Advances in treatment technology now allow for the use of customizable 3D-printed hydrogel wound dressings for patients with osteoradionecrosis (ORN) of the jaw (ONJ). Meanwhile, deep learning has enabled precise segmentation of 3D medical images using tools like nnUNet. However, the scarcity of labeled data in ONJ imaging makes supervised training impractical. This study aims to develop an unsupervised training approach for automatically identifying anomalies in imaging scans. We propose a novel two-stage training pipeline. In the first stage, a VQ-GAN is trained to accurately reconstruct normal subjects. In the second stage, random cube masking and ONJ-specific masking are applied to train a new encoder capable of recovering the data. The proposed method achieves successful segmentation on both simulated and real patient data. This approach provides a fast initial segmentation solution, reducing the burden of manual labeling. Additionally, it has the potential to be directly used for 3D printing when combined with hand-tuned post-processing.

Inter reader variability and cross site domain shift challenge the automatic segmentation of prostate anatomy using T2 weighted MRI images. This study investigates whether transformer models can retain precision amid such heterogeneity. We compare the performance of UNETR and SwinUNETR in prostate gland segmentation against our previous 3D UNet model [1], based on 546 MRI (T2weighted) volumes annotated by two independent experts. Three training strategies were analyzed: single cohort dataset, 5 fold cross validated mixed cohort, and gland size based dataset. Hyperparameters were tuned by Optuna. The test set, from an independent population of readers, served as the evaluation endpoint (Dice Similarity Coefficient). In single reader training, SwinUNETR achieved an average dice score of 0.816 for Reader#1 and 0.860 for Reader#2, while UNETR scored 0.8 and 0.833 for Readers #1 and #2, respectively, compared to the baseline UNets 0.825 for Reader #1 and 0.851 for Reader #2. SwinUNETR had an average dice score of 0.8583 for Reader#1 and 0.867 for Reader#2 in cross-validated mixed training. For the gland size-based dataset, SwinUNETR achieved an average dice score of 0.902 for Reader#1 subset and 0.894 for Reader#2, using the five-fold mixed training strategy (Reader#1, n=53; Reader#2, n=87) at larger gland size-based subsets, where UNETR performed poorly. Our findings demonstrate that global and shifted-window self-attention effectively reduces label noise and class imbalance sensitivity, resulting in improvements in the Dice score over CNNs by up to five points while maintaining computational efficiency. This contributes to the high robustness of SwinUNETR for clinical deployment.

Latent diffusion models (LDM) could alleviate data scarcity challenges affecting machine learning development for medical imaging. However, medical LDM training typically relies on performance- or scientific accessibility-limiting strategies including a reliance on short-prompt text encoders, the reuse of non-medical LDMs, or a requirement for fine-tuning with large data volumes. We propose a Class-Conditioned Efficient Large Language model Adapter (CCELLA) to address these limitations. CCELLA is a novel dual-head conditioning approach that simultaneously conditions the LDM U-Net with non-medical large language model-encoded text features through cross-attention and with pathology classification through the timestep embedding. We also propose a joint loss function and a data-efficient LDM training framework. In combination, these strategies enable pathology-conditioned LDM training for high-quality medical image synthesis given limited data volume and human data annotation, improving LDM performance and scientific accessibility. Our method achieves a 3D FID score of 0.025 on a size-limited prostate MRI dataset, significantly outperforming a recent foundation model with FID 0.071. When training a classifier for prostate cancer prediction, adding synthetic images generated by our method to the training dataset improves classifier accuracy from 69% to 74%. Training a classifier solely on our method's synthetic images achieved comparable performance to training on real images alone.

