To develop a convolutional neural network (CNN) model to diagnose thyroid cartilage invasion by laryngeal and hypopharyngeal cancers observed on computed tomography (CT) images and evaluate the model's diagnostic performance. We retrospectively analyzed 91 cases of laryngeal or hypopharyngeal cancer treated surgically at our hospital during the period April 2010 through May 2023, and we divided the cases into datasets for training (n = 61) and testing (n = 30). We reviewed the CT images and pathological diagnoses in all cases to determine the invasion positive- or negative-status as a ground truth. We trained the new CNN model to classify thyroid cartilage invasion-positive or -negative status from the pre-treatment axial CT images by transfer learning from Residual Network 101 (ResNet101), using the training dataset. We then used the test dataset to evaluate the model's performance. Two radiologists, one with extensive head and neck imaging experience (senior reader) and the other with less experience (junior reader) reviewed the CT images of the test dataset to determine whether thyroid cartilage invasion was present. The following were obtained by the CNN model with the test dataset: area under the curve (AUC), 0.82; 90 % accuracy, 80 % sensitivity, and 95 % specificity. The CNN model showed a significant difference in AUCs compared to the junior reader (p = 0.035) but not the senior reader (p = 0.61). The CNN-based diagnostic model can be a useful supportive tool for the assessment of thyroid cartilage invasion in patients with laryngeal or hypopharyngeal cancer.

Accurate segmentation of lung tumors is essential for advancing personalized medicine in non-small cell lung cancer (NSCLC). However, stage IV NSCLC presents significant challenges due to heterogeneous tumor morphology and the presence of associated conditions including infection, atelectasis and pleural effusion. The complexity of multicentric datasets further complicates robust segmentation across diverse clinical settings. In this study, we evaluate deep-learning-based approaches for automated segmentation of advanced-stage lung tumors using 3D architectures on 387 CT scans from the Deep-Lung-IV study. Through comprehensive experiments, we assess the impact of model design, HU windowing, and dataset size on delineation performance, providing practical guidelines for robust implementation. Additionally, we propose a confidence score using deep ensembles to quantify prediction uncertainty and automate the identification of complex cases that require further review. Our results demonstrate the potential of attention-based architectures and specific preprocessing strategies to improve segmentation quality in such a challenging clinical scenario, while emphasizing the importance of uncertainty estimation to build trustworthy AI systems in medical imaging. Code is available at: https://github.com/Sacha-Dedeken/SegStageIVNSCLC.

Propagation-based imaging (one method of X-ray phase contrast imaging) with microcomputed tomography (PBI-µCT) offers the potential to visualise low-density materials, such as soft tissues and hydrogel constructs, which are difficult to be identified by conventional absorption-based contrast µCT. Conventional µCT reconstruction produces edge-enhanced contrast (EEC) images which preserve sharp boundaries but are susceptible to noise and do not provide consistent grey value representation for the same material. Meanwhile, phase retrieval (PR) algorithms can convert edge enhanced contrast to area contrast to improve signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) but usually results to over-smoothing, thus creating inaccuracies in quantitative analysis. To alleviate these problems, this study developed a deep learning-based method called edge view enhanced phase retrieval (EVEPR), by strategically integrating the complementary spatial features of denoised EEC and PR images, and further applied this method to segment the hydrogel constructs in vivo and ex vivo. EVEPR used paired denoised EEC and PR images to train a deep convolutional neural network (CNN) on a dataset-to-dataset basis. The CNN had been trained on important high-frequency details, for example, edges and boundaries from the EEC image and area contrast from PR images. The CNN predicted result showed enhanced area contrast beyond conventional PR algorithms while improving SNR and CNR. The enhanced CNR especially allowed for the image to be segmented with greater efficiency. EVEPR was applied to in vitro and ex vivo PBI-µCT images of low-density hydrogel constructs. The enhanced visibility and consistency of hydrogel constructs was essential for segmenting such material which usually exhibit extremely poor contrast. The EVEPR images allowed for more accurate segmentation with reduced manual adjustments. The efficiency in segmentation allowed for the generation of a sizeable database of segmented hydrogel scaffolds which were used in conventional data-driven segmentation applications. EVEPR was demonstrated to be a robust post-image processing method capable of significantly enhancing image quality by training a CNN on paired denoised EEC and PR images. This method not only addressed the common issues of over-smoothing and noise susceptibility in conventional PBI-µCT image processing but also allowed for efficient and accurate in vitro and ex vivo image processing applications of low-density materials.

