulas. In this clinical report, we sought to determine whether the Qr89 kernel has utility in patients with dural tears, the other main type of spinal CSF leak. We performed a retrospective review of patients with dural tears diagnosed on photon counting CT myelography, comparing Br56, Qr89 denoised with quantum iterative reconstruction, and Qr89 denoised with a trained convolutional neural network. We specifically assessed spatial resolution, noise level, and diagnostic confidence in eight such cases, finding that the sharper Qr89 kernel outperformed the smoother Br56 kernel. This was particularly true when Qr89 was denoised using a convolutional neural network. Furthermore, in two cases, the dural tear was only seen on the Qr89 reconstructions and missed on the Br56 kernel. Overall, our study demonstrates the potential value of further optimizing post-processing techniques for photon counting CT myelography aimed at localizing dural tears.ABBREVIATIONS: CNN = convolutional neural network; CVF = CSF-venous fistula; DSM = digital subtraction myelography; EID = energy integrating detector; PCD = photon counting detector; QIR = quantum iterative reconstruction.

Identifying whether there is a traumatic intracranial bleed (ICB+) on head CT is critical for clinical care and research. Free text CT reports are unstructured and therefore must undergo time-consuming manual review. Existing artificial intelligence classification schemes are not optimised for the emergency department endpoint of classification of ICB+ or ICB-. We sought to assess three methods for classifying CT reports: a text classification (TC) programme, a commercial natural language processing programme (Clinithink) and a generative pretrained transformer large language model (Digitalizing English-language CT Interpretation for Positive Haemorrhage Evaluation Reporting (DECIPHER)-LLM). Primary objective: determine the diagnostic classification performance of the dichotomous categorisation of each of the three approaches. determine whether the LLM could achieve a substantial reduction in CT report review workload while maintaining 100% sensitivity.Anonymised radiology reports of head CT scans performed for trauma were manually labelled as ICB+/-. Training and validation sets were randomly created to train the TC and natural language processing models. Prompts were written to train the LLM. 898 reports were manually labelled. Sensitivity and specificity (95% CI)) of TC, Clinithink and DECIPHER-LLM (with probability of ICB set at 10%) were respectively 87.9% (76.7% to 95.0%) and 98.2% (96.3% to 99.3%), 75.9% (62.8% to 86.1%) and 96.2% (93.8% to 97.8%) and 100% (93.8% to 100%) and 97.4% (95.3% to 98.8%).With DECIPHER-LLM probability of ICB+ threshold of 10% set to identify CT reports requiring manual evaluation, CT reports requiring manual classification reduced by an estimated 385/449 cases (85.7% (95% CI 82.1% to 88.9%)) while maintaining 100% sensitivity. DECIPHER-LLM outperformed other tested free-text classification methods.


Intrathoracic airway segmentation in computed tomography (CT) is important for quantitative and qualitative analysis of various chronic respiratory diseases and bronchial surgery navigation. However, the airway tree's morphological complexity, incomplete labels resulting from annotation difficulty, and intra-class imbalance between main and terminal airways limit the segmentation performance.
Methods:
Three methodological improvements are proposed to deal with the challenges. Firstly, we design a DCT-UNet to collect better information on neighbouring voxels and ones within a larger spatial region. Secondly, an airway label self-updating (ALSU) strategy is proposed to iteratively update the reference labels to conquer the problem of incomplete labels. Thirdly, a deep learning-based terminal region growing (TRG) is adopted to extract terminal airways. Extensive experiments were conducted on two internal datasets and three public datasets.
Results:
Compared to the counterparts, the proposed method can achieve a higher Branch Detected, Tree-length Detected, Branch Ratio, and Tree-length Ratio (ISICDM2021 dataset, 95.19%, 94.89%, 166.45%, and 172.29%; BAS dataset, 96.03%, 95.11%, 129.35%, and 137.00%). Ablation experiments show the effectiveness of three proposed solutions. Our method is applied to an in-house Chorionic Obstructive Pulmonary Disease (COPD) dataset. The measures of branch count, tree length, endpoint count, airway volume, and airway surface area are significantly different between COPD severity stages.
Conclusions:
The proposed methods can segment more terminal bronchi and larger length of airway, even some bronchi which are real but missed in the manual annotation can be detected. Potential application significance has been presented in characterizing COPD airway lesions and severity stages.&#xD.