Colonoscopy is a mainstay of colorectal cancer screening and has helped to lower cancer incidence and mortality. The resection of polyps during colonoscopy is critical for tissue diagnosis and prevention of colorectal cancer, albeit resulting in increased resource requirements and expense. Discarding resected benign polyps without sending for histopathological processing and confirmatory diagnosis, known as the resect and discard strategy, could enhance efficiency but is not commonly practiced due to endoscopists predominant preference for pathological confirmation. The inaccessibility of histopathology from unprocessed resected tissue hampers endoscopic decisions. We show that intraprocedural fibre-optic microscopy with ultraviolet-C surface excitation (FUSE) of polyps post-resection enables rapid diagnosis, potentially complementing endoscopic interpretation and incorporating pathologist oversight. In a clinical study of 28 patients, slide-free FUSE microscopy of freshly resected polyps yielded mucosal views that greatly magnified the surface patterns observed on endoscopy and revealed previously unavailable histopathological signatures. We term this new cross-specialty readout surface histology. In blinded interpretations of 42 polyps (19 training, 23 reading) by endoscopists and pathologists of varying experience, surface histology differentiated normal/benign, low-grade dysplasia, and high-grade dysplasia and cancer, with 100% performance in classifying high/low risk. This FUSE dataset was also successfully interpreted by foundation AI models pretrained on histopathology slides, illustrating a new potential for these models to not only expedite conventional pathology tasks but also autonomously provide instant expert feedback during procedures that typically lack pathologists. Surface histology readouts during colonoscopy promise to empower endoscopist decisions and broadly enhance confidence and participation in resect and discard. One Sentence SummaryRapid microscopy of resected polyps during colonoscopy yielded accurate diagnoses, promising to enhance colorectal screening.

BackgroundHepatic steatosis (HS) is a common cardiometabolic risk factor frequently present but under- diagnosed in patients with suspected or known coronary artery disease. We used artificial intelligence (AI) to automatically quantify hepatic tissue measures for identifying HS from CT attenuation correction (CTAC) scans during myocardial perfusion imaging (MPI) and evaluate their added prognostic value for all-cause mortality prediction. MethodsThis study included 27039 consecutive patients [57% male] with MPI scans from nine sites. We used an AI model to segment liver and spleen on low dose CTAC scans and quantify the liver measures, and the difference of liver minus spleen (LmS) measures. HS was defined as mean liver attenuation < 40 Hounsfield units (HU) or LmS attenuation < -10 HU. Additionally, we used seven sites to develop an AI liver risk index (LIRI) for comprehensive hepatic assessment by integrating the hepatic measures and two external sites to validate its improved prognostic value and generalizability for all-cause mortality prediction over HS. FindingsMedian (interquartile range [IQR]) age was 67 [58, 75] years and body mass index (BMI) was 29.5 [25.5, 34.7] kg/m2, with diabetes in 8950 (33%) patients. The algorithm identified HS in 6579 (24%) patients. During median [IQR] follow-up of 3.58 [1.86, 5.15] years, 4836 (18%) patients died. HS was associated with increased mortality risk overall (adjusted hazard ratio (HR): 1.14 [1.05, 1.24], p=0.0016) and in subpopulations. LIRI provided higher prognostic value than HS after adjustments overall (adjusted HR 1.5 [1.32, 1.69], p<0.0001 vs HR 1.16 [1.02, 1.31], p=0.0204) and in subpopulations. InterpretationsAI-based hepatic measures automatically identify HS from CTAC scans in patients undergoing MPI without additional radiation dose or physician interaction. Integrated liver assessment combining multiple hepatic imaging measures improved risk stratification for all-cause mortality. FundingNational Heart, Lung, and Blood Institute/National Institutes of Health. Research in context Evidence before this studyExisting studies show that fully automated hepatic quantification analysis from chest computed tomography (CT) scans is feasible. While hepatic measures show significant potential for improving risk stratification and patient management, CT attenuation correction (CTAC) scans from patients undergoing myocardial perfusion imaging (MPI) have rarely been utilized for concurrent and automated volumetric hepatic analysis beyond its current utilization for attenuation correction and coronary artery calcium burden assessment. We conducted a literature review on PubMed and Google Scholar on April 1st, 2025, using the