This study evaluated the performance of a multi-stage Segmentation Residual Network (SegResNet)-based deep learning (DL) model for the automatic segmentation of cone-beam computed tomography (CBCT) images of patients with stage III and IV periodontitis. Seventy pre-processed CBCT scans from patients undergoing periodontal rehabilitation were used for training and validation. The model was tested on 10 CBCT scans independent from the training dataset by comparing results with semi-automatic (SA) segmentations. Segmentation accuracy was assessed using the Dice similarity coefficient (DSC), Intersection over Union (IoU), and Hausdorff distance 95^th percentile (HD95). Linear periodontal measurements were performed on four tooth surfaces to assess the validity of the DL segmentation in the periodontal region. The DL model achieved a mean DSC of 0.9650 ± 0.0097, with an IoU of 0.9340 ± 0.0180 and HD95 of 0.4820 mm ± 0.1269 mm, showing strong agreement with SA segmentation. Linear measurements revealed high statistical correlations between the mesial, distal, and lingual surfaces, with intraclass correlation coefficients (ICC) of 0.9442 (p<0.0001), 0.9232 (p<0.0001), and 0.9598(p<0.0001), respectively, while buccal measurements revealed lower consistency, with an ICC of 0.7481 (p<0.0001). The DL method reduced the segmentation time by 47 times compared to the SA method. Acquired 3D models may enable precise treatment planning in cases where conventional diagnostic modalities are insufficient. However, the robustness of the model must be increased to improve its general reliability and consistency at the buccal aspect of the periodontal region. This study presents a DL model for the CBCT-based segmentation of periodontal defects, demonstrating high accuracy and a 47-fold time reduction compared to SA methods, thus improving the feasibility of 3D diagnostics for advanced periodontitis.

Artificial intelligence (AI) has become pivotal in advancing medical care, particularly in interventional cardiology. Recent AI developments have proven effective in guiding advanced procedures and complex decisions. The authors review the latest AI-based innovations in the diagnosis of chronic total occlusions (CTO) and in determining the probability of success of CTO percutaneous coronary intervention (PCI). Neural networks and deep learning strategies were the most commonly used algorithms, and the models were trained and deployed using a variety of data types, such as clinical parameters and imaging. AI holds great promise in facilitating CTO PCI.

To evaluate the possibility of performing renal vessel reconstruction on non-enhanced CT images using deep learning models. 177 patients' CT scans in the non-enhanced phase, arterial phase and venous phase were chosen. These data were randomly divided into the training set (n = 120), validation set (n = 20) and test set (n = 37). In training set and validation set, a radiologist marked out the right renal arteries and veins on non-enhanced CT phase images using contrast phases as references. Trained deep learning models were tested and evaluated on the test set. A radiologist performed renal vessel reconstruction on the test set without the contrast phase reference, and the results were used for comparison. Reconstruction using the arterial phase and venous phase was used as the gold standard. Without the contrast phase reference, both radiologist and model could accurately identify artery and vein main trunk. The accuracy was 91.9% vs. 97.3% (model vs. radiologist) in artery and 91.9% vs. 100% in vein, the difference was insignificant. The model had difficulty identify accessory arteries, the accuracy was significantly lower than radiologist (44.4% vs. 77.8%, p = 0.044). The model also had lower accuracy in accessory veins, but the difference was insignificant (64.3% vs. 85.7%, p = 0.094). Deep learning models could accurately recognize the right renal artery and vein main trunk, and accuracy was comparable to that of radiologists. Although the current model still had difficulty recognizing small accessory vessels, further training and model optimization would solve these problems.

The rapid spread of SARS-CoV-2 has highlighted the need for intelligent methodologies in COVID-19 diagnosis. Clinicians face significant challenges due to the virus's fast transmission rate and the lack of reliable diagnostic tools. Although artificial intelligence (AI) has improved image processing, conventional approaches still rely on centralized data storage and training. This reliance increases complexity and raises privacy concerns, which hinder global data exchange. Therefore, it is essential to develop collaborative models that balance accuracy with privacy protection. This research presents a novel framework that combines blockchain technology with a combined learning paradigm to ensure secure data distribution and reduced complexity. The proposed Combined Learning Collective Deep Learning Blockchain Model (CLCD-Block) aggregates data from multiple institutions and leverages a hybrid capsule learning network for accurate predictions. Extensive testing with lung CT images demonstrates that the model outperforms existing models, achieving an accuracy exceeding 97%. Specifically, on four benchmark datasets, CLCD-Block achieved up to 98.79% Precision, 98.84% Recall, 98.79% Specificity, 98.81% F1-Score, and 98.71% Accuracy, showcasing its superior diagnostic capability. Designed for COVID-19 diagnosis, the CLCD-Block framework is adaptable to other applications, integrating AI, decentralized training, privacy protection, and secure blockchain collaboration. It addresses challenges in diagnosing chronic diseases, facilitates cross-institutional research and monitors infectious outbreaks. Future work will focus on enhancing scalability, optimizing real-time performance and adapting the model for broader healthcare datasets.

