Accurate estimation of intravoxel incoherent motion (IVIM) parameters from diffusion-weighted MRI remains challenging due to the ill-posed nature of the inverse problem and high sensitivity to noise, particularly in the perfusion compartment. In this work, we propose a probabilistic deep learning framework based on Deep Ensembles (DE) of Mixture Density Networks (MDNs), enabling estimation of total predictive uncertainty and decomposition into aleatoric (AU) and epistemic (EU) components. The method was benchmarked against non probabilistic neural networks, a Bayesian fitting approach and a probabilistic network with single Gaussian parametrization. Supervised training was performed on synthetic data, and evaluation was conducted on both simulated and two in vivo datasets. The reliability of the quantified uncertainties was assessed using calibration curves, output distribution sharpness, and the Continuous Ranked Probability Score (CRPS). MDNs produced more calibrated and sharper predictive distributions for the D and f parameters, although slight overconfidence was observed in D*. The Robust Coefficient of Variation (RCV) indicated smoother in vivo estimates for D* with MDNs compared to Gaussian model. Despite the training data covering the expected physiological range, elevated EU in vivo suggests a mismatch with real acquisition conditions, highlighting the importance of incorporating EU, which was allowed by DE. Overall, we present a comprehensive framework for IVIM fitting with uncertainty quantification, which enables the identification and interpretation of unreliable estimates. The proposed approach can also be adopted for fitting other physical models through appropriate architectural and simulation adjustments.

This study aims to develop and validate a multi-modal machine learning model for preoperative risk stratification in papillary thyroid carcinoma (PTC), addressing limitations of current systems that rely on postoperative pathological features. We analyzed 974 PTC patients from three medical centers in China using a multi-modal approach integrating: (1) clinical indicators, (2) immunological indices, (3) ultrasound radiomics features, and (4) CT radiomics features. Our methodology employed gradient boosting machine for feature selection and random forest for classification, with model interpretability provided through SHapley Additive exPlanations (SHAP) analysis. The model was validated on internal (n = 225) and two external cohorts (n = 51, n = 174). The final 15-feature model achieved AUCs of 0.91, 0.84, and 0.77 across validation cohorts, improving to 0.96, 0.95, and 0.89 after cohort-specific refitting. SHAP analysis revealed CT texture features, ultrasound morphological features, and immune-inflammatory markers as key predictors, with consistent patterns across validation sites despite center-specific variations. Subgroup analysis showed superior performance in tumors > 1 cm and patients without extrathyroidal extension. Our multi-modal machine learning approach provides accurate preoperative risk stratification for PTC with robust cross-center applicability. This computational framework for integrating heterogeneous imaging and clinical data demonstrates the potential of multi-modal joint learning in healthcare imaging to transform clinical decision-making by enabling personalized treatment planning.

To develop and validate a hybrid radiomics model to predict the overall survival in pancreatic cancer patients and identify risk factors that affect patient prognosis. We conducted a retrospective analysis of 272 pancreatic cancer patients diagnosed at the First Affiliated Hospital of Soochow University from January 2013 to December 2023, and divided them into a training set and a test set at a ratio of 7:3. Pre-treatment contrast-enhanced computed tomography (CT), magnetic resonance imaging (MRI) images, and clinical features were collected. Dimensionality reduction was performed on the radiomics features using principal component analysis (PCA), and important features with non-zero coefficients were selected using the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation. In the training set, we built clinical prediction models using both random survival forests (RSF) and traditional Cox regression analysis. These models included a radiomics model based on contrast-enhanced CT, a radiomics model based on MRI, a clinical model, 3 bimodal models combining two types of features, and a multimodal model combining radiomics features with clinical features. Model performance evaluation in the test set was based on two dimensions: discrimination and calibration. In addition, risk stratification was performed in the test set based on predicted risk scores to evaluate the model's prognostic utility. The RSF-based hybrid model performed best with a C-index of 0.807 and a Brier score of 0.101, outperforming the COX hybrid model (C-index of 0.726 and a Brier score of 0.145) and other unimodal and bimodal models. The SurvSHAP(t) plot highlighted CA125 as the most important variable. In the test set, patients were stratified into high- and low-risk groups based on the predicted risk scores, and Kaplan-Meier analysis demonstrated a significant survival difference between the two groups (p < 0.0001). A multi-modal model using radiomics based on clinical tabular data and contrast-enhanced CT and MRI was developed by RSF, presenting strengths in predicting prognosis in pancreatic cancer patients.

