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Automated detection of zygomatic fractures on spiral computed tomography using a deep learning model.

Yari A, Fasih P, Kamali Hakim L, Asadi A

pubmed logopapersAug 6 2025
The aim of this study was to evaluate the performance of the YOLOv8 deep learning model for detecting zygomatic fractures. Computed tomography scans with zygomatic fractures were collected, with all slices annotated to identify fracture lines across seven categories: zygomaticomaxillary suture, zygomatic arch, zygomaticofrontal suture, sphenozygomatic suture, orbital floor, zygomatic body, and maxillary sinus wall. The images were divided into training, validation, and test datasets in a 6:2:2 ratio. Performance metrics were calculated for each category. A total of 13,988 axial and 14,107 coronal slices were retrieved. The trained algorithm achieved accuracy of 94.2-97.9%. Recall exceeded 90% across all categories, with sphenozygomatic suture fractures having the highest value (96.6%). Average precision was highest for zygomatic arch fractures (0.827) and lowest for zygomatic body fractures (0.692). The highest F1 score was 96.7% for zygomaticomaxillary suture fractures, and the lowest was 82.1% for zygomatic body fractures. Area under the curve (AUC) values were also highest for zygomaticomaxillary suture (0.943) and lowest for zygomatic body fractures (0.876). The YOLOv8 model demonstrated promising results in the automated detection of zygomatic fractures, achieving the highest performance in identifying fractures of the zygomaticomaxillary suture and zygomatic arch.

EATHOA: Elite-evolved hiking algorithm for global optimization and precise multi-thresholding image segmentation in intracerebral hemorrhage images.

Abdel-Salam M, Houssein EH, Emam MM, Samee NA, Gharehchopogh FS, Bacanin N

pubmed logopapersAug 6 2025
Intracerebral hemorrhage (ICH) is a life-threatening condition caused by bleeding in the brain, with high mortality rates, particularly in the acute phase. Accurate diagnosis through medical image segmentation plays a crucial role in early intervention and treatment. However, existing segmentation methods, such as region-growing, clustering, and deep learning, face significant limitations when applied to complex images like ICH, especially in multi-threshold image segmentation (MTIS). As the number of thresholds increases, these methods often become computationally expensive and exhibit degraded segmentation performance. To address these challenges, this paper proposes an Elite-Adaptive-Turbulent Hiking Optimization Algorithm (EATHOA), an enhanced version of the Hiking Optimization Algorithm (HOA), specifically designed for high-dimensional and multimodal optimization problems like ICH image segmentation. EATHOA integrates three novel strategies including Elite Opposition-Based Learning (EOBL) for improving population diversity and exploration, Adaptive k-Average-Best Mutation (AKAB) for dynamically balancing exploration and exploitation, and a Turbulent Operator (TO) for escaping local optima and enhancing the convergence rate. Extensive experiments were conducted on the CEC2017 and CEC2022 benchmark functions to evaluate EATHOA's global optimization performance, where it consistently outperformed other state-of-the-art algorithms. The proposed EATHOA was then applied to solve the MTIS problem in ICH images at six different threshold levels. EATHOA achieved peak values of PSNR (34.4671), FSIM (0.9710), and SSIM (0.8816), outperforming recent methods in segmentation accuracy and computational efficiency. These results demonstrate the superior performance of EATHOA and its potential as a powerful tool for medical image analysis, offering an effective and computationally efficient solution for the complex challenges of ICH image segmentation.

AI-Guided Cardiac Computer Tomography in Type 1 Diabetes Patients with Low Coronary Artery Calcium Score.

Wohlfahrt P, Pazderník M, Marhefková N, Roland R, Adla T, Earls J, Haluzík M, Dubský M

pubmed logopapersAug 6 2025
<b><i>Objective:</i></b> Cardiovascular risk stratification based on traditional risk factors lacks precision at the individual level. While coronary artery calcium (CAC) scoring enhances risk prediction by detecting calcified atherosclerotic plaques, it may underestimate risk in individuals with noncalcified plaques-a pattern common in younger type 1 diabetes (T1D) patients. Understanding the prevalence of noncalcified atherosclerosis in T1D is crucial for developing more effective screening strategies. Therefore, this study aimed to assess the burden of clinically significant atherosclerosis in T1D patients with CAC <100 using artificial intelligence (AI)-guided quantitative coronary computed tomographic angiography (AI-QCT). <b><i>Methods:</i></b> This study enrolled T1D patients aged ≥30 years with disease duration ≥10 years and no manifest or symptomatic atherosclerotic cardiovascular disease (ASCVD). CAC and carotid ultrasound were assessed in all participants. AI-QCT was performed in patients with CAC 0 and at least one plaque in the carotid arteries or those with CAC 1-99. <b><i>Results:</i></b> Among the 167 participants (mean age 52 ± 10 years; 44% women; T1D duration 29 ± 11 years), 93 (56%) had CAC = 0, 46 (28%) had CAC 1-99, 8 (5%) had CAC 100-299, and 20 (12%) had CAC ≥300. AI-QCT was performed in a subset of 52 patients. Only 11 (21%) had no evidence of coronary artery disease. Significant coronary stenosis was identified in 17% of patients, and 30 (73%) presented with at least one high-risk plaque. Compared with CAC-based risk categories, AI-QCT reclassified 58% of patients, and 21% compared with the STENO1 risk categories. There was only fair agreement between AI-QCT and CAC (κ = 0.25), and a slight agreement between AI-QCT and STENO1 risk categories (κ = 0.02). <b><i>Conclusion:</i></b> AI-QCT may reveal subclinical atherosclerotic burden and high-risk features that remain undetected by traditional risk models or CAC. These findings challenge the assumption that a low CAC score equates to a low cardiovascular risk in T1D.

TotalRegistrator: Towards a Lightweight Foundation Model for CT Image Registration

Xuan Loc Pham, Gwendolyn Vuurberg, Marjan Doppen, Joey Roosen, Tip Stille, Thi Quynh Ha, Thuy Duong Quach, Quoc Vu Dang, Manh Ha Luu, Ewoud J. Smit, Hong Son Mai, Mattias Heinrich, Bram van Ginneken, Mathias Prokop, Alessa Hering

arxiv logopreprintAug 6 2025
Image registration is a fundamental technique in the analysis of longitudinal and multi-phase CT images within clinical practice. However, most existing methods are tailored for single-organ applications, limiting their generalizability to other anatomical regions. This work presents TotalRegistrator, an image registration framework capable of aligning multiple anatomical regions simultaneously using a standard UNet architecture and a novel field decomposition strategy. The model is lightweight, requiring only 11GB of GPU memory for training. To train and evaluate our method, we constructed a large-scale longitudinal dataset comprising 695 whole-body (thorax-abdomen-pelvic) paired CT scans from individual patients acquired at different time points. We benchmarked TotalRegistrator against a generic classical iterative algorithm and a recent foundation model for image registration. To further assess robustness and generalizability, we evaluated our model on three external datasets: the public thoracic and abdominal datasets from the Learn2Reg challenge, and a private multiphase abdominal dataset from a collaborating hospital. Experimental results on the in-house dataset show that the proposed approach generally surpasses baseline methods in multi-organ abdominal registration, with a slight drop in lung alignment performance. On out-of-distribution datasets, it achieved competitive results compared to leading single-organ models, despite not being fine-tuned for those tasks, demonstrating strong generalizability. The source code will be publicly available at: https://github.com/DIAGNijmegen/oncology_image_registration.git.

Development of a deep learning based approach for multi-material decomposition in spectral CT: a proof of principle in silico study.

