Universal medical image segmentation using the Segment Anything Model (SAM) remains challenging due to its limited adaptability to medical domains. Existing adaptations, such as MedSAM, enhance SAM's performance in medical imaging but at the cost of reduced generalization to unseen data. Therefore, in this paper, we propose SAM-aware Test-Time Adaptation (SAM-TTA), a fundamentally different pipeline that preserves the generalization of SAM while improving its segmentation performance in medical imaging via a test-time framework. SAM-TTA tackles two key challenges: (1) input-level discrepancies caused by differences in image acquisition between natural and medical images and (2) semantic-level discrepancies due to fundamental differences in object definition between natural and medical domains (e.g., clear boundaries vs. ambiguous structures). Specifically, our SAM-TTA framework comprises (1) Self-adaptive Bezier Curve-based Transformation (SBCT), which adaptively converts single-channel medical images into three-channel SAM-compatible inputs while maintaining structural integrity, to mitigate the input gap between medical and natural images, and (2) Dual-scale Uncertainty-driven Mean Teacher adaptation (DUMT), which employs consistency learning to align SAM's internal representations to medical semantics, enabling efficient adaptation without auxiliary supervision or expensive retraining. Extensive experiments on five public datasets demonstrate that our SAM-TTA outperforms existing TTA approaches and even surpasses fully fine-tuned models such as MedSAM in certain scenarios, establishing a new paradigm for universal medical image segmentation. Code can be found at https://github.com/JianghaoWu/SAM-TTA.

The two-dimensional computed tomography measurement of the consolidation tumor ratio (2D-CTR) has limitations in the prognostic evaluation of early-stage lung adenocarcinoma: the measurement is subject to inter-observer variability and lacks spatial information, which undermines its reliability as a prognostic tool. This study aims to investigate the value of the three-dimensional volume-based CTR (3D-CTR) in preoperative prognosis prediction for pathological Stage IA lung adenocarcinoma, and compare its predictive performance with that of 2D-CTR. A retrospective cohort of 980 patients with pathological Stage IA lung adenocarcinoma who underwent surgery was included. Preoperative thin-section CT images were processed using artificial intelligence (AI) software for 3D segmentation. Tumor solid component volume was quantified using different density thresholds (-300 to -150 HU, in 50 HU intervals), and 3D-CTR was calculated. The optimal threshold associated with prognosis was selected using multivariate Cox regression. The predictive performance of 3D-CTR and 2D-CTR for recurrence-free survival (RFS) post-surgery was compared using receiver operating characteristic (ROC) curves, and the best cutoff value was determined. The integrated discrimination improvement (IDI) was utilized to assess the enhancement in predictive efficacy of 3D-CTR relative to 2D-CTR. Among traditional preoperative factors, 2D-CTR (cutoff value 0.54, HR=1.044, P=0.001) and carcinoembryonic antigen (CEA) were identified as independent prognostic factors for RFS. In 3D analysis, -150 HU was determined as the optimal threshold for distinguishing solid components from ground-glass opacity (GGO) components. The corresponding 3D-CTR (cutoff value 0.41, HR=1.033, P<0.001) was an independent risk factor for RFS. The predictive performance of 3D-CTR was significantly superior to that of 2D-CTR (AUC: 0.867 vs. 0.840, P=0.006), with a substantial enhancement in predictive capacity, as evidenced by an IDI of 0.038 (95% CI: 0.021-0.055, P<0.001). Kaplan-Meier analysis revealed that the 5-year RFS rate for the 3D-CTR >0.41 group was significantly lower than that of the ≤0.41 group (68.5% vs. 96.7%, P<0.001). The 3D-CTR based on a -150 HU density threshold provides a more accurate prediction of postoperative recurrence risk in pathological Stage IA lung adenocarcinoma, demonstrating superior performance compared to traditional 2D-CTR.

Accurate segmentation of mammographic mass is very important as shape characteristics of these masses play a significant role for radiologist to diagnose benign and malignant cases. Recently, various deep learning segmentation algorithms have become popular for segmentation tasks. In the present work, rigorous performance analysis of ten semantic-segmentation models has been performed with 518 images taken from DDSM dataset (digital database for screening mammography) with 208 mass images ϵ BIRAD3, 150 mass images ϵ BIRAD4 and 160 mass images ϵ BIRAD5 classes, respectively. These models are (1) simple convolution series models namely- VGG16/VGG19, (2) simple convolution DAG (directed acyclic graph) models namely- U-Net (3) dilated convolution DAG models namely ResNet18/ResNet50/ShuffleNet/XceptionNet/InceptionV2/MobileNetV2 and (4) hybrid model, i.e. hybrid U-Net. On the basis of exhaustive experimentation, it was observed that dilated convolution DAG models namely- ResNet50, ShuffleNet and MobileNetV2 outperform other network models yielding cumulative JI and F1 score values of 0.87 and 0.92, 0.85 and 91, 0.84 and 0.90, respectively. The segmented images obtained by best performing models were subjectively analyzed by participating radiologist in terms of (a) size (b) margins and (c) shape characteristics. From objective and subjective analysis it was concluded that ResNet50 is the optimal model for segmentation of difficult to delineate breast masses with dense background and masses where both masses and micro-calcifications are simultaneously present. The result of the study indicates that ResNet50 model can be used in routine clinical environment for segmentation of mammographic masses.

Knee osteoarthritis (KOA) is a prevalent condition. Accurate selection between total knee arthroplasty (TKA) and unicompartmental knee arthroplasty (UKA) is crucial for optimal treatment in patients who have end-stage KOA, particularly for improving clinical outcomes and reducing healthcare costs. This study proposes a machine learning model based on a voting mechanism to enhance the accuracy of surgical decision-making for KOA patients. Radiographic data were collected from a high-volume joint arthroplasty practice, focusing on anterior-posterior, lateral, and skyline X-ray views. The dataset included 277 TKA and 293 UKA cases, each labeled through intraoperative observations (indicating whether TKA or UKA was the appropriate choice). A five-fold cross-validation approach was used for training and validation. In the proposed method, three base models were first trained independently on single-view images, and a voting mechanism was implemented to aggregate model outputs. The performance of the proposed method was evaluated by using metrics such as accuracy and the area under the receiver operating characteristic curve (AUC). The proposed method achieved an accuracy of 94.2% and an AUC of 0.98%, demonstrating superior performance compared to existing models. The voting mechanism enabled base models to effectively utilize the detailed features from all three X-ray views, leading to enhanced predictive accuracy and model interpretability. This study provides a high-accuracy method for surgical decision-making between TKA and UKA for KOA patients, requiring only standard X-rays and offering potential for clinical application in automated referrals and preoperative planning.

Computed tomography is an inadequate method for detecting myocardial focal scar (MFS) due to its moderate density resolution, which is insufficient for distinguishing MFS from artificial beam-hardening (BH). Virtual monochromatic images (VMIs) of dual-energy coronary computed tomography angiography (DECCTA) provide a variety of diagnostic information with significant potential for detecting myocardial lesions. The aim of this study was to assess whether radiomics analysis in VMIs of DECCTA can help distinguish MFS from BH. A prospective cohort of patients who were suspected with an old myocardial infarction was assembled at two different centers between Janurary 2021 and June 2024. MFS and BH segmentation and radiomics feature extraction and selection were performed on VMIs images, and four machine learning classifiers were constructed using selected strongest features. Subsequently, an independent validation was conducted, and a subjective diagnosis of the validation set was provided by an radiologist. The AUC was used to assess the performance of the radiomics models. The training set included 57 patients from center 1 (mean age, 54 years +/- 9, 55 men), and the external validation set included 10 patients from center 2 (mean age, 59 years +/- 10, 9 men). The radiomics models exhibited the highest AUC value of 0.937 (expressed at 130 keV VMIs), while the radiologist demonstrated the highest AUC value of 0.734 (expressed at 40 keV VMIs). The integration of radiomic features derived from VMIs of DECCTA with machine learning algorithms has the potential to improve the efficiency of distinguishing MFS from BH.

