Aortic dissection (AD) is a rare condition with a high mortality rate, necessitating accurate and rapid diagnosis. This study develops an automated deep learning pipeline for identifying, segmenting, and Stanford subtyping AD using computed tomography angiography (CTA) images. This pipeline consists of four interconnected modules: aorta segmentation, AD identification, true lumen (TL) and false lumen (FL) segmentation, and Stanford subtyping. In the aorta segmentation module, a 3D full-resolution nnU-Net is trained. The segmented aorta's boundary is extracted using morphological operations and projected from multiple views in the AD identification module. AD identification is then performed using the multi-view projection data. For AD cases, a 3D nnU-Net is further trained for TL/FL segmentation based on the segmented aorta. Finally, a network is trained for Stanford subtyping using multi-view maximum density projections of the segmented TL/FL. A total of 386 CTA scans were collected for training, validation, and testing of the pipeline. For AD identification, the method achieved an accuracy of 0.979. The TL/FL segmentation for TypeA-AD and Type-B-AD achieved average Dice coefficient of 0.968 for TL and 0.971 for FL. For Stanford subtyping, the multi-view method achieved an accuracy of 0.990. The automated pipeline enables rapid and accurate identification, segmentation, and Stanford subtyping of AD using CTA images, potentially accelerating the diagnosis and treatment. The segmented aorta and TL/FL can also serve as references for physicians. The code, models, and pipeline are publicly available at https://github.com/zhuangCJ/A-pipeline-of-AD.git .

Ultrasound-guided transverse thoracic plane (TTP) block has been shown to be highly effective in relieving postoperative pain in a variety of surgeries involving the anterior chest wall. Accurate identification of the target structure on ultrasound images is key to the successful implementation of TTP block. Nevertheless, the complexity of anatomical structures in the targeted blockade area coupled with the potential for adverse clinical incidents presents considerable challenges, particularly for anesthesiologists who are less experienced. This study applied deep learning methods to TTP block and developed a deep learning model to achieve real-time region segmentation in ultrasound to assist doctors in the accurate identification of the target nerve. Using 2329 images from 155 patients, we successfully segmented key structures associated with TTP areas and nerve blocks, including the transversus thoracis muscle, lungs, and bones. The achieved IoU (Intersection over Union) scores are 0.7272, 0.9736, and 0.8244 in that order. Recall metrics were 0.8305, 0.9896, and 0.9336 respectively, whilst Dice coefficients reached 0.8421, 0.9866, and 0.9037, particularly with an accuracy surpassing 97% in the identification of perilous lung regions. The real-time segmentation frame rate of the model for ultrasound video was as high as 42.7 fps, thus meeting the exigencies of performing nerve blocks under real-time ultrasound guidance in clinical practice. This study introduces TTP-Unet, a deep learning model specifically designed for TTP block, capable of automatically identifying crucial anatomical structures within ultrasound images of TTP block, thereby offering a practicable solution to attenuate the clinical difficulty associated with TTP block technique.

The Segment Anything Model (SAM) was fine-tuned on the EchoNet-Dynamic dataset and evaluated on external transthoracic echocardiography (TTE) and Point-of-Care Ultrasound (POCUS) datasets from CAMUS (University Hospital of St Etienne) and Mayo Clinic (99 patients: 58 TTE, 41 POCUS). Fine-tuned SAM was superior or comparable to MedSAM. The fine-tuned SAM also outperformed EchoNet and U-Net models, demonstrating strong generalization, especially on apical 2-chamber (A2C) images (fine-tuned SAM vs. EchoNet: CAMUS-A2C: DSC 0.891 ± 0.040 vs. 0.752 ± 0.196, p < 0.0001) and POCUS (DSC 0.857 ± 0.047 vs. 0.667 ± 0.279, p < 0.0001). Additionally, SAM-enhanced workflow reduced annotation time by 50% (11.6 ± 4.5 sec vs. 5.7 ± 1.7 sec, p < 0.0001) while maintaining segmentation quality. We demonstrated an effective strategy for fine-tuning a vision foundation model for enhancing clinical workflow efficiency and supporting human-AI collaboration.

