Medical disease prediction, particularly through imaging, remains a challenging task due to the complexity and variability of medical data, including noise, ambiguity, and differing image quality. Recent deep learning models, including Knowledge Distillation (KD) methods, have shown promising results in brain tumor image identification but still face limitations in handling uncertainty and generalizing across diverse medical conditions. Traditional KD methods often rely on a context-unaware temperature parameter to soften teacher model predictions, which does not adapt effectively to varying uncertainty levels present in medical images. To address this issue, we propose a novel framework that integrates Ant Colony Optimization (ACO) for optimal teacher-student model selection and a novel context-aware predictor approach for temperature scaling. The proposed context-aware framework adjusts the temperature based on factors such as image quality, disease complexity, and teacher model confidence, allowing for more robust knowledge transfer. Additionally, ACO efficiently selects the most appropriate teacher-student model pair from a set of pre-trained models, outperforming current optimization methods by exploring a broader solution space and better handling complex, non-linear relationships within the data. The proposed framework is evaluated using three publicly available benchmark datasets, each corresponding to a distinct medical imaging task. The results demonstrate that the proposed framework significantly outperforms current state-of-the-art methods, achieving top accuracy rates: 98.01% on the MRI brain tumor (Kaggle) dataset, 92.81% on the Figshare MRI dataset, and 96.20% on the GastroNet dataset. This enhanced performance is further evidenced by the improved results, surpassing existing benchmarks of 97.24% (Kaggle), 91.43% (Figshare), and 95.00% (GastroNet).

Lung cancer is the leading cause of patient mortality in the world. Early diagnosis of malignant pulmonary nodules in CT images can have a significant impact on reducing disease mortality and morbidity. In this work, we propose LMLCC-Net, a novel deep learning framework for classifying nodules from CT scan images using a 3D CNN, considering Hounsfield Unit (HU)-based intensity filtering. Benign and malignant nodules have significant differences in their intensity profile of HU, which was not exploited in the literature. Our method considers the intensity pattern as well as the texture for the prediction of malignancies. LMLCC-Net extracts features from multiple branches that each use a separate learnable HU-based intensity filtering stage. Various combinations of branches and learnable ranges of filters were explored to finally produce the best-performing model. In addition, we propose a semi-supervised learning scheme for labeling ambiguous cases and also developed a lightweight model to classify the nodules. The experimental evaluations are carried out on the LUNA16 dataset. Our proposed method achieves a classification accuracy (ACC) of 91.96%, a sensitivity (SEN) of 92.04%, and an area under the curve (AUC) of 91.87%, showing improved performance compared to existing methods. The proposed method can have a significant impact in helping radiologists in the classification of pulmonary nodules and improving patient care.

Due to population aging, the increasing prevalence of Alzheimer's Disease (AD) and related dementias are major public health concerns. Dietary consumption of antioxidant nutrients, in particular the carotenoid β-carotene, has been associated with lower age-related neurocognitive decline. What is unclear, however, is the extent to which antioxidant nutrients may exert neuroprotective effects via their influence on established indicators of age-related changes in brain tissue. This study thus tested associations of circulating β-carotene and other nutrients with a structural neuroimaging indicator of brain age derived from cross-validated machine learning models trained to predict chronological age from brain tissue morphology in independent cohorts. Midlife adults (N=132, aged 30.4 to 50.8 years, 59 female at birth) underwent a structural magnetic resonance imaging (MRI) protocol and fasting phlebotomy to assess plasma concentrations of β-carotene, retinol, γ-tocopherol, ⍺-tocopherol, and β-cryptoxanthin. In regression analyses adjusting for chronological age, sex at birth, smoking status, MRI image quality, season of testing, annual income, and education, greater circulating levels of β-carotene were associated with a lower (i.e., younger) predicted brain age (β=-0.23, 95% CI=-0.40 to -0.07, P=0.006). Other nutrients were not statistically associated with brain age, and results persisted after additional covariate control for body mass index, cortical volume, and cortical thickness. These cross-sectional findings are consistent with the possibility that dietary intake of β-carotene may be associated with slower biological aging at the level of the brain, as reflected by a neuroimaging indicator of brain age.

