Availability of ultraportable chest x-ray (CXR) and advancements in artificial intelligence (AI)-enabled CXR interpretation are promising developments in tuberculosis (TB) active case finding (ACF) but costing and cost-effectiveness analyses are limited. We provide implementation cost and cost-effectiveness estimates of different screening algorithms using symptoms, CXR and AI in Nigeria. People 15 years and older were screened for TB symptoms and offered a CXR with AI-enabled interpretation using qXR v3 (Qure.ai) at lung health camps. Sputum samples were tested on Xpert MTB/RIF for individuals reporting symptoms or with qXR abnormality scores ≥0.30. We conducted a retrospective costing using a combination of top-down and bottom-up approaches while utilizing itemized expense data from a health system perspective. We estimated costs in five screening scenarios: abnormality score ≥0.30 and ≥0.50; cough ≥ 2 weeks; any symptom; abnormality score ≥0.30 or any symptom. We calculated total implementation costs, cost per bacteriologically-confirmed case detected, and assessed cost-effectiveness using incremental cost-effectiveness ratio (ICER) as additional cost per additional case. Overall, 3205 people with presumptive TB were identified, 1021 were tested, and 85 people with bacteriologically-confirmed TB were detected. Abnormality ≥ 0.30 or any symptom (US$65704) had the highest costs while cough ≥ 2 weeks was the lowest (US$40740). The cost per case was US$1198 for cough ≥ 2 weeks, and lowest for any symptom (US$635). Compared to baseline strategy of cough ≥ 2 weeks, the ICER for any symptom was US$191 per additional case detected and US$ 2096 for Abnormality ≥0.30 OR any symptom algorithm. Using CXR and AI had lower cost per case detected than any symptom screening criteria when asymptomatic TB was higher than 30% of all bacteriologically-confirmed TB detected. Compared to traditional symptom screening, using CXR and AI in combination with symptoms detects more cases at lower cost per case detected and is cost-effective. TB programs should explore adoption of CXR and AI for screening in ACF.

Osteoporosis, a prevalent skeletal disorder, necessitates accurate and accessible diagnostic tools for effective disease management and fracture prevention. While dual-energy X-ray absorptiometry (DXA) remains the clinical standard for bone mineral density (BMD) assessment, its limitations, including ionizing radiation exposure and susceptibility to artifacts, underscore the need for alternative technologies. Ultrasound-based methods have emerged as promising radiation-free alternatives, with radiofrequency echographic multi-spectrometry (REMS) representing a significant advancement in axial skeleton assessment, specifically at the lumbar spine and proximal femur. REMS analyzes unfiltered radiofrequency ultrasound signals, providing not only BMD estimates but also a novel fragility score (FS), which reflects bone quality and microarchitectural integrity. This review critically evaluates the underlying principles, diagnostic performance, and clinical applications of REMS. It compares REMS with DXA, quantitative computed tomography (QCT), and trabecular bone score (TBS), highlighting REMS's potential advantages in artifact-prone scenarios and specific populations, including children and patients with secondary osteoporosis. The clinical utility of REMS in fracture risk prediction and therapy monitoring is explored alongside its operational precision, cost-effectiveness, and portability. In addition, the integration of artificial intelligence (AI) within REMS software has enhanced its capacity for artifact exclusion and automated spectral interpretation, improving usability and reproducibility. Current limitations, such as the need for broader validation and guideline inclusion, are identified, and future research directions are proposed. These include multicenter validation studies, development of pediatric and secondary osteoporosis reference models, and deeper evaluation of AI-driven enhancements. REMS offers a compelling, non-ionizing alternative for axial bone health assessment and may significantly advance the diagnostic landscape for osteoporosis care.

Hip fractures are a major orthopedic problem, especially in the elderly population. Hip fractures are usually diagnosed by clinical evaluation and imaging, especially X-rays. In recent years, new approaches to fracture detection have emerged with the use of artificial intelligence (AI) and deep learning techniques in medical imaging. In this study, we aimed to evaluate the diagnostic performance of ChatGPT-4o, an artificial intelligence model, in diagnosing hip fractures. A total of 200 anteroposterior pelvic X-ray images were retrospectively analyzed. Half of the images belonged to patients with surgically confirmed hip fractures, including both displaced and non-displaced types, while the other half represented patients with soft tissue trauma and no fractures. