Video capsule endoscopy (VCE) originally revolutionized gastrointestinal imaging by providing a noninvasive method for evaluating small bowel diseases. Recent technological innovations, including enhanced imaging systems, artificial intelligence (AI), and improved localization, have significantly improved VCE's diagnostic accuracy, efficiency, and clinical utility. This review aims to summarize and evaluate recent technological advances in VCE, focusing on system comparisons, image enhancement, localization technologies, and AI-assisted lesion detection.

Given the high burden of hepatocellular carcinoma (HCC), risk stratification in patients with cirrhosis is critical but remains inadequate. In this study, we aimed to develop and validate an HCC prediction model by integrating radiomics and deep learning features from liver and spleen computed tomography (CT) images into the established age-male-ALBI-platelet (aMAP) clinical model. Patients were enrolled between 2018 and 2023 from a Chinese multicenter, prospective, observational cirrhosis cohort, all of whom underwent 3-phase contrast-enhanced abdominal CT scans at enrollment. The aMAP clinical score was calculated, and radiomic (PyRadiomics) and deep learning (ResNet-18) features were extracted from liver and spleen regions of interest. Feature selection was performed using the least absolute shrinkage and selection operator. Among 2,411 patients (median follow-up: 42.7 months [IQR: 32.9-54.1]), 118 developed HCC (three-year cumulative incidence: 3.59%). Chronic hepatitis B virus infection was the main etiology, accounting for 91.5% of cases. The aMAP-CT model, which incorporates CT signatures, significantly outperformed existing models (area under the receiver-operating characteristic curve: 0.809-0.869 in three cohorts). It stratified patients into high-risk (three-year HCC incidence: 26.3%) and low-risk (1.7%) groups. Stepwise application (aMAP → aMAP-CT) further refined stratification (three-year incidences: 1.8% [93.0% of the cohort] vs. 27.2% [7.0%]). The aMAP-CT model improves HCC risk prediction by integrating CT-based liver and spleen signatures, enabling precise identification of high-risk cirrhosis patients. This approach personalizes surveillance strategies, potentially facilitating earlier detection and improved outcomes.

To evaluate the predictive performance of a clinical-CT-radiomics nomogram based on radiomics signature and independent clinical-CT predictors for predicting endoscopic red color sign (RC) in cirrhotic patients with esophageal varices (EV). We retrospectively evaluated 215 cirrhotic patients. Among them, 108 and 107 cases were positive and negative for endoscopic RC, respectively. Patients were assigned to a training cohort (n = 150) and a validation cohort (n = 65) at a 7:3 ratio. In the training cohort, univariate and multivariate logistic regression analyses were performed on clinical and CT features to develop a clinical-CT model. Radiomic features were extracted from portal venous phase CT images to generate a Radiomic score (Rad-score) and to construct five machine learning models. A combined model was built using clinical-CT predictors and Rad-score through logistic regression. The performance of different models was evaluated using the receiver operating characteristic (ROC) curves and the area under the curve (AUC). The spleen-to-platelet ratio, liver volume, splenic vein diameter, and superior mesenteric vein diameter were independent predictors. Six radiomics features were selected to construct five machine learning models. The adaptive boosting model showed excellent predictive performance, achieving an AUC of 0.964 in the validation cohort, while the combined model achieved the highest predictive accuracy with an AUC of 0.985 in the validation cohort. The clinical-CT-radiomics nomogram demonstrates high predictive accuracy for endoscopic RC in cirrhotic patients with EV, which provides a novel tool for non-invasive prediction of esophageal varices bleeding.

The validation of multimodal deep learning models for medical image translation is limited by the lack of high-quality, paired datasets. We propose a novel framework that leverages computational phantoms to generate realistic CT and MRI images, enabling reliable ground-truth datasets for robust validation of artificial intelligence (AI) methods that generate synthetic CT (sCT) from MRI, specifically for radiotherapy applications. Two CycleGANs (cycle-consistent generative adversarial networks) were trained to transfer the imaging style of real patients onto CT and MRI phantoms, producing synthetic data with realistic textures and continuous intensity distributions. These data were evaluated through paired assessments with original phantoms, unpaired comparisons with patient scans, and dosimetric analysis using patient-specific radiotherapy treatment plans. Additional external validation was performed on public CT datasets to assess the generalizability to unseen data. The resulting, paired CT/MRI phantoms were used to validate a GAN-based model for sCT generation from abdominal MRI in particle therapy, available in the literature. Results showed strong anatomical consistency with original phantoms, high histogram correlation with patient images (HistCC = 0.998 ± 0.001 for MRI, HistCC = 0.97 ± 0.04 for CT), and dosimetric accuracy comparable to real data. The novelty of this work lies in using generated phantoms as validation data for deep learning-based cross-modality synthesis techniques.

Prostate-specific membrane antigen (PSMA) PET/CT plays an increasing role in prostate cancer management. Radiomics analysis of PSMA PET/CT images may provide additional information for risk stratification. This study aimed to evaluate the performance of PSMA PET/CT radiomics analysis in differentiating between Gleason Grade Groups (GGG 1–3 vs. GGG 4–5) and predicting PSA levels (below vs. at or above 20 ng/ml) in patients with newly diagnosed prostate cancer. In this multicenter study, patients with confirmed primary prostate cancer were enrolled who underwent [68Ga]Ga-PSMA PET/CT for staging. Inclusion criteria required intraprostatic lesions on PET and the International Society of Urological Pathology (ISUP) grade information. Three different segments were delineated including intraprostatic PSMA-avid lesions on PET, the whole prostate in PET, and the whole prostate in CT. Radiomic features (RFs) were extracted from all segments. Dimensionality reduction was achieved through principal component analysis (PCA) prior to model training on data from two centers (186 cases) with 10-fold cross-validation. Model performance was validated with external data set (57 cases) using various machine learning models including random forest, nearest centroid, support vector machine (SVM), calibrated classifier CV and logistic regression. In this retrospective study, 243 patients with a median age of 69 (range: 46–89) were enrolled. For distinguishing GGG 1–3 from GGG 4–5, the nearest centroid classifier using radiomic features (RFs) from whole-prostate PET achieved the best performance in the internal test set, while the random forest classifier using RFs from PSMA-avid lesions in PET performed best in the external test set. However, when considering both internal and external test sets, a calibrated classifier CV using RFs from PSMA-avid PET data showed slightly improved overall performance. Regarding PSA level classification (< 20 ng/ml vs. ≥20 ng/ml), the nearest centroid classifier using RFs from the whole prostate in PET achieved the best performance in the internal test set. In the external test set, the highest performance was observed using RFs derived from the concatenation of PET and CT. Notably, when combining both internal and external test sets, the best performance was again achieved with RFs from the concatenated PET/CT data. Our research suggests that [68Ga]Ga-PSMA PET/CT radiomic features, particularly features derived from intraprostatic PSMA-avid lesions, may provide valuable information for pre-biopsy risk stratification in newly diagnosed prostate cancer.

Ensuring the seamless transfer of knowledge and models across various datasets and clinical contexts is of paramount importance in medical image segmentation. This is especially true for liver lesion segmentation which plays a key role in pre-operative planning and treatment follow-up. Despite the progress of deep learning algorithms using Transformers, automatically segmenting small hepatic metastases remains a persistent challenge. This can be attributed to the degradation of small structures due to the intrinsic process of feature down-sampling inherent to many deep architectures, coupled with the imbalance between foreground metastases voxels and background. While similar challenges have been observed for liver tumors originated from hepatocellular carcinoma, their manifestation in the context of liver metastasis delineation remains under-explored and require well-defined guidelines. Through comprehensive experiments, this paper aims to bridge this gap and to demonstrate the impact of various transfer learning schemes from off-the-shelf datasets to a dataset containing liver metastases only. Our scale-specific evaluation reveals that models trained from scratch or with domain-specific pre-training demonstrate greater proficiency.

Inflammatory bowel disease (IBD) is a chronic inflammatory condition of the intestines with a rising global incidence. Colonoscopy remains the gold standard for IBD diagnosis, but traditional image-scoring methods are subjective and complex, impacting diagnostic accuracy and efficiency. To address these limitations, this paper investigates machine learning techniques for intestinal ulcer segmentation, focusing on multi-category ulcer segmentation to enhance IBD diagnosis. We identified two primary challenges in intestinal ulcer segmentation: 1) labeling noise, where inaccuracies in medical image annotation introduce ambiguity, hindering model training, and 2) performance variability across datasets, where models struggle to maintain high accuracy due to medical image diversity. To address these challenges, we propose an active ulcer segmentation algorithm based on fuzzy labeling. A collaborative training segmentation model is designed to utilize pixel-wise confidence extracted from fuzzy labels, distinguishing high- and low-confidence regions, and enhancing robustness to noisy labels through network cooperation. To mitigate performance disparities, we introduce a data adaptation strategy leveraging active learning. By selecting high-information samples based on uncertainty and diversity, the strategy enables incremental model training, improving adaptability. Extensive experiments on public and hospital datasets validate the proposed methods. Our collaborative training model and active learning strategy show significant advantages in handling noisy labels and enhancing model performance across datasets, paving the way for more precise and efficient IBD diagnosis.