To investigate the diagnostic capability of multiple machine learning algorithms combined with intratumoral and peritumoral ultrasound radiomics models for non-massive breast cancer in dense breast backgrounds. Manual segmentation of ultrasound images was performed to define the intratumoral region of interest (ROI), and five peritumoral ROIs were generated by extending the contours by 1 to 5 mm. A total of 851 radiomics features were extracted from these regions and filtered using statistical methods. Thirteen machine learning algorithms were employed to create radiomics models for the intratumoral and peritumoral areas. The best model was combined with clinical ultrasound predictive factors to form a joint model, which was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA).Based on this model, a nomogram was developed, demonstrating high predictive performance, with C-index values of 0.982 and 0.978.The model incorporating the intratumoral and peritumoral 2 mm regions outperformed other models, indicating its effectiveness in distinguishing between benign and malignant breast lesions. This study concludes that ultrasound imaging, particularly in the intratumoral and peritumoral 2 mm regions, has significant potential for diagnosing non-massive breast cancer, and the nomogram can assist clinical decision-making.

Scanner-related changes in data quality are common in medical imaging, yet monitoring their impact on diagnostic AI performance remains challenging. In this study, we performed standardized consistency testing of an FDA-cleared and CE-marked AI for triage and notification of intracranial aneurysms across changes in image data quality caused by dose and image reconstruction. Our assessment was based on repeated examinations of a head CT phantom designed for AI evaluation, replicating a patient with three intracranial aneurysms in the anterior, middle and posterior circulation. We show that the AI maintains stable performance within the medium dose range but produces inconsistent results at reduced dose and, unexpectedly, at higher dose when filtered back projection is used. Data quality standards required for AI are stricter than those for neuroradiologists, who report higher aneurysm visibility rates and experience performance degradation only at substantially lower doses, with no decline at higher doses.

Kyphosis is a prevalent spinal condition where the spine curves in the sagittal plane, resulting in spine deformities. Curvature estimation provides a powerful index to assess the deformation severity of scoliosis. In current clinical diagnosis, the standard curvature estimation method for quantitatively assessing the curvature is performed by measuring the vertebral angle, which is the angle between two lines, drawn perpendicular to the upper and lower endplates of the involved vertebra. However, manual Cobb angle measurement requires considerable time and effort, along with associated problems such as interobserver and intraobserver variations. Hence, in this study, we propose UNet-based Attention Network for Thoracolumbar Vertebral Compression Fracture Angle (UANV), a vertebra angle measuring model using lateral spinal X-ray based on a deep convolutional neural network (CNN). Specifically, we considered the detailed shape of each vertebral body with an attention mechanism and then recorded each edge of each vertebra to calculate vertebrae angles.

Predicting the final infarct after an extended time window mechanical thrombectomy (MT) is beneficial for treatment planning in acute ischemic stroke (AIS). By introducing guidance from prior knowledge, this study aims to improve the accuracy of the deep learning model for post-MT infarct prediction using pre-MT brain perfusion data. This retrospective study collected CT perfusion data at admission for AIS patients receiving MT over 6 hours after symptom onset, from January 2020 to December 2024, across three centers. Infarct on post-MT diffusion weighted imaging served as ground truth. Five Swin transformer based models were developed for post-MT infarct segmentation using pre-MT CT perfusion parameter maps: BaselineNet served as the basic model for comparative analysis, CollateralFlowNet included a collateral circulation evaluation score, InfarctProbabilityNet incorporated infarct probability mapping, ArterialTerritoryNet was guided by artery territory mapping, and UnifiedNet combined all prior knowledge sources. Model performance was evaluated using the Dice coefficient and intersection over union (IoU). A total of 221 patients with AIS were included (65.2% women) with a median age of 73 years. Baseline ischemic core based on CT perfusion threshold achieved a Dice coefficient of 0.50 and IoU of 0.33. BaselineNet improved to a Dice coefficient of 0.69 and IoU of 0.53. Compared with BaselineNet, models incorporating medical knowledge demonstrated higher performance: CollateralFlowNet (Dice coefficient 0.72, IoU 0.56), InfarctProbabilityNet (Dice coefficient 0.74, IoU 0.58), ArterialTerritoryNet (Dice coefficient 0.75, IoU 0.60), and UnifiedNet (Dice coefficient 0.82, IoU 0.71) (all P<0.05). In this study, integrating medical knowledge into deep learning models enhanced the accuracy of infarct predictions in AIS patients undergoing extended time window MT.