following keywords: ("liver" OR "hepatic") AND ("quantification" OR "measure") AND ("risk stratification" OR "survival analysis" OR "prognosis" OR "prognostic prediction") AND ("CT" OR "computed tomography"). Previous studies have established approaches for the identification of hepatic steatosis (HS) and its prognostic value in various small- scale cohorts using either invasive biopsy or non-invasive imaging approaches. However, CT-based non- invasive imaging, existing research predominantly focuses on manual region-of-interest (ROI)-based hepatic quantification from selected CT slices or on identifying hepatic steatosis without comprehensive prognostic assessment in large-scale and multi-site cohorts, which hinders the association evaluation of hepatic steatosis for risk stratification in clinical routine with less precise estimates, weak statistical reliability, and limited subgroup analysis to assess bias effects. No existing studies investigated the prognostic value of hepatic steatosis measured in consecutive patients undergoing MPI. These patients usually present with multiple cardiovascular risk factors such as hypertension, dyslipidemia, diabetes and family history of coronary disease. Whether hepatic measures could provide added prognostic value over existing cardiometabolic factors is unknown. Furthermore, despite the diverse hepatic measures on CT in existing literature, integrated AI-based assessment has not been investigated before though it may improve the risk stratification further over HS. Lastly, previous research relied on dedicated CT scans performed for screening purposes. CTAC scans obtained routinely with MPI had never been utilized for automated HS detection and prognostic evaluation, despite being readily available at no additional cost or radiation exposure. Added value of this studyIn this multi-center (nine sites) international (three countries) study of 27039 consecutive patients undergoing myocardial perfusion imaging (MPI) with PET or SPECT, we used an innovative artificial intelligence (AI)- based approach for automatically segmenting the entire liver and spleen volumes from low-dose ungated CT attenuation correction (CTAC) scans acquired during MPI, followed by the identification of hepatic steatosis. We evaluated the added prognostic value of several key hepatic metrics--liver measures (mean attenuation, coefficient of variation (CoV), entropy, and standard deviation), and similar measures for the difference of liver minus spleen (LmS)--derived from volumetric quantification of CTAC scans with adjustment for existing clinical and MPI variables. A HS imaging criterion (HSIC: a patient has moderate or severe hepatic steatosis if the mean liver attenuation is < 40 Hounsfield unit (HU) or the difference of liver mean attenuation and spleen mean attenuation is < -10 HU) was used to detect HS. These hepatic metrics were assessed for their ability to predict all-cause mortality in a large-scale and multi-center patient cohort. Additionally, we developed and validated an eXtreme Gradient Boosting decision tree model for integrated liver assessment and risk stratification by combining the hepatic metrics with the demographic variables to derive a liver risk index (LIRI). Our results demonstrated strong associations between the hepatic metrics and all-cause mortality, even after adjustment for clinical variables, myocardial perfusion, and atherosclerosis biomarkers. Our results revealed significant differences in the association of HS with mortality in different sex, age, and race subpopulations. Similar differences were also observed in various chronic disease subpopulations such as obese and diabetic subpopulations. These results highlighted the modifying effects of various patient characteristics, partially accounting for the inconsistent association observed in existing studies. Compared with individual hepatic measures, LIRI showed significant improvement compared to HSIC-based HS in mortality prediction in external testing. All these demonstrate the feasibility of HS detection and integrated liver assessment from cardiac low-dose CT scans from MPI, which is also expected to apply for generic chest CT scans which have coverage of liver and spleen while prior studies used dedicated abdominal CT scans for such purposes. Implications of all the available evidenceRoutine point-of-care analysis of hepatic quantification can be seamlessly integrated into all MPI using CTAC scans to noninvasively identify HS at no additional cost or radiation exposure. The automatically derived hepatic metrics enhance risk stratification by providing additional prognostic value beyond existing clinical and imaging factors, and the LIRI enables comprehensive assessment of liver and further improves risk stratification and patient management.