Deep learning models have shown remarkable success in dermatological image analysis, offering potential for automated skin disease diagnosis. Previously, convolutional neural network(CNN) based architectures have achieved immense popularity and success in computer vision (CV) based task like skin image recognition, generation and video analysis. But with the emergence of transformer based models, CV tasks are now are nowadays carrying out using these models. Vision Transformers (ViTs) is such a transformer-based models that have shown success in computer vision. It uses self-attention mechanisms to achieve state-of-the-art performance across various tasks. However, their reliance on global attention mechanisms makes them susceptible to adversarial perturbations. This paper aims to investigate the susceptibility of ViTs for medical images to adversarial watermarking-a method that adds so-called imperceptible perturbations in order to fool models. By generating adversarial watermarks through Projected Gradient Descent (PGD), we examine the transferability of such attacks to CNNs and analyze the performance defense mechanism -- adversarial training. Results indicate that while performance is not compromised for clean images, ViTs certainly become much more vulnerable to adversarial attacks: an accuracy drop of as low as 27.6%. Nevertheless, adversarial training raises it up to 90.0%.

Patients living with HIV (PLHIV) have a higher cardiovascular risk than others, which is why the early detection of atherosclerosis in this population is important. The present study reports predictive models of subclinical atherosclerosis for this population of patients, made up of variables that are easily collected in the clinic. The study design is a cross-sectional observational study. PLHIV without established cardiovascular disease were recruited for this study. Predictive models of subclinical atherosclerosis (Doppler ultrasound) were developed by testing sociodemographic variables, pathological history, data related to HIV infection, laboratory parameters, and capillaroscopy as potential predictors. Logistic regression with internal validation (bootstrapping) and machine learning techniques were used to develop the models. Data from 96 HIV patients were analysed, 19 (19.8%) of whom had subclinical atherosclerosis. The predictors that went into both machine learning models and the regression model were hypertension, dyslipidaemia, protease inhibitors, triglycerides, fibrinogen, and alkaline phosphatase. Age and C-reactive protein were also part of the machine learning models. The logistic regression model had an area under the receiver operating characteristic curve (AUC) of 0.91 (95% CI: 0.84-0.99), which became 0.80 after internal validation by bootstrapping. The ma-chine learning techniques produced models with AUCs ranging from 0.73 to 0.86. We report predictive models for subclinical atherosclerosis in PLHIV, demonstrating relevant predictive performance based on easily accessible parameters, making them potentially useful as a screening tool. However, given the study's limitations-primarily the sample size-external validation in larger cohorts is warranted.

Lung cancer is one of the main threats to global health, among lung diseases. Low-Dose Computed Tomography (LDCT) provides significant benefits for its screening but also brings new diagnostic challenges that require close attention. By searching the Web of Science core collection, we selected articles and reviews published in English between 2005 and June 2024 on topics such as "Low-dose", "CT image", and "Lung". These literatures were analyzed by bibliometric method, and CiteSpace software was used to explore the cooperation between countries, the cooperative relationship between authors, highly cited literature, and the distribution of keywords to reveal the research hotspots and trends in this field. The number of LDCT research articles show a trend of continuous growth between 2019 and 2022. The United States is at the forefront of research in this field, with a centrality of 0.31; China has also rapidly conducted research with a centrality of 0.26. The authors' co-occurrence map shows that research teams in this field are highly cooperative, and their research questions are closely related. The analysis of highly cited literature and keywords confirmed the significant advantages of LDCT in lung cancer screening, which can help reduce the mortality of lung cancer patients and improve the prognosis. "Lung cancer" and "CT" have always been high-frequency keywords, while "image quality" and "low dose CT" have become new hot keywords, indicating that LDCT using deep learning techniques has become a hot topic in early lung cancer research. The study revealed that advancements in CT technology have driven in-depth research from application challenges to image processing, with the research trajectory evolving from technical improvements to health risk assessments and subsequently to AI-assisted diagnosis. Currently, the research focus has shifted toward integrating deep learning with LDCT technology to address complex diagnostic challenges. The study also presents global research trends and geographical distributions of LDCT technology, along with the influence of key research institutions and authors. The comprehensive analysis aims to promote the development and application of LDCT technology in pulmonary disease diagnosis and enhance diagnostic accuracy and patient management efficiency. The future will focus on LDCT reconstruction algorithms to balance image noise and radiation dose. AI-assisted multimodal imaging supports remote diagnosis and personalized health management by providing dynamic analysis, risk assessment, and follow-up recommendations to support early diagnosis.