In radiotherapy planning, acquiring both magnetic resonance (MR) and computed tomography (CT) images is crucial for comprehensive evaluation and treatment. However, simultaneous acquisition of MR and CT images is time-consuming, economically expensive, and involves ionizing radiation, which poses health risks to patients. The objective of this study is to generate CT images from radiation-free MR images using a novel quasi-supervised learning framework. In this work, we propose a quasi-supervised framework to explore the underlying relationship between unpaired MR and CT images. Normalized mutual information (NMI) is employed as a similarity metric to evaluate the correspondence between MR and CT scans. To establish optimal pairings, we compute an NMI matrix across the training set and apply the Hungarian algorithm for global matching. The resulting MR-CT pairs, along with their NMI scores, are treated as prior knowledge and integrated into the training process to guide the MR-to-CT image translation model. Experimental results indicate that the proposed method significantly outperforms existing unsupervised image synthesis methods in terms of both image quality and consistency of image features during the MR to CT image conversion process. The generated CT images show a higher degree of accuracy and fidelity to the original MR images, ensuring better preservation of anatomical details and structural integrity. This study proposes a quasi-supervised framework that converts unpaired MR and CT images into structurally consistent pseudo-pairs, providing informative priors to enhance cross-modality image synthesis. This strategy not only improves the accuracy and reliability of MR-CT conversion, but also reduces reliance on costly and scarce paired datasets. The proposed framework offers a practical 1 and scalable solution for real-world medical imaging applications, where paired annotations are often unavailable.

This study aims to evaluate the predictive value of multiparameter characteristics of coronary computed tomography angiography (CCTA) plaque and the ratio of coronary artery volume to myocardial mass (V/M) in guiding percutaneous coronary stent implantation (PCI) in patients diagnosed with unstable angina. Patients who underwent CCTA and coronary angiography (CAG) within 2 months were retrospectively analyzed. According to CAG results, patients were divided into a medical therapy group (n=41) and a PCI revascularization group (n=37). The plaque characteristics and V/M were quantitatively evaluated. The parameters included minimum lumen area at stenosis (MLA), maximum area stenosis (MAS), maximum diameter stenosis (MDS), total plaque burden (TPB), plaque length, plaque volume, and each component volume within the plaque. Fractional flow reserve (FFR) and pericoronary fat attenuation index (FAI) were calculated based on CCTA. Artificial intelligence software was employed to compare the differences in each parameter between the 2 groups at both the vessel and plaque levels. The PCI group had higher MAS, MDS, TPB, FAI, noncalcified plaque volume and lipid plaque volume, and significantly lower V/M, MLA, and CT-derived fractional flow reserve (FFRCT). V/M, TPB, MLA, FFRCT, and FAI are important influencing factors of PCI. The combined model of MLA, FFRCT, and FAI had the largest area under the ROC curve (AUC=0.920), and had the best performance in predicting PCI. The integration of AI-derived multiparameter features from one-stop CCTA significantly enhances the accuracy of predicting PCI in angina pectoris patients, evaluating at the plaque, vessel, and patient levels.

Ocular and non-ocular diseases significantly impact millions of people worldwide, leading to vision impairment or blindness if not detected and managed early. Many individuals could be prevented from becoming blind by treating these diseases early on and stopping their progression. Despite advances in medical imaging and diagnostic tools, the manual detection of these diseases remains labor-intensive, time-consuming, and dependent on the expert's experience. Computer-aided diagnosis (CAD) has been transformed by machine learning (ML), providing promising methods for the automated detection and grading of diseases using various retinal imaging modalities. In this paper, we present a comprehensive systematic literature review that discusses the use of ML techniques to detect diseases from retinal images, utilizing both single and multi-modal imaging approaches. We analyze the efficiency of various Deep Learning and classical ML models, highlighting their achievements in accuracy, sensitivity, and specificity. Even with these advancements, the review identifies several critical challenges. We propose future research directions to address these issues. By overcoming these challenges, the potential of ML to enhance diagnostic accuracy and patient outcomes can be fully realized, opening the way for more reliable and effective ocular and non-ocular disease management.