To develop a positron emission tomography/computed tomography (PET/CT)-based radiomics model for predicting programmed cell death ligand 1 (PD-L1) expression in non-small cell lung cancer (NSCLC) patients and estimating progression-free survival (PFS) and overall survival (OS) in PD-L1-positive patients undergoing first-line immunotherapy. We retrospectively analysed 143 NSCLC patients who underwent pretreatment ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET/CT scans, of whom 86 were PD-L1-positive. Clinical data collected included gender, age, smoking history, Tumor-Node-Metastases (TNM) staging system, pathologic types, laboratory parameters, and PET metabolic parameters. Four machine learning algorithms-Bayes, logistic, random forest, and Supportsupport vector machine (SVM)-were used to build models. The predictive performance was validated using receiver operating characteristic (ROC) curves. Univariate and multivariate Cox analyses identified independent predictors of OS and PFS in PD-L1-positive expression patients undergoing immunotherapy, and a nomogram was created to predict OS. A total of 20 models were built for predicting PD-L1 expression. The clinical combined PET/CT radiomics model based on the SVM algorithm performed best (area under curve for training and test sets: 0.914 and 0.877, respectively). The Cox analyses showed that smoking history independently predicted PFS. SUVmean, monocyte percentage and white blood cell count were independent predictors of OS, and the nomogram was created to predict 1-year, 2-year, and 3-year OS based on these three factors. We developed PET/CT-based machine learning models to help predict PD-L1 expression in NSCLC patients and identified independent predictors of PFS and OS in PD-L1-positive patients receiving immunotherapy, thereby aiding precision treatment.

This article proposes a method called DenseNet 121-Mask R-CNN (DN-MRCNN) for the detection and segmentation of brain tumors. The main objective is to reduce the execution time and accurately locate and segment the tumor, including its subareas. The input images undergo preprocessing techniques such as median filtering and Gaussian filtering to reduce noise and artifacts, as well as improve image quality. Histogram equalization is used to enhance the tumor regions, and image augmentation is employed to improve the model's diversity and robustness. To capture important patterns, a gated axial self-attention layer is added to the DenseNet 121 model, allowing for increased attention during the analysis of the input images. For accurate segmentation, boundary boxes are generated using a Regional Proposal Network with anchor customization. Post-processing techniques, specifically nonmaximum suppression, are performed to neglect redundant bounding boxes caused by overlapping regions. The Mask R-CNN model is used to accurately detect and segment the entire tumor (WT), tumor core (TC), and enhancing tumor (ET). The proposed model is evaluated using the BraTS 2019 dataset, the UCSF-PDGM dataset, and the UPENN-GBM dataset, which are commonly used for brain tumor detection and segmentation.

Accurate medical image segmentation is crucial for enabling automated clinical decision procedures. However, existing supervised deep learning methods for medical image segmentation face significant challenges due to their reliance on extensive labeled training data. To address this limitation, our novel approach introduces a dual-branch transformer network operating on two scales, strategically encoding global contextual dependencies while preserving local information. To promote self-supervised learning, our method leverages semantic dependencies between different scales, generating a supervisory signal for inter-scale consistency. Additionally, it incorporates a spatial stability loss within each scale, fostering self-supervised content clustering. While intra-scale and inter-scale consistency losses enhance feature uniformity within clusters, we introduce a cross-entropy loss function atop the clustering score map to effectively model cluster distributions and refine decision boundaries. Furthermore, to account for pixel-level similarities between organ or lesion subpixels, we propose a selective kernel regional attention module as a plug and play component. This module adeptly captures and outlines organ or lesion regions, slightly enhancing the definition of object boundaries. Our experimental results on skin lesion, lung organ, and multiple myeloma plasma cell segmentation tasks demonstrate the superior performance of our method compared to state-of-the-art approaches.

Translating quantitative skeletal muscle MRI biomarkers into clinics requires efficient automatic segmentation methods. The purpose of this work is to investigate a simple yet effective iterative methodology for building a high-quality automatic segmentation model while minimizing the manual annotation effort. We used a retrospective database of quantitative MRI examinations (n = 70) of healthy and pathological thighs for training a nnU-Net segmentation model. Healthy volunteers and patients with various neuromuscular diseases, broadly categorized as dystrophic, inflammatory, neurogenic, and unlabeled NMDs. We designed an iterative procedure, progressively adding cases to the training set and using a simple visual five-level rating scale to judge the validity of generated segmentations for clinical use. On an independent test set (n = 20), we assessed the quality of the segmentation in 13 individual thigh muscles using standard segmentation metrics-dice coefficient (DICE) and 95% Hausdorff distance (HD95)-and quantitative biomarkers-cross-sectional area (CSA), fat fraction (FF), and water-T1/T2. We obtained high-quality segmentations (DICE = 0.88 ± 0.15/0.86 ± 0.14, HD95 = 6.35 ± 12.33/6.74 ± 11.57 mm), comparable to recent works, although with a smaller training set (n = 30). Inter-rater agreement on the five-level scale was fair to moderate but showed progressive improvement of the segmentation model along with the iterations. We observed limited differences from manually delineated segmentations on the quantitative outcomes (MAD: CSA = 65.2 mm², FF = 1%, water-T1 = 8.4 ms, water-T2 = 0.35 ms), with variability comparable to manual delineations.