BackgroundImpulse control disorders (ICD) in Parkinsons disease (PD) patients mainly occur as adverse effects of dopamine replacement therapy. Despite several known risk factors associated with ICD development, this cannot yet be accurately predicted at PD diagnosis. ObjectivesWe aimed to investigate the predictability of incident ICD by baseline measures of demographic, clinical, dopamine transporter single photon emission computed tomography (DAT-SPECT), and genetic variables. MethodsWe used demographic and clinical data of medication-free PD patients from two longitudinal datasets; Parkinsons Progression Markers Initiative (PPMI) (n=311) and Amsterdam UMC (n=72). We extracted radiomic and latent features from DAT-SPECT. We used single nucleotic polymorphisms (SNPs) from PPMIs NeuroX and Exome sequencing data. Four machine learning classifiers were trained on combinations of the input feature sets, to predict incident ICD at any follow-up assessment. Classification performance was measured with 10x5-fold cross-validation. ResultsICD prevalence at any follow-up was 0.32. The highest performance in predicting incident ICD (AUC=0.66) was achieved by the models trained on clinical features only. Anxiety severity and age of PD onset were identified as the most important features. Performance did not improve with adding features from DAT-SPECT or SNPs. We observed significantly higher performance (AUC=0.74) when classifying patients who developed ICD within four years from diagnosis compared with those tested negative for seven or more years. ConclusionsPrediction accuracy for later ICD development, at the time of PD diagnosis, is limited; however, it increases for shorter time-to-event predictions. Neither DAT-SPECT nor genetic data improve the predictability obtained using demographic and clinical variables alone.

Computed Tomography (CT) is widely acknowledged as the gold standard for diagnosing thoracic diseases. However, the accuracy of interpretation significantly depends on radiologists' expertise. Large Language Models (LLMs) have shown considerable promise in various medical applications, particularly in radiology. This study aims to assess the performance of leading LLMs in analyzing unstructured chest CT reports and to examine how different questioning methodologies and fine-tuning strategies influence their effectiveness in enhancing chest CT diagnosis. This retrospective analysis evaluated 13,489 chest CT reports encompassing 13 common thoracic conditions across pulmonary, cardiovascular, pleural, and upper abdominal systems. Five LLMs (Claude-3.5-Sonnet, GPT-4, GPT-3.5-Turbo, Gemini-Pro, Qwen-Max) were assessed using dual questioning methodologies: multiple-choice and open-ended. Radiologist-curated datasets underwent rigorous preprocessing, including RadLex terminology standardization, multi-step diagnostic validation, and exclusion of ambiguous cases. Model performance was quantified via Subjective Answer Accuracy Rate (SAAR), Reference Answer Accuracy Rate (RAAR), and Area Under the Receiver Operating Characteristic (ROC) Curve analysis. GPT-3.5-Turbo underwent fine-tuning (100 iterations with one training epoch) on 200 high-performing cases to enhance diagnostic precision for initially misclassified conditions. GPT-4 demonstrated superior performance with the highest RAAR of 75.1% in multiple-choice questioning, followed by Qwen-Max (66.0%) and Claude-3.5 (63.5%), significantly outperforming GPT-3.5-Turbo (41.8%) and Gemini-Pro (40.8%) across the entire patient cohort. Multiple-choice questioning consistently improved both RAAR and SAAR for all models compared to open-ended questioning, with RAAR consistently surpassing SAAR. Model performance demonstrated notable variations across different diseases and organ conditions. Notably, fine-tuning substantially enhanced the performance of GPT-3.5-Turbo, which initially exhibited suboptimal results in most scenarios. This study demonstrated that general-purpose LLMs can effectively interpret chest CT reports, with performance varying significantly across models depending on the questioning methodology and fine-tuning approaches employed. For surgical practice, these findings provided evidence-based guidance for selecting appropriate AI tools to enhance preoperative planning, particularly for thoracic procedures. The integration of optimized LLMs into surgical workflows may improve decision-making efficiency, risk stratification, and diagnostic speed, potentially contributing to better surgical outcomes through more accurate preoperative assessment.