Accurate estimation of cerebral amyloid-{beta} (A{beta}) burden is critical for early detection and risk stratification in Alzheimers disease (AD). While A{beta} positron emission tomography (PET) remains the gold standard, its high cost, invasive nature and limited accessibility hinder broad clinical application. Blood-based biomarkers offer a non-invasive and cost-effective alternative, but their standalone predictive accuracy remains limited due to biological heterogeneity and limited reflection of central nervous system pathology. Here, we present a high-precision, multimodal prediction machine learning model that integrates plasma biomarkers, brain structural magnetic resonance imaging (sMRI) features, diffusion tensor imaging (DTI)-derived structural connectomes, and genetic risk profiles. The model was trained on 150 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI) and externally validated on 111 participants from the SILCODE cohort. Multimodal integration substantially improved A{beta} prediction, with R{superscript 2} increasing from 0.515 using plasma biomarkers alone to 0.637 when adding imaging and genetic features. These results highlight the potential of this multimodal machine learning approach as a scalable, non-invasive, and economically viable alternative to PET for estimating A{beta} burden.

Background and ObjectivesBottom-of-sulcus dysplasia (BOSD) is a diagnostically challenging subtype of focal cortical dysplasia, 60% being missed on patients first MRI. Automated MRI-based detection methods have been developed for focal cortical dysplasia, but not BOSD specifically. Use of FDG-PET alongside MRI is not established in automated methods. We report the development and performance of an automated BOSD detector using combined MRI+PET data. MethodsThe training set comprised 54 mostly operated patients with BOSD. The test sets comprised 17 subsequently diagnosed patients with BOSD from the same center, and 12 published patients from a different center. 81% patients across training and test sets had reportedly normal first MRIs and most BOSDs were <1.5cm3. In the training set, 12 features from T1-MRI, FLAIR-MRI and FDG-PET were evaluated using a novel "pseudo-control" normalization approach to determine which features best distinguished dysplastic from normal-appearing cortex. Using the Multi-centre Epilepsy Lesion Detection groups machine-learning detection method with the addition of FDG-PET, neural network classifiers were then trained and tested on MRI+PET features, MRI-only and PET-only. The proportion of patients whose BOSD was overlapped by the top output cluster, and the top five output clusters, were assessed. ResultsCortical and subcortical hypometabolism on FDG-PET were superior in discriminating dysplastic from normal-appearing cortex compared to MRI features. When the BOSD detector was trained on MRI+PET features, 87% BOSDs were overlapped by one of the top five clusters (69% top cluster) in the training set, 76% in the prospective test set (71% top cluster) and 75% in the published test set (42% top cluster). Cluster overlap was similar when the detector was trained and tested on PET-only features but lower when trained and tested on MRI-only features. ConclusionDetection of BOSD is possible using established MRI-based automated detection methods, supplemented with FDG-PET features and trained on a BOSD-specific cohort. In clinical practice, an MRI+PET BOSD detector could improve assessment and outcomes in seemingly MRI-negative patients being considered for epilepsy surgery.

Online adaptive magnetic resonance-guided radiotherapy (MRgRT) has emerged as a state-of-the-art treatment option for multiple tumour entities, accounting for daily anatomical and tumour volume changes, thus allowing sparing of relevant organs at risk (OARs). However, the annotation of treatment-relevant anatomical structures in context of online plan adaptation remains challenging, often relying on commercial segmentation solutions due to limited availability of clinically validated alternatives. The aim of this study was to investigate whether an open-source artificial intelligence (AI) segmentation network can compete with the annotation accuracy of a commercial solution, both trained on the identical dataset, questioning the need for commercial models in clinical practice. For 47 pelvic patients, T2w MR imaging data acquired on a 1.5 T MR-Linac were manually contoured, identifying prostate, seminal vesicles, rectum, anal canal, bladder, penile bulb, and bony structures. These training data were used for the generation of an in-house AI segmentation model, a nnU-Net with residual encoder architecture featuring a streamlined single image inference pipeline, and re-training of a commercial solution. For quantitative evaluation, 20 MR images were contoured by a radiation oncologist, considered as ground truth contours (GTC) and compared with the in-house/commercial AI-based contours (iAIC/cAIC) using Dice Similarity Coefficient (DSC), 95% Hausdorff distances (HD95), and surface DSC (sDSC). For qualitative evaluation, four radiation oncologists assessed the usability of OAR/target iAIC within an online adaptive workflow using a four-point Likert scale: (1) acceptable without modification, (2) requiring minor adjustments, (3) requiring major adjustments, and (4) not usable. Patient-individual annotations were generated in a median [range] time of 23 [16-34] s for iAIC and 152 [121-198] s for cAIC, respectively. OARs showed a maximum median DSC of 0.97/0.97 (iAIC/cAIC) for bladder and minimum median DSC of 0.78/0.79 (iAIC/cAIC) for anal canal/penile bulb. Maximal respectively minimal median HD95 were detected for rectum with 17.3/20.6 mm (iAIC/cAIC) and for bladder with 5.6/6.0 mm (iAIC/cAIC). Overall, the average median DSC/HD95 values were 0.87/11.8mm (iAIC) and 0.83/10.2mm (cAIC) for OAR/targets and 0.90/11.9mm (iAIC) and 0.91/16.5mm (cAIC) for bony structures. For a tolerance of 3 mm, the highest and lowest sDSC were determined for bladder (iAIC:1.00, cAIC:0.99) and prostate in iAIC (0.89) and anal canal in cAIC (0.80), respectively. Qualitatively, 84.8% of analysed contours were considered as clinically acceptable for iAIC, while 12.9% required minor and 2.3% major adjustments or were classed as unusable. Contour-specific analysis showed that iAIC achieved the highest mean scores with 1.00 for the anal canal and the lowest with 1.61 for the prostate. This study demonstrates that open-source segmentation framework can achieve comparable annotation accuracy to commercial solutions for pelvic anatomy in online adaptive MRgRT. The adapted framework not only maintained high segmentation performance, with 84.8% of contours accepted by physicians or requiring only minor corrections (12.9%) but also enhanced clinical workflow efficiency of online adaptive MRgRT through reduced inference times. These findings establish open-source frameworks as viable alternatives to commercial systems in supervised clinical workflows.

Osteosarcoma (OS) is the most common primary bone malignancy, and neoadjuvant chemotherapy (NAC) improves survival rates. However, OS heterogeneity results in variable treatment responses, highlighting the need for reliable, non-invasive tools to predict NAC response. Radiomics-based machine learning (ML) offers potential for identifying imaging biomarkers to predict treatment outcomes. This systematic review and meta-analysis evaluated the accuracy and reliability of radiomics models for predicting NAC response in OS. A systematic search was conducted in PubMed, Embase, Scopus, and Web of Science up to November 2024. Studies using radiomics-based ML for NAC response prediction in OS were included. Pooled sensitivity, specificity, and AUC for training and validation cohorts were calculated using bivariate random-effects modeling, with clinical-combined models analyzed separately. Quality assessment was performed using the QUADAS-2 tool, radiomics quality score (RQS), and METRICS scores. Sixteen studies were included, with 63 % using MRI and 37 % using CT. Twelve studies, comprising 1639 participants, were included in the meta-analysis. Pooled metrics for training cohorts showed an AUC of 0.93, sensitivity of 0.89, and specificity of 0.85. Validation cohorts achieved an AUC of 0.87, sensitivity of 0.81, and specificity of 0.82. Clinical-combined models outperformed radiomics-only models. The mean RQS score was 9.44 ± 3.41, and the mean METRICS score was 60.8 % ± 17.4 %. Radiomics-based ML shows promise for predicting NAC response in OS, especially when combined with clinical indicators. However, limitations in external validation and methodological consistency must be addressed.

Pneumonia is the leading cause of hospitalisation and mortality among children under five, particularly in low-resource settings. Accurate differentiation between viral and bacterial pneumonia is essential for guiding appropriate treatment, yet it remains challenging due to overlapping clinical and radiographic features. Advances in machine learning (ML), particularly deep learning (DL), have shown promise in classifying pneumonia using chest X-ray (CXR) images. This scoping review summarises the evidence on ML techniques for classifying viral and bacterial pneumonia using CXR images in paediatric patients. This scoping review was conducted following the Joanna Briggs Institute methodology and the PRISMA-ScR guidelines. A comprehensive search was performed in PubMed, Embase, and Scopus to identify studies involving children (0-18 years) with pneumonia diagnosed through CXR, using ML models for binary or multiclass classification. Data extraction included ML models, dataset characteristics, and performance metrics. A total of 35 studies, published between 2018 and 2025, were included in this review. Of these, 31 studies used the publicly available Kermany dataset, raising concerns about overfitting and limited generalisability to broader, real-world clinical populations. Most studies (n=33) used convolutional neural networks (CNNs) for pneumonia classification. While many models demonstrated promising performance, significant variability was observed due to differences in methodologies, dataset sizes, and validation strategies, complicating direct comparisons. For binary classification (viral vs bacterial pneumonia), a median accuracy of 92.3% (range: 80.8% to 97.9%) was reported. For multiclass classification (healthy, viral pneumonia, and bacterial pneumonia), the median accuracy was 91.8% (range: 76.8% to 99.7%). Current evidence is constrained by a predominant reliance on a single dataset and variability in methodologies, which limit the generalisability and clinical applicability of findings. To address these limitations, future research should focus on developing diverse and representative datasets while adhering to standardised reporting guidelines. Such efforts are essential to improve the reliability, reproducibility, and translational potential of machine learning models in clinical settings.