To evaluate the accuracy and reliability of a novel automated 3D CT-based method for measuring femoral neck anteversion (FNA) compared to three traditional manual methods. A total of 126 femurs from 63 full-length CT scans (35 men and 28 women; average age: 52.0 ± 14.7 years) were analyzed. The automated method used a deep learning network for femur segmentation, landmark identification, and anteversion calculation, with results generated based on two axes: Auto_GT (using the greater trochanter-to-intercondylar notch center axis) and Auto_P (using the piriformis fossa-to-intercondylar notch center axis). These results were validated through manual landmark annotation. The same dataset was assessed using three conventional manual methods: Murphy, Reikeras, and Lee methods. Intra- and inter-observer reliability were assessed using intraclass correlation coefficients (ICCs), and pairwise comparisons analyzed correlations and differences between methods. The automated methods produced consistent FNA measurements (Auto_GT: 17.59 ± 9.16° vs. Auto_P: 17.37 ± 9.17° on the right; 15.08 ± 9.88° vs. 14.84 ± 9.90° on the left). Intra-observer ICCs ranged from 0.864 to 0.961, and inter-observer ICCs between Auto_GT and the manual methods were high, except for the Lee method. No significant differences were observed between the two automated methods or between the automated and manual verification methods. Moreover, strong correlations (R > 0.9, p < 0.001) were found between Auto_GT and the manual methods. The novel automated 3D CT-based method demonstrates strong reproducibility and reliability for measuring femoral neck anteversion, with performance comparable to traditional manual techniques. These results indicate its potential utility for preoperative planning, postoperative evaluation, and computer-assisted orthopedic procedures. Not applicable.

Pneumonia is a major threat to the health of children, especially those under the age of five. Mycoplasma  pneumoniae infection is a core cause of pediatric pneumonia, and the incidence of severe mycoplasma pneumoniae pneumonia (SMPP) has increased in recent years. Therefore, there is an urgent need to establish an early warning model for SMPP to improve the prognosis of pediatric pneumonia. The study comprised 597 SMPP patients aged between 1 month and 18 years. Clinical data were selected through Lasso regression analysis, followed by the application of eight machine learning algorithms to develop early warning model. The accuracy of the model was assessed using validation and prospective cohort. To facilitate clinical assessment, the study simplified the indicators and constructed visualized simplified model. The clinical applicability of the model was evaluated by DCA and CIC curve. After variable selection, eight machine learning models were developed using age, sex and 21 serum indicators identified as predictive factors for SMPP. A Light Gradient Boosting Machine (LightGBM) model demonstrated strong performance, achieving AUC of 0.92 for prospective validation. The SHAP analysis was utilized to screen advantageous variables, which contains of serum S100A8/A9, tracheal computed tomography (CT), retinol-binding protein(RBP), platelet larger cell ratio(P-LCR) and CD4+CD25+Treg cell counts, for constructing a simplified model (SCRPT) to improve clinical applicability. The SCRPT diagnostic model exhibited favorable diagnostic efficacy (AUC > 0.8). Additionally, the study found that S100A8/A9 outperformed clinical inflammatory markers can also differentiate the severity of MPP. The SCRPT model consisting of five dominant variables (S100A8/A9, CT, RBP, PLCR and Treg cell) screened based on eight machine learning is expected to be a tool for early diagnosis of SMPP. S100A8/A9 can also be used as a biomarker for validity differentiation of SMPP when medical conditions are limited.

Diagnosis of peritoneal invasion, lymph node metastasis, and hepatic metastasis is crucial in the decision-making process of ovarian tumor treatment. This study aimed to test the feasibility of low-dose abdominopelvic CT with an artificial intelligence iterative reconstruction (AIIR) for diagnosing peritoneal invasion, lymph node metastasis, and hepatic metastasis in pre-operative imaging of ovarian tumor. This study prospectively enrolled 88 patients with pathology-confirmed ovarian tumors, where routine-dose CT at portal venous phase (120 kVp/ref. 200 mAs) with hybrid iterative reconstruction (HIR) was followed by a low-dose scan (120 kVp/ref. 40 mAs) with AIIR. The performance of diagnosing peritoneal invasion and lymph node metastasis was assessed using receiver operating characteristic (ROC) analysis with pathological results serving as the reference. The hepatic parenchymal metastases were diagnosed and signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured. The perihepatic structures were also scored on the clarity of porta hepatis, gallbladder fossa and intersegmental fissure. The effective dose of low-dose CT was 79.8% lower than that of routine-dose scan (2.64 ± 0.46 vs. 13.04 ± 2.25 mSv, p < 0.001). The low-dose AIIR showed similar area under the ROC curve (AUC) with routine-dose HIR for diagnosing both peritoneal invasion (0.961 vs. 0.960, p = 0.734) and lymph node metastasis (0.711 vs. 0.715, p = 0.355). The 10 hepatic parenchymal metastases were all accurately diagnosed on the two image sets. The low-dose AIIR exhibited higher SNR and CNR for hepatic parenchymal metastases and superior clarity for perihepatic structures. In low-dose pre-operative CT of ovarian tumor, AIIR delivers similar diagnostic accuracy for peritoneal invasion, lymph node metastasis, and hepatic metastasis, as compared to routine-dose abdominopelvic CT. It is feasible and diagnostically safe to apply up to 80% dose reduction in CT imaging of ovarian tumor by using AIIR.