Accurate preoperative prediction of erectile dysfunction (ED) is important for counseling patients undergoing radical prostatectomy. While clinical features are established predictors, the added value of preoperative MRI remains underexplored. We investigate whether MRI provides additional predictive value for ED at 12 months post-surgery, evaluating four modeling strategies: (1) a clinical-only baseline, representing current state-of-the-art; (2) classical models using handcrafted anatomical features derived from MRI; (3) deep learning models trained directly on MRI slices; and (4) multimodal fusion of imaging and clinical inputs. Imaging-based models (maximum AUC 0.569) slightly outperformed handcrafted anatomical approaches (AUC 0.554) but fell short of the clinical baseline (AUC 0.663). Fusion models offered marginal gains (AUC 0.586) but did not exceed clinical-only performance. SHAP analysis confirmed that clinical features contributed most to predictive performance. Saliency maps from the best-performing imaging model suggested a predominant focus on anatomically plausible regions, such as the prostate and neurovascular bundles. While MRI-based models did not improve predictive performance over clinical features, our findings suggest that they try to capture patterns in relevant anatomical structures and may complement clinical predictors in future multimodal approaches.

Augmented reality (AR) enhances surgical navigation by superimposing visible anatomical structures with three-dimensional virtual models using head-mounted displays (HMDs). In particular, interventions such as open liver surgery can benefit from AR navigation, as it aids in identifying and distinguishing tumors and risk structures. However, there is a lack of automatic and markerless methods that are robust against real-world challenges, such as partial occlusion and organ motion. We introduce a novel multi-device approach for automatic live navigation in open liver surgery that enhances the visualization and interaction capabilities of a HoloLens 2 HMD through precise and reliable registration using an Intel RealSense RGB-D camera. The intraoperative RGB-D segmentation and the preoperative CT data are utilized to register a virtual liver model to the target anatomy. An AR-prompted Segment Anything Model (SAM) enables robust segmentation of the liver in situ without the need for additional training data. To mitigate algorithmic latency, Double Exponential Smoothing (DES) is applied to forecast registration results. We conducted a phantom study for open liver surgery, investigating various scenarios of liver motion, viewpoints, and occlusion. The mean registration errors (8.31 mm-18.78 mm TRE) are comparable to those reported in prior work, while our approach demonstrates high success rates even for high occlusion factors and strong motion. Using forecasting, we bypassed the algorithmic latency of 79.8 ms per frame, with median forecasting errors below 2 mms and 1.5 degrees between the quaternions. To our knowledge, this is the first work to approach markerless in situ visualization by combining a multi-device method with forecasting and a foundation model for segmentation and tracking. This enables a more reliable and precise AR registration of surgical targets with low latency. Our approach can be applied to other surgical applications and AR hardware with minimal effort.

Conventional systems supporting percutaneous radiofrequency ablation (PRFA) have faced difficulties in ensuring safe and accurate puncture due to issues inherent to the medical images used and organ displacement caused by patients' respiration. To address this problem, this study proposes a semi-automatic puncture robot system that integrates real-time ultrasound (US) images with computed tomography (CT) images. The purpose of this paper is to evaluate the system's usefulness through a pilot clinical experiment involving participants. For the clinical experiment using the proposed system, an improved U-net model based on fivefold cross-validation was constructed. Following the workflow of the proposed system, the model was trained using US images acquired from patients with robotic arms. The average Dice coefficient for the entire validation dataset was confirmed to be 0.87. Therefore, the model was implemented in the robotic system and applied to clinical experiment. A clinical experiment was conducted using the robotic system equipped with the developed AI model on five adult male and female participants. The centroid distances between the point clouds from each modality were evaluated in the 3D US-CT fusion process, assuming the blood vessel centerline represents the overall structural position. The results of the centroid distances showed a minimum value of 0.38 mm, a maximum value of 4.81 mm, and an average of 1.97 mm. Although the five participants had different CP classifications and the derived US images exhibited individual variability, all centroid distances satisfied the ablation margin of 5.00 mm considered in PRFA, suggesting the potential accuracy and utility of the robotic system for puncture navigation. Additionally, the results suggested the potential generalization performance of the AI model trained with data acquired according to the robotic system's workflow.