Age estimation, especially in adults, presents substantial challenges in different contexts ranging from forensic to clinical applications. Bone mineral density (BMD), with its distinct age-related variations, has emerged as a critical marker in this domain. This study aims to enhance chronological age estimation accuracy using deep learning (DL) incorporating a multi-modality fusion strategy based on BMD. We conducted a retrospective analysis of 4296 CT scans from a Chinese population, covering August 2015 to November 2022, encompassing lumbar, femur, and pubis modalities. Our DL approach, integrating multi-modality fusion, was applied to predict chronological age automatically. The model's performance was evaluated using an internal real-world clinical cohort of 644 scans (December 2022 to May 2023) and an external cadaver validation cohort of 351 scans. In single-modality assessments, the lumbar modality excelled. However, multi-modality models demonstrated superior performance, evidenced by lower mean absolute errors (MAEs) and higher Pearson's R² values. The optimal multi-modality model exhibited outstanding R² values of 0.89 overall, 0.88 in females, 0.90 in males, with the MAEs of 4.05 overall, 3.69 in females, 4.33 in males in the internal validation cohort. In the external cadaver validation, the model maintained favourable R² values (0.84 overall, 0.89 in females, 0.82 in males) and MAEs (5.01 overall, 4.71 in females, 5.09 in males), highlighting its generalizability across diverse scenarios. The integration of multi-modalities fusion with DL significantly refines the accuracy of adult age estimation based on BMD. The AI-based system that effectively combines multi-modalities BMD data, presenting a robust and innovative tool for accurate AAE, poised to significantly improve both geriatric diagnostics and forensic investigations.

Vestibular schwannomas (VS) are benign tumors that can lead to hearing loss, balance issues, and tinnitus. Gamma Knife Radiosurgery (GKS) is a common treatment for VS, aimed at halting tumor growth and preserving neurological function. Accurate monitoring of VS volume before and after GKS is essential for assessing treatment efficacy. To evaluate the accuracy of an artificial intelligence (AI) algorithm, originally developed to identify NF2-SWN-related VS, in segmenting non-NF2-SWN-related VS and detecting volume changes pre- and post-GKS. We hypothesize this AI algorithm, trained on NF2-SWN-related VS data, will accurately apply to non-NF2-SWN VS and VS treated with GKS. In this retrospective cohort study, we reviewed data from an established Gamma Knife database, identifying 16 patients who underwent GKS for VS and had pre- and post-GKS scans. Contrast-enhanced T1-weighted MRI scans were analyzed with both manual segmentation and the AI algorithm. DICE similarity coefficients were computed to compare AI and manual segmentations, and a paired t-test was used to assess statistical significance. Volume changes for pre- and post-GKS scans were calculated for both segmentation methods. The mean DICE score between AI and manual segmentations was 0.91 (range 0.79-0.97). Pre- and post-GKS DICE scores were 0.91 (range 0.79-0.97) and 0.92 (range 0.81-0.97), indicating high spatial overlap. AI-segmented VS volumes pre- and post-GKS were consistent with manual measurements, with high DICE scores indicating strong spatial overlap. The AI algorithm processed scans within 5 min, suggesting it offers a reliable, efficient alternative for clinical monitoring. DICE scores showed high similarity between manual and AI segmentations. The pre- and post-GKS VS volume percentage changes were also similar between manual and AI-segmented VS volumes, indicating that our AI algorithm can accurately detect changes in tumor growth.

