Accurate tumor delineation is foundational to radiotherapy. In the era of deep learning, the automation of this labor-intensive and variation-prone process is increasingly tractable. We developed a deep neural network model to segment gross tumor volumes (GTVs) in the lung and propagate them across 4D CT images to generate an internal target volume (ITV), capturing tumor motion during respiration. Using a multicenter cohort-based registry from 9 clinics across 2 health systems, we trained a 3D UNet model (iSeg) on pre-treatment CT images and corresponding GTV masks (n = 739, 5-fold cross-validation) and validated it on two independent cohorts (n = 161; n = 102). The internal cohort achieved a median Dice (DSC) of 0.73 [IQR: 0.62-0.80], with comparable performance in external cohorts (DSC = 0.70 [0.52-0.78] and 0.71 [0.59-79]), indicating multi-site validation. iSeg matched human inter-observer variability and was robust to image quality and tumor motion (DSC = 0.77 [0.68-0.86]). Machine-generated ITVs were significantly smaller than physician delineated contours (p < 0.0001), indicating more precise delineation. Notably, higher false positive voxel rate (regions segmented by the machine but not the human) were associated with increased local failure (HR: 1.01 per voxel, p = 0.03), suggesting the clinical relevance of these discordant regions. These results mark a leap in automated target volume segmentation and suggest that machine delineation can enhance the accuracy, reproducibility, and efficiency of this core task in radiotherapy.

The application of artificial intelligence (AI) in medicine has garnered significant attention in recent years, offering new possibilities for improving patient care across various domains. For herpes zoster, a viral infection caused by the reactivation of the varicella-zoster virus, AI technologies have shown remarkable potential in enhancing disease diagnosis, treatment, and management. This study aims to investigate the current research status in the use of AI for herpes zoster, offering a comprehensive synthesis of existing advancements. A systematic literature review was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Three databases of Web of Science Core Collection, PubMed, and IEEE were searched to identify relevant studies on AI applications in herpes zoster research on November 17, 2023. Inclusion criteria were as follows: (1) research articles, (2) published in English, (3) involving actual AI applications, and (4) focusing on herpes zoster. Exclusion criteria comprised nonresearch articles, non-English papers, and studies only mentioning AI without application. Two independent clinicians screened the studies, with a third senior clinician resolving disagreements. In total, 26 articles were included. Data were extracted on AI task types; algorithms; data sources; data types; and clinical applications in diagnosis, treatment, and management. Trend analysis revealed an increasing annual interest in AI applications for herpes zoster. Hospital-derived data were the primary source (15/26, 57.7%), followed by public databases (6/26, 23.1%) and internet data (5/26, 19.2%). Medical images (9/26, 34.6%) and electronic medical records (7/26, 26.9%) were the most commonly used data types. Classification tasks (85.2%) dominated AI applications, with neural networks, particularly multilayer perceptron and convolutional neural networks being the most frequently used algorithms. AI applications were analyzed across three domains: (1) diagnosis, where mobile deep neural networks, convolutional neural network ensemble models, and mixed-scale attention-based models have improved diagnostic accuracy and efficiency; (2) treatment, where machine learning models, such as deep autoencoders combined with functional magnetic resonance imaging, electroencephalography, and clinical data, have enhanced treatment outcome predictions; and (3) management, where AI has facilitated case identification, epidemiological research, health care burden assessment, and risk factor exploration for postherpetic neuralgia and other complications. Overall, this study provides a comprehensive overview of AI applications in herpes zoster from clinical, data, and algorithmic perspectives, offering valuable insights for future research in this rapidly evolving field. AI has significantly advanced herpes zoster research by enhancing diagnostic accuracy, predicting treatment outcomes, and optimizing disease management. However, several limitations exist, including potential omissions from excluding databases like Embase and Scopus, language bias due to the inclusion of only English publications, and the risk of subjective bias in study selection. Broader studies and continuous updates are needed to fully capture the scope of AI applications in herpes zoster in the future.