Deep learning (DL) is increasingly explored in interventional radiology (IR) applications. This systematic review evaluates current DL-based applications for digital subtraction angiography (DSA) in abdominopelvic interventions, summarizes performance, and identifies gaps in the literature. Following PRISMA guidelines, we searched MEDLINE, Scopus, and Google Scholar for studies published up to February 1, 2025. English-language original articles assessing DL methods for automatic DSA image analysis were included, and study quality was evaluated with QUADAS-2. Nine studies were included. Two focused on hemorrhage detection, in which area under the curve (AUC) values ranged between 0.80-0.85. Four examined image enhancement, one performed vessel segmentation, and one applied classification of the anatomic location. Only a single study evaluated treatment response prediction, with an accuracy of 0.75. Most models were tested on small datasets from single centers, limiting their generalizability. Preliminary studies show that DL can improve hemorrhage detection, image quality, and vessel segmentation in DSA. However, larger, prospectively validated datasets are warranted. Currently no FDA-approved DL tools exist for abdominal or pelvic DSA. Future efforts should explore advanced generative AI and multimodal approaches.

Deep learning has significantly advanced medical imaging, particularly computed tomography (CT), which is vital for diagnosing heart and cancer patients, evaluating treatments, and tracking disease progression. High-quality CT images enhance clinical decision-making, making image reconstruction a key research focus. This study develops a framework to improve CT image quality while minimizing reconstruction time. The proposed four-step medical image analysis framework includes reconstruction, preprocessing, segmentation, and image description. Initially, raw projection data undergoes reconstruction via a Radon transform to generate a sinogram, which is then used to construct a CT image of the pelvis. A convolutional neural network (CNN) ensures high-quality reconstruction. A bilateral filter reduces noise while preserving critical anatomical features. If required, a medical expert can review the image. The K-means clustering algorithm segments the preprocessed image, isolating the pelvis and removing irrelevant structures. Finally, the FuseCap model generates an automated textual description to assist radiologists. The framework's effectiveness is evaluated using peak signal-to-noise ratio (PSNR), normalized mean square error (NMSE), and structural similarity index measure (SSIM). The achieved values-PSNR 30.784, NMSE 0.032, and SSIM 0.877-demonstrate superior performance compared to existing methods. The proposed framework reconstructs high-quality CT images from raw projection data, integrating segmentation and automated descriptions to provide a decision-support tool for medical experts. By enhancing image clarity, segmenting outputs, and providing descriptive insights, this research aims to reduce the workload of frontline medical professionals and improve diagnostic efficiency.

Rectocele (RC) is a common pelvic organ prolapse (POP) that can cause obstructed defecation and reduced quality of life. Magnetic resonance defecography (MRD) offers high-resolution, radiation-free visualization of pelvic floor anatomy but relies on time-consuming, observer-dependent manual measurements. Our research constructs a nomogram model incorporating intra-ROI and habitat radiomics features to improve MRD-based RC diagnosis. We retrospectively analyzed 222 MRD patients (155 training, 67 testing). Clinical features were selected via univariate and multivariate logistic regression. The least absolute shrinkage and selection operator (LASSO) algorithm was applied, and features with non-zero coefficients were retained to construct the radiomics signatures. A support vector machine (SVM) learning algorithm was used to construct the intra-ROI combined with the habitat radiomics model. Clinical features were then combined with radiomics features using a multivariable logistic regression algorithm to generate a clinical-radiomics nomogram. Model performance was assessed using receiver operating characteristic curve (ROC) and decision curve analysis (DCA). The combined intra-ROI and habitat radiomics model outperformed intra-ROI or habitat radiomics models alone, achieving areas under the curve (AUCs) of 0.913 (training) and 0.805 (testing). The nomogram integrating radiomics features and gender showed strong calibration and discrimination, with AUCs of 0.930 and 0.852 in the training and testing cohorts, respectively. Our findings suggest that integrating intra-ROI with habitat radiomics features can aid RC assessment. While the clinical-radiomics nomogram showed the highest internal performance, this single-center retrospective study lacks external validation and includes a relatively small test cohort. Therefore, risk of model overfitting cannot be excluded. Prospective, multi-center validation and larger cohorts are warranted before routine clinical deployment.