Accurate classification of second-trimester fetal ultrasound images remains challenging due to low image quality, high intra-class variability, and significant class imbalance. In this work, we introduce a simple yet powerful, biologically inspired deep learning ensemble framework that-unlike prior studies focused on only a handful of anatomical targets-simultaneously distinguishes 16 fetal structures. Drawing on the hierarchical, modular organization of biological vision systems, our model stacks two complementary branches (a "shallow" path for coarse, low-resolution cues and a "detailed" path for fine, high-resolution features), concatenating their outputs for final prediction. To our knowledge, no existing method has addressed such a large number of classes with a comparably lightweight architecture. We trained and evaluated on 5,298 routinely acquired clinical images (annotated by three experts and reconciled via Dawid-Skene), reflecting real-world noise and variability rather than a "cleaned" dataset. Despite this complexity, our ensemble (EfficientNet-B0 + EfficientNet-B6 with LDAM-Focal loss) identifies 90% of organs with accuracy > 0.75 and 75% of organs with accuracy > 0.85-performance competitive with more elaborate models applied to far fewer categories. These results demonstrate that biologically inspired modular stacking can yield robust, scalable fetal anatomy recognition in challenging clinical settings.

BackgroundCoeliac disease, an autoimmune disorder affecting approximately 1% of the global population, is typically diagnosed on a duodenal biopsy. However, inter-pathologist agreement on coeliac disease diagnosis is only around 80%. Existing machine learning solutions designed to improve coeliac disease diagnosis often lack interpretability, which is essential for building trust and enabling widespread clinical adoption. ObjectiveTo develop an interpretable AI model capable of segmenting key histological structures in duodenal biopsies, generating explainable segmentation masks, estimating intraepithelial lymphocyte (IEL)-to-enterocyte and villus-to-crypt ratios, and diagnosing coeliac disease. DesignSemantic segmentation models were trained to identify villi, crypts, IELs, and enterocytes using 49 annotated 2048x2048 patches at 40x magnification. IEL-to-enterocyte and villus-to-crypt ratios were calculated from segmentation masks, and a logistic regression model was trained on 172 images to diagnose coeliac disease based on these ratios. Evaluation was performed on an independent test set of 613 duodenal biopsy scans from a separate NHS Trust. ResultsThe villus-crypt segmentation model achieved a mean PR AUC of 80.5%, while the IEL-enterocyte model reached a PR AUC of 82%. The diagnostic model classified WSIs with 96% accuracy, 86% positive predictive value, and 98% negative predictive value on the independent test set. ConclusionsOur interpretable AI models accurately segmented key histological structures and diagnosed coeliac disease in unseen WSIs, demonstrating strong generalization performance. These models provide pathologists with reliable IEL-to-enterocyte and villus-to-crypt ratio estimates, enhancing diagnostic accuracy. Interpretable AI solutions like ours are essential for fostering trust among healthcare professionals and patients, complementing existing black-box methodologies. What is already known on this topicPathologist concordance in diagnosing coeliac disease from duodenal biopsies is consistently reported to be below 80%, highlighting diagnostic variability and the need for improved methods. Several recent studies have leveraged artificial intelligence (AI) to enhance coeliac disease diagnosis. However, most of these models operate as "black boxes," offering limited interpretability and transparency. The lack of explainability in AI-driven diagnostic tools prevents widespread adoption by healthcare professionals and reduces patient trust. What this study addsThis study presents an interpretable semantic segmentation algorithm capable of detecting the four key histological structures essential for diagnosing coeliac disease: crypts, villi, intraepithelial lymphocytes (IELs), and enterocytes. The model accurately estimates the IEL-to-enterocyte ratio and the villus-to-crypt ratio, the latter being an indicator of villous atrophy and crypt hyperplasia, thereby providing objective, reproducible metrics for diagnosis. The segmentation outputs allow for transparent, explainable decision-making, supporting pathologists in coeliac disease diagnosis with improved accuracy and confidence. This study presents an AI model that automates the estimation of the IEL-to-enterocyte ratio--a labour-intensive task currently performed manually by pathologists in limited biopsy regions. By minimising diagnostic variability and alleviating time constraints for pathologists, the model provides an efficient and practical solution to streamline the diagnostic workflow. Tested on an independent dataset from a previously unseen source, the model demonstrates explainability and generalizability, enhancing trust and encouraging adoption in routine clinical practice. Furthermore, this approach could set a new standard for AI-assisted duodenal biopsy evaluation, paving the way for the development of interpretable AI tools in pathology to address the critical challenges of limited pathologist availability and diagnostic inconsistencies.

While Deep Learning (DL) models trained on Magnetic Resonance Imaging (MRI) have shown promise for prostate cancer detection, their lack of direct biological validation often undermines radiologists trust and hinders clinical adoption. Radiologic-histopathologic (rad-path) correlation has the potential to validate MRI-based lesion detection using digital histopathology. This study uses automated and manually annotated digital histopathology slides as a standard of reference to evaluate the spatial extent of lesion annotations derived from both radiologist interpretations and DL models previously trained on prostate bi-parametric MRI (bp-MRI). 117 histopathology slides were used as reference. Prospective patients with clinically significant prostate cancer performed a bp-MRI examination before undergoing a robotic radical prostatectomy, and each prostate specimen was sliced using a 3D-printed patient-specific mold to ensure a direct comparison between pre-operative imaging and histopathology slides. The histopathology slides and their corresponding T2-weighted MRI images were co-registered. We trained DL models for cancer detection on large retrospective datasets of T2-w MRI only, bp-MRI and histopathology images and did inference in a prospective patient cohort. We evaluated the spatial extent between detected lesions and between detected lesions and the histopathological and radiological ground-truth, using the Dice similarity coefficient (DSC). The DL models trained on digital histopathology tiles and MRI images demonstrated promising capabilities in lesion detection. A low overlap was observed between the lesion detection masks generated by the histopathology and bp-MRI models, with a DSC = 0.10. However, the overlap was equivalent (DSC = 0.08) between radiologist annotations and histopathology ground truth. A rad-path correlation pipeline was established in a prospective patient cohort with prostate cancer undergoing surgery. The correlation between rad-path DL models was low but comparable to the overlap between annotations. While DL models show promise in prostate cancer detection, challenges remain in integrating MRI-based predictions with histopathological findings.