This study employed machine learning models to quantitatively analyze liver fat content from MRI images for the evaluation of liver fibrosis and disease severity in patients with metabolic dysfunction-associated fatty liver disease (MAFLD). A total of 26 confirmed MAFLD cases, along with MRI image sequences obtained from public repositories, were included to perform a comprehensive assessment. Radiomics features-such as contrast, correlation, homogeneity, energy, and entropy-were extracted and used to construct a random forest classification model with optimized hyperparameters. The model achieved outstanding performance, with an accuracy of 96.8%, sensitivity of 95.7%, specificity of 97.8%, and an F1-score of 96.8%, demonstrating its strong capability in accurately evaluating the degree of liver fibrosis and overall disease severity in MAFLD patients. The integration of machine learning with MRI-based analysis offers a promising approach to enhancing clinical decision-making and guiding treatment strategies, underscoring the potential of advanced technologies to improve diagnostic precision and disease management in MAFLD.

Radiologists often mix medical image reading strategies, including inspection of individual modalities and local image regions, using information at different locations from different images independently as well as concurrently. In this paper, we propose a recommend system to assist machine learning-based segmentation models, by suggesting appropriate image portions along with the best modality, such that prostate cancer segmentation performance can be maximised. Our approach trains a policy network that assists tumor localisation, by recommending both the optimal imaging modality and the specific sections of interest for review. During training, a pre-trained segmentation network mimics radiologist inspection on individual or variable combinations of these imaging modalities and their sections - selected by the policy network. Taking the locally segmented regions as an input for the next step, this dynamic decision making process iterates until all cancers are best localised. We validate our method using a data set of 1325 labelled multiparametric MRI images from prostate cancer patients, demonstrating its potential to improve annotation efficiency and segmentation accuracy, especially when challenging pathology is present. Experimental results show that our approach can surpass standard segmentation networks. Perhaps more interestingly, our trained agent independently developed its own optimal strategy, which may or may not be consistent with current radiologist guidelines such as PI-RADS. This observation also suggests a promising interactive application, in which the proposed policy networks assist human radiologists.

This study investigates how deep learning (DL) can enhance ovarian cancer diagnosis and staging using large imaging datasets. Specifically, we compare six conventional convolutional neural network (CNN) architectures-ResNet, DenseNet, GoogLeNet, U-Net, VGG, and AlexNet-with OCDA-Net, an enhanced model designed for [¹⁸F]FDG PET image analysis. The OCDA-Net, an advancement on the ResNet architecture, was thoroughly compared using randomly split datasets of training (80%), validation (10%), and test (10%) images. Trained over 100 epochs, OCDA-Net achieved superior diagnostic classification with an accuracy of 92%, and staging results of 94%, supported by robust precision, recall, and F-measure metrics. Grad-CAM ++ heat-maps confirmed that the network attends to hyper-metabolic lesions, supporting clinical interpretability. Our findings show that OCDA-Net outperforms existing CNN models and has strong potential to transform ovarian cancer diagnosis and staging. The study suggests that implementing these DL models in clinical practice could ultimately improve patient prognoses. Future research should expand datasets, enhance model interpretability, and validate these models in clinical settings.

For patients with locally advanced cervical cancer (LACC), precise survival prediction models could guide personalized treatment. We developed and validated CerviPro, a deep learning-based multimodal prognostic model, to predict disease-free survival (DFS) in 1018 patients with LACC receiving definitive radiotherapy. The model integrates pre- and post-treatment CT imaging, handcrafted radiomic features, and clinical variables. CerviPro demonstrated robust predictive performance in the internal validation cohort (C-index 0.81), and external validation cohorts (C-index 0.70&0.66), significantly stratifying patients into distinct high- and low-risk DFS groups. Multimodal feature fusion consistently outperformed models based on single feature categories (clinical data, imaging, or radiomics alone), highlighting the synergistic value of integrating diverse data sources. By integrating multimodal data to predict DFS and recurrence risk, CerviPro provides a clinically valuable prognostic tool for LACC, offering the potential to guide personalized treatment strategies.