Rajagopal JR, Rapaka S, Farhadi F, Abadi E, Segars WP, Nowak T, Sharma P, Pritchard WF, Malayeri A, Jones EC, Samei E, Sahbaee P

pubmed logopapersAug 6 2025
Conventional approaches to material decomposition in spectral CT face challenges related to precise algorithm calibration across imaged conditions and low signal quality caused by variable object size and reduced dose. In this proof-of-principle study, a deep learning approach to multi-material decomposition was developed to quantify iodine, gadolinium, and calcium in spectral CT. A dual-phase network architecture was trained using synthetic datasets containing computational models of cylindrical and virtual patient phantoms. Classification and quantification performance was evaluated across a range of patient size and dose parameters. The model was found to accurately classify (accuracy: cylinders - 98%, virtual patients - 97%) and quantify materials (mean absolute percentage difference: cylinders - 8-10%, virtual patients - 10-15%) in both datasets. Performance in virtual patient phantoms improved as the hybrid training dataset included a larger contingent of virtual patient phantoms (accuracy: 48% with 0 virtual patients to 97% with 8 virtual patients). For both datasets, the algorithm was able to maintain strong performance under challenging conditions of large patient size and reduced dose. This study shows the validity of a deep-learning based approach to multi-material decomposition trained with in-silico images that can overcome the limitations of conventional material decomposition approaches.

Artificial Intelligence and Extended Reality in TAVR: Current Applications and Challenges.

Skalidis I, Sayah N, Benamer H, Amabile N, Laforgia P, Champagne S, Hovasse T, Garot J, Garot P, Akodad M

pubmed logopapersAug 6 2025
Integration of AI and XR in TAVR is revolutionizing the management of severe aortic stenosis by enhancing diagnostic accuracy, risk stratification, and pre-procedural planning. Advanced algorithms now facilitate precise electrocardiographic, echocardiographic, and CT-based assessments that reduce observer variability and enable patient-specific risk prediction. Immersive XR technologies, including augmented, virtual, and mixed reality, improve spatial visualization of complex cardiac anatomy and support real-time procedural guidance. Despite these advancements, standardized protocols, regulatory frameworks, and ethical safeguards remain necessary for widespread clinical adoption.

A novel lung cancer diagnosis model using hybrid convolution (2D/3D)-based adaptive DenseUnet with attention mechanism.

Deepa J, Badhu Sasikala L, Indumathy P, Jerrin Simla A

pubmed logopapersAug 5 2025
Existing Lung Cancer Diagnosis (LCD) models have difficulty in detecting early-stage lung cancer due to the asymptomatic nature of the disease which leads to an increased death rate of patients. Therefore, it is important to diagnose lung disease at an early stage to save the lives of affected persons. Hence, the research work aims to develop an efficient lung disease diagnosis using deep learning techniques for the early and accurate detection of lung cancer. This is achieved by. Initially, the proposed model collects the mandatory CT images from the standard benchmark datasets. Then, the lung cancer segmentation is done by using the development of Hybrid Convolution (2D/3D)-based Adaptive DenseUnet with Attention mechanism (HC-ADAM). The Hybrid Sewing Training with Spider Monkey Optimization (HSTSMO) is introduced to optimize the parameters in the developed HC-ADAM segmentation approach. Finally, the dissected lung nodule imagery is considered for the lung cancer classification stage, where the Hybrid Adaptive Dilated Networks with Attention mechanism (HADN-AM) are implemented with the serial cascading of ResNet and Long Short Term Memory (LSTM) for attaining better categorization performance. The accuracy, precision, and F1-score of the developed model for the LIDC-IDRI dataset are 96.3%, 96.38%, and 96.36%, respectively.

Towards a zero-shot low-latency navigation for open surgery augmented reality applications.