BackgroundOsteoporosis is underdiagnosed and undertreated prompting the exploration of opportunistic screening using CT and artificial intelligence (AI). PurposeTo develop a reproducible deep learning-based convolutional neural network to automatically place a 3D region of interest (ROI) in trabecular bone, develop a correction method to normalize attenuation across different CT protocols or and scanner models, and to establish thresholds for osteoporosis in a large diverse population. MethodsA deep learning-based method was developed to automatically quantify trabecular attenuation using a 3D ROI of the thoracic and lumbar spine on chest, abdomen, or spine CTs, adjusted for different tube voltages and scanner models. Normative values, thresholds for osteoporosis of trabecular attenuation of the spine were established across a diverse population, stratified by age, sex, race, and ethnicity using reported prevalence of osteoporosis by the WHO. Results538,946 CT examinations from 283,499 patients (mean age 65 years{+/-}15, 51.2% women and 55.5% White), performed on 50 scanner models using six different tube voltages were analyzed. Hounsfield Units at 80 kVp versus 120 kVp differed by 23%, and different scanner models resulted in differences of values by < 10%. Automated ROI placement of 1496 vertebra was validated by manual radiologist review, demonstrating >99% agreement. Mean trabecular attenuation was higher in young women (<50 years) than young men (p<.001) and decreased with age, with a steeper decline in postmenopausal women. In patients older than 50 years, trabecular attention was higher in males than females (p<.001). Trabecular attenuation was highest in Blacks, followed by Asians and lowest in Whites (p<.001). The threshold for L1 in diagnosing osteoporosis was 80 HU. ConclusionDeep learning-based automated opportunistic osteoporosis screening can identify patients with low bone mineral density that undergo CT scans for clinical purposes on different scanners and protocols. Key Results 3 main results/conclusionsO_LIIn a study of 538,946 CT examinations performed in 283,499 patients using different scanner models and imaging protocols, an automated deep learning-based convolutional neural network was able to accurately place a three-dimensional regions of interest within thoracic and lumbar vertebra to measure trabecular attenuation. C_LIO_LITube voltage had a larger influence on attenuation values (23%) than scanner model (<10%). C_LIO_LIA threshold of 80 HU was identified for L1 to diagnose osteoporosis using an automated three-dimensional region of interest. C_LI

Providing effective treatment and making informed clinical decisions are essential goals of modern medicine and clinical care. We are interested in simulating disease dynamics for clinical decision-making, leveraging recent advances in large generative models. To this end, we introduce the Medical World Model (MeWM), the first world model in medicine that visually predicts future disease states based on clinical decisions. MeWM comprises (i) vision-language models to serve as policy models, and (ii) tumor generative models as dynamics models. The policy model generates action plans, such as clinical treatments, while the dynamics model simulates tumor progression or regression under given treatment conditions. Building on this, we propose the inverse dynamics model that applies survival analysis to the simulated post-treatment tumor, enabling the evaluation of treatment efficacy and the selection of the optimal clinical action plan. As a result, the proposed MeWM simulates disease dynamics by synthesizing post-treatment tumors, with state-of-the-art specificity in Turing tests evaluated by radiologists. Simultaneously, its inverse dynamics model outperforms medical-specialized GPTs in optimizing individualized treatment protocols across all metrics. Notably, MeWM improves clinical decision-making for interventional physicians, boosting F1-score in selecting the optimal TACE protocol by 13%, paving the way for future integration of medical world models as the second readers.