Embolic protection devices (EPDs) used during carotid artery stenting (CAS) are crucial in reducing ischemic complications. Although minimizing the filter-type EPD movement is considered important, limited research has demonstrated this practice. We used an artificial intelligence (AI)-based device recognition technology to investigate the correlation between filter movements and ischemic complications. We retrospectively studied 28 consecutive patients who underwent CAS using FilterWire EZ (Boston Scientific, Marlborough, MA, USA) from April 2022 to September 2023. Clinical data, procedural videos, and postoperative magnetic resonance imaging were collected. An AI-based device detection function in the Neuro-Vascular Assist (iMed Technologies, Tokyo, Japan) was used to quantify the filter movement. Multivariate proportional odds model analysis was performed to explore the correlations between postoperative diffusion-weighted imaging (DWI) hyperintense lesions and potential ischemic risk factors, including filter movement. In total, 23 patients had sufficient information and were eligible for quantitative analysis. Fourteen patients (60.9 %) showed postoperative DWI hyperintense lesions. Multivariate analysis revealed significant associations between filter movement distance (odds ratio, 1.01; 95 % confidence interval, 1.00-1.02; p = 0.003) and high-intensity signals in time-of-flight magnetic resonance angiography with DWI hyperintense lesions. Age, symptomatic status, and operative time were not significantly correlated. Increased filter movement during CAS was correlated with a higher incidence of postoperative DWI hyperintense lesions. AI-based quantitative evaluation of endovascular techniques may enable demonstration of previously unproven recommendations. To the best of our knowledge, this is the first study to use an AI system for quantitative evaluation to address real-world clinical issues.

Periodontitis is a severe infection affecting oral and systemic health and is traditionally diagnosed through clinical probing-a process that is time-consuming, uncomfortable for patients, and subject to variability based on the operator's skill. We hypothesized that computer vision can be used to estimate periodontal stability from radiographs alone. At the tooth level, we used intraoral radiographs to detect and categorize individual teeth according to their periodontal stability and corresponding treatment needs: healthy (prevention), stable (maintenance), and unstable (active treatment). At the patient level, we assessed full-mouth series and classified patients as stable or unstable by the presence of at least 1 unstable tooth. Our 3-way tooth classification model achieved an area under the receiver operating characteristic curve of 0.71 for healthy teeth, 0.56 for stable, and 0.67 for unstable. The model achieved an F₁ score of 0.45 for healthy teeth, 0.57 for stable, and 0.54 for unstable (recall, 0.70). Saliency maps generated by gradient-weighted class activation mapping primarily showed highly activated areas corresponding to clinically probed regions around teeth. Our binary patient classifier achieved an area under the receiver operating characteristic curve of 0.68 and an F₁ score of 0.74 (recall, 0.70). Taken together, our results suggest that it is feasible to estimate periodontal stability, which traditionally requires clinical and radiographic examination, from radiographic signal alone using computer vision. Variations in model performance across different classes at the tooth level indicate the necessity of further refinement.

The nasopalatine canal (NPC) is an anatomical formation with varying morphology. NPC can be visualized using the cone-beam computed tomography (CBCT). Also, CBCT has been used in many studies on artificial intelligence (AI). The "You only look once" (YOLO) is an AI framework that stands out with its speed. This study compared the observer and AI regarding the NPC segmentation and assessment of the NPC furcation status in CBCT images. In this study, axial sections of 200 CBCT images were used. These images were labeled and evaluated for the absence or presence of the NPC furcation. These images were then divided into three; 160 images were used as the training dataset, 20 as the validation dataset, and 20 as the test dataset. The training was performed by making 800 epochs using the YOLOv5x-seg model. Sensitivity, Precision, F1 score, IoU, mAP, and AUC values were determined for NPC detection, segmentation, and classification of the YOLOv5x-seg model. The values were found to be 0.9680, 0.9953, 0.9815, 0.9636, 0.7930, and 0.8841, respectively, for the group with the absence of the NPC furcation; and 0.9827, 0.9975, 0.9900, 0.9803, 0.9637, and 0.9510, for the group with the presence of the NPC furcation. Our results showed that even when the YOLOv5x-seg model is trained with the NPC furcation and fewer datasets, it achieves sufficient prediction accuracy. The segmentation feature of the YOLOv5 algorithm, which is based on an object detection algorithm, has achieved quite successful results despite its recent development.

U-Net is a commonly used model for medical image segmentation. However, when applied to chest X-ray images that show pathologies, it often fails to include these critical pathological areas in the generated masks. To address this limitation, in our study, we tackled the challenge of precise segmentation and mask generation by developing a novel approach, using CycleGAN, that encompasses the areas affected by pathologies within the region of interest, allowing the extraction of relevant radiomic features linked to pathologies. Furthermore, we adopted a feature selection approach to focus the analysis on the most significant features. The results of our proposed pipeline are promising, with an average accuracy of 92.05% and an average AUC of 89.48% for the multi-label classification of effusion and infiltration acquired from the ChestX-ray14 dataset, using the XGBoost model. Furthermore, applying our methodology to the classification of the 14 diseases in the ChestX-ray14 dataset resulted in an average AUC of 83.12%, outperforming previous studies. This research highlights the importance of effective pathological mask generation and features selection for accurate classification of chest diseases. The promising results of our approach underscore its potential for broader applications in the classification of chest diseases.