Accurate preoperative assessment of axillary lymph node metastasis (ALNM) in breast cancer is crucial for guiding treatment decisions. This study aimed to develop a deep-learning radiomics model for assessing ALNM and to evaluate its impact on radiologists' diagnostic accuracy. This multicenter study included 866 breast cancer patients from 6 hospitals. The data were categorized into training, internal test, external test, and prospective test sets. Deep learning and handcrafted radiomics features were extracted from ultrasound images of primary tumors and lymph nodes. The tumor score and LN score were calculated following feature selection, and a clinical-radiomics model was constructed based on these scores along with clinical-ultrasonic risk factors. The model's performance was validated across the 3 test sets. Additionally, the diagnostic performance of radiologists, with and without model assistance, was evaluated. The clinical-radiomics model demonstrated robust discrimination with AUCs of 0.94, 0.92, 0.91, and 0.95 in the training, internal test, external test, and prospective test sets, respectively. It surpassed the clinical model and single score in all sets (P < .05). Decision curve analysis and clinical impact curves validated the clinical utility of the clinical-radiomics model. Moreover, the model significantly improved radiologists' diagnostic accuracy, with AUCs increasing from 0.71 to 0.82 for the junior radiologist and from 0.75 to 0.85 for the senior radiologist. The clinical-radiomics model effectively predicts ALNM in breast cancer patients using noninvasive ultrasound features. Additionally, it enhances radiologists' diagnostic accuracy, potentially optimizing resource allocation in breast cancer management.

To perform a systematic review on artificial intelligence (AI) studies focused on identifying and differentiating pelvic gynecological tumors on ultrasound scans. Studies developing or validating AI models for diagnosing gynecological pelvic tumors on ultrasound scans were eligible for inclusion. We systematically searched PubMed, Embase, Web of Science, and Cochrane Central from their database inception until April 30th, 2024. To assess the quality of the included studies, we adapted the QUADAS-2 risk of bias tool to address the unique challenges of AI in medical imaging. Using multi-level random effects models, we performed a meta-analysis to generate summary estimates of the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. To provide a reference point of current diagnostic support tools for ultrasound examiners, we descriptively compared the pooled performance to that of the well-recognized ADNEX model on external validation. Subgroup analyses were performed to explore sources of heterogeneity. From 9151 records retrieved, 44 studies were eligible: 40 on ovarian, three on endometrial, and one on myometrial pathology. Overall, 95% were at high risk of bias - primarily due to inappropriate study inclusion criteria, the absence of a patient-level split of training and testing image sets, and no calibration assessment. For ovarian tumors, the summary AUC for AI models distinguishing benign from malignant tumors was 0.89 (95% CI: 0.85-0.92). In lower-risk studies (at least three low-risk domains), the summary AUC dropped to 0.87 (0.83-0.90), with deep learning models outperforming radiomics-based machine learning approaches in this subset. Only five studies included an external validation, and six evaluated calibration performance. In a recent systematic review of external validation studies, the ADNEX model had a pooled AUC of 0.93 (0.91-0.94) in studies at low risk of bias. Studies on endometrial and myometrial pathologies were reported individually. Although AI models show promising discriminative performances for diagnosing gynecological tumors on ultrasound, most studies have methodological shortcomings that result in a high risk of bias. In addition, the ADNEX model appears to outperform most AI approaches for ovarian tumors. Future research should emphasize robust study designs - ideally large, multicenter, and prospective cohorts that mirror real-world populations - along with external validation, proper calibration, and standardized reporting. This study was pre-registered with Open Science Framework (OSF): https://doi.org/10.17605/osf.io/bhkst.