The automated segmentation of computed tomography (CT) images has made their opportunistic use more feasible, yet, the association of muscle area and density from multiple anatomical regions with functional outcomes and mortality risk in older adults has not been fully explored. We aimed to determine if muscle area and density at the L1 and L3 vertebra and right and left proximal thigh were similarly related to functional outcomes and 10-year mortality risk. Men from the Osteoporotic Fractures in Men (MrOS) study who had CT images, measures of grip strength, 6 m walking speed, and leg power (Nottingham Power Rig) at the baseline visit were included in the analyses (n = 3290, 73.7 ± 5.8 years). CT images were automatically segmented to derive muscle area and muscle density. Deaths were centrally adjudicated over a 10-year follow-up. Linear regression and proportional hazards were used to model relationships of CT muscle metrics with functional outcomes and mortality, respectively, while adjusting for covariates. Muscle area and density were positively related to functional outcomes regardless of anatomical region, with the most variance explained in leg power (adjusted R² = 0.40-0.46), followed by grip strength (adjusted R² = 0.25-0.29) and walking speed (adjusted R² = 0.18-0.20). A one-unit SD increase in muscle area and density was associated with a 5%-13% and 8%-21% decrease in the risk of all-cause mortality, respectively, with the strongest associations observed at the right and left thigh. Automated measures of CT muscle area and density are related to functional outcomes and risk of mortality in older men, regardless of CT anatomical region.

To evaluate the efficacy of thin-slice T₂-weighted imaging (T₂WI) and super-resolution reconstruction (SRR) for preoperative assessment of vascular invasion in pancreatic ductal adenocarcinoma (PDAC). Ninety-five PDACs with preoperative MRI were retrospectively enrolled as a training set, with non-reconstructed T₂WI (NRT₂) in different slice thicknesses (NRT₂-3, 3 mm; NRT₂-5, ≥ 5 mm). A prospective test set was collected with NRT₂-5 (n = 125) only. A deep-learning network was employed to generate reconstructed super-resolution T₂WI (SRT₂) in different slice thicknesses (SRT₂-3, 3 mm; SRT₂-5, ≥ 5 mm). Image quality was assessed, including the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and signal-intensity ratio (SIR_t/p, tumor/pancreas; SIR_t/b, tumor/background). Diagnostic efficacy for vascular invasion was evaluated using the area under the curve (AUC) and compared across different slice thicknesses before and after reconstruction. SRT₂-5 demonstrated higher SNR and SIR_t/p compared to NRT₂-5 (74.18 vs 72.46; 1.42 vs 1.30; p < 0.05). SRT₂-3 showed increased SIR_t/p and SIR_t/b over NRT₂-3 (1.35 vs 1.31; 2.73 vs 2.58; p < 0.05). SRT₂-5 showed higher CNR, SIR_t/p and SIR_t/b than NRT₂-3 (p < 0.05). NRT₂-3 outperformed NRT₂-5 in evaluating venous invasion (AUC: 0.732 vs 0.597, p = 0.021). SRR improved venous assessment (AUC: NRT₂-3, 0.927 vs 0.732; NRT₂-5, 0.823 vs 0.597; p < 0.05), and SRT₂-5 exhibits comparable efficacy to NRT₂-3 in venous assessment (AUC: 0.823 vs 0.732, p = 0.162). Thin-slice T₂WI and SRR effectively improve the image quality and diagnostic efficacy for assessing venous invasion in PDAC. Thick-slice T₂WI with SRR is a potential alternative to thin-slice T₂WI. Both thin-slice T₂-WI and SRR effectively improve image quality and diagnostic performance, providing valuable options for optimizing preoperative vascular assessment in PDAC. Non-invasive and accurate assessment of vascular invasion supports treatment planning and avoids futile surgery. Vascular invasion evaluation is critical for the surgical eligibility of PDAC. SRR improved image quality and vascular assessment in T₂WI. Utilizing thin-slice T₂WI and SRR aids in clinical decision making for PDAC.