Schwimmbeck M, Khajarian S, Auer C, Wittenberg T, Remmele S

pubmed logopapersAug 5 2025
Augmented reality (AR) enhances surgical navigation by superimposing visible anatomical structures with three-dimensional virtual models using head-mounted displays (HMDs). In particular, interventions such as open liver surgery can benefit from AR navigation, as it aids in identifying and distinguishing tumors and risk structures. However, there is a lack of automatic and markerless methods that are robust against real-world challenges, such as partial occlusion and organ motion. We introduce a novel multi-device approach for automatic live navigation in open liver surgery that enhances the visualization and interaction capabilities of a HoloLens 2 HMD through precise and reliable registration using an Intel RealSense RGB-D camera. The intraoperative RGB-D segmentation and the preoperative CT data are utilized to register a virtual liver model to the target anatomy. An AR-prompted Segment Anything Model (SAM) enables robust segmentation of the liver in situ without the need for additional training data. To mitigate algorithmic latency, Double Exponential Smoothing (DES) is applied to forecast registration results. We conducted a phantom study for open liver surgery, investigating various scenarios of liver motion, viewpoints, and occlusion. The mean registration errors (8.31 mm-18.78 mm TRE) are comparable to those reported in prior work, while our approach demonstrates high success rates even for high occlusion factors and strong motion. Using forecasting, we bypassed the algorithmic latency of 79.8 ms per frame, with median forecasting errors below 2 mms and 1.5 degrees between the quaternions. To our knowledge, this is the first work to approach markerless in situ visualization by combining a multi-device method with forecasting and a foundation model for segmentation and tracking. This enables a more reliable and precise AR registration of surgical targets with low latency. Our approach can be applied to other surgical applications and AR hardware with minimal effort.

Unsupervised learning based perfusion maps for temporally truncated CT perfusion imaging.

Tung CH, Li ZY, Huang HM

pubmed logopapersAug 5 2025
&#xD;Computed tomography perfusion (CTP) imaging is a rapid diagnostic tool for acute stroke but is less robust when tissue time-attenuation curves are truncated. This study proposes an unsupervised learning method for generating perfusion maps from truncated CTP images. Real brain CTP images were artificially truncated to 15% and 30% of the original scan time. Perfusion maps of complete and truncated CTP images were calculated using the proposed method and compared with standard singular value decomposition (SVD), tensor total variation (TTV), nonlinear regression (NLR), and spatio-temporal perfusion physics-informed neural network (SPPINN).&#xD;Main results.&#xD;The NLR method yielded many perfusion values outside physiological ranges, indicating a lack of robustness. The proposed method did not improve the estimation of cerebral blood flow compared to both the SVD and TTV methods, but reduced the effect of truncation on the estimation of cerebral blood volume, with a relative difference of 15.4% in the infarcted region for 30% truncation (20.7% for SVD and 19.4% for TTV). The proposed method also showed better resistance to 30% truncation for mean transit time, with a relative difference of 16.6% in the infarcted region (25.9% for SVD and 26.2% for TTV). Compared to the SPPINN method, the proposed method had similar responses to truncation in gray and white matter, but was less sensitive to truncation in the infarcted region. These results demonstrate the feasibility of using unsupervised learning to generate perfusion maps from CTP images and improve robustness under truncation scenarios.&#xD.

The Use of Artificial Intelligence to Improve Detection of Acute Incidental Pulmonary Emboli.

Kuzo RS, Levin DL, Bratt AK, Walkoff LA, Suman G, Houghton DE

pubmed logopapersAug 4 2025
Incidental pulmonary emboli (IPE) are frequently overlooked by radiologists. Artificial intelligence (AI) algorithms have been developed to aid detection of pulmonary emboli. To measure diagnostic performance of AI compared with prospective interpretation by radiologists. A commercially available AI algorithm was used to retrospectively review 14,453 contrast-enhanced outpatient CT CAP exams in 9171 patients where PE was not clinically suspected. Natural language processing (NLP) searches of reports identified IPE detected prospectively. Thoracic radiologists reviewed all cases read as positive by AI or NLP to confirm IPE and assess the most proximal level of clot and overall clot burden. 1,400 cases read as negative by both the initial radiologist and AI were re-reviewed to assess for additional IPE. Radiologists prospectively detected 218 IPE and AI detected an additional 36 unreported cases. AI missed 30 cases of IPE detected by the radiologist and had 94 false positives. For 36 IPE missed by the radiologist, median clot burden was 1 and 19 were solitary segmental or subsegmental. For 30 IPE missed by AI, one case had large central emboli and the others were small with 23 solitary subsegmental emboli. Radiologist re-review of 1,400 exams interpreted as negative found 8 additional cases of IPE. Compared with radiologists, AI had similar sensitivity but reduced positive predictive value. Our experience indicates that the AI tool is not ready to be used autonomously without human oversight, but a human observer plus AI is better than either alone for detection of incidental pulmonary emboli.
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