[Objective] In single-photon emission computed tomography (SPECT) image reconstruction, limited-angle conditions lead to a loss of frequency components, which distort the reconstructed tomographic image along directions corresponding to the non-collected projection angle range. Although conventional iterative image reconstruction methods have been used to improve the reconstructed images in limited-angle conditions, the image quality is still unsuitable for clinical use. We propose a limited-angle SPECT image reconstruction method that uses an end-to-end deep image prior (DIP) framework to improve reconstructed image quality.&#xD;[Approach] The proposed limited-angle SPECT image reconstruction is an end-to-end DIP framework which incorporates a forward projection model into the loss function to optimise the neural network. By also incorporating a binary mask that indicates whether each data point in the measured projection data has been collected, the proposed method restores the non-collected projection data and reconstructs a less distorted image.&#xD;[Main results] The proposed method was evaluated using 20 numerical phantoms and clinical patient data. In numerical simulations, the proposed method outperformed existing back-projection-based methods in terms of peak signal-to-noise ratio and structural similarity index measure. We analysed the reconstructed tomographic images in the frequency domain using an object-specific modulation transfer function, in simulations and on clinical patient data, to evaluate the response of the reconstruction method to different frequencies of the object. The proposed method significantly improved the response to almost all spatial frequencies, even in the non-collected projection angle range. The results demonstrate that the proposed method reconstructs a less distorted tomographic image.&#xD;[Significance] The proposed end-to-end DIP-based reconstruction method restores lost frequency components and mitigates image distortion under limited-angle conditions by incorporating a binary mask into the loss function.

Current virtual imaging phantoms primarily emphasize geometric&#xD;accuracy of anatomical structures. However, to enhance realism, it is also important&#xD;to incorporate intra-organ detail. Because biological tissues are heterogeneous in&#xD;composition, virtual phantoms should reflect this by including realistic intra-organ&#xD;texture and material variation.&#xD;We propose training two 3D Double U-Net conditional generative adversarial&#xD;networks (3D DUC-GAN) to generate sixteen unique textures that encompass organs&#xD;found within the torso. The model was trained on 378 CT image-segmentation&#xD;pairs taken from a publicly available dataset with 18 additional pairs reserved for&#xD;testing. Textured phantoms were generated and imaged using DukeSim, a virtual CT&#xD;simulation platform.&#xD;Results showed that the deep learning model was able to synthesize realistic&#xD;heterogeneous phantoms from a set of homogeneous phantoms. These phantoms were&#xD;compared with original CT scans and had a mean absolute difference of 46.15 ± 1.06&#xD;HU. The structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR)&#xD;were 0.86 ± 0.004 and 28.62 ± 0.14, respectively. The maximum mean discrepancy&#xD;between the generated and actual distribution was 0.0016. These metrics marked&#xD;an improvement of 27%, 5.9%, 6.2%, and 28% respectively, compared to current&#xD;homogeneous texture methods. The generated phantoms that underwent a virtual&#xD;CT scan had a closer visual resemblance to the true CT scan compared to the previous&#xD;method.&#xD;The resulting heterogeneous phantoms offer a significant step toward more realistic&#xD;in silico trials, enabling enhanced simulation of imaging procedures with greater fidelity&#xD;to true anatomical variation.

Personalized medicine aims to tailor medical treatments to the unique characteristics of each patient, but its effectiveness relies on achieving diagnostic accuracy to fully understand individual variability in disease response and treatment efficacy. This systematic literature review explores the role of large language models (LLMs) in enhancing diagnostic precision and supporting the advancement of personalized medicine. A comprehensive search was conducted across Web of Science, Science Direct, Scopus, and IEEE Xplore, targeting peer-reviewed articles published in English between January 2020 and March 2025 that applied LLMs within personalized medicine contexts. Following PRISMA guidelines, 39 relevant studies were selected and systematically analyzed. The findings indicate a growing integration of LLMs across key domains such as clinical informatics, medical imaging, patient-specific diagnosis, and clinical decision support. LLMs have shown potential in uncovering subtle data patterns critical for accurate diagnosis and personalized treatment planning. This review highlights the expanding role of LLMs in improving diagnostic accuracy in personalized medicine, offering insights into their performance, applications, and challenges, while also acknowledging limitations in generalizability due to variable model performance and dataset biases. The review highlights the importance of addressing challenges related to data privacy, model interpretability, and reliability across diverse clinical scenarios. For successful clinical integration, future research must focus on refining LLM technologies, ensuring ethical standards, and validating models continuously to safeguard effective and responsible use in healthcare environments.

This study focuses on automating the classification of certain thoracic lung cancer stages in 3D ¹⁸FDG-PET/CT images according to the 9th Edition of the TNM Classification for Lung Cancer (2024). By leveraging advanced segmentation and classification techniques, we aim to enhance the accuracy of distinguishing between T4 (pulmonary nodules) Thoracic M0 and M1a (pulmonary nodules) stages. Precise segmentation of pulmonary lobes using the Pulmonary Toolkit enables the identification of tumor locations and additional malignant nodules, ensuring reliable differentiation between ipsilateral and contralateral spread. A modified ResNet-50 model is employed to classify the segmented regions. The performance evaluation shows that the model achieves high accuracy. The unchanged class has the best recall 93% and an excellent F1 score 91%. The M1a (pulmonary nodules) class performs well with an F1 score of 94%, though recall is slightly lower 91%. For T4 (pulmonary nodules) Thoracic M0, the model shows balanced performance with an F1 score of 87%. The overall accuracy is 87%, indicating a robust classification model.

Accurate segmentation of organs in the abdomen is a primary requirement for any medical analysis and treatment planning. In this study, we propose an approach based on 3D UNet and UNet +  + architectures implemented in the MONAI framework for addressing challenges that arise due to anatomical variability, complex shape rendering of organs, and noise in CT/MRI scans. The models can analyze information in three dimensions from volumetric data, making use of skip and dense connections, and optimizing the parameters using Secretary Bird Optimization (SBO), which together help in better feature extraction and boundary delineation of the structures of interest across sets of multi-organ tissues. The developed model's performance was evaluated on multiple datasets, ranging from Pancreas-CT to Liver-CT and BTCV. The results indicated that on the Pancreas-CT dataset, a DSC of 94.54% was achieved for 3D UNet, while a slightly higher DSC of 95.62% was achieved for 3D UNet +  +. Both models performed well on the Liver-CT dataset, with 3D UNet acquiring a DSC score of 95.67% and 3D UNet +  + a DSC score of 97.36%. And in the case of the BTCV dataset, both models had DSC values ranging from 93.42 to 95.31%. These results demonstrate the robustness and efficiency of the models presented for clinical applications and medical research in multi-organ segmentation. This study validates the proposed architectures, underpinning and accentuating accuracy in medical imaging, creating avenues for scalable solutions for complex abdominal-imaging tasks.

Fetal ultrasound (US) image segmentation plays an important role in fetal development assessment, maternal pregnancy management, and intrauterine surgery planning. However, obtaining large-scale, accurately annotated fetal US imaging data is time-consuming and labor-intensive, posing challenges to the application of deep learning in this field. To address this challenge, we propose a semi-supervised fetal US image segmentation method based on bidirectional prototype-guided consistency constraint (BiPCC). BiPCC utilizes the prototype to bridge labeled and unlabeled data and establishes interaction between them. Specifically, the model generates pseudo-labels using prototypes from labeled data and then utilizes these pseudo-labels to generate pseudo-prototypes for segmenting the labeled data inversely, thereby achieving bidirectional consistency. Additionally, uncertainty-based cross-supervision is incorporated to provide additional supervision signals, thereby enhancing the quality of pseudo-labels. Extensive experiments on two fetal US datasets demonstrate that BiPCC outperforms state-of-the-art methods for semi-supervised fetal US segmentation. Furthermore, experimental results on two additional medical segmentation datasets exhibit BiPCC's outstanding generalization capability for diverse medical image segmentation tasks. Our proposed method offers a novel insight for semi-supervised fetal US image segmentation and holds promise for further advancing the development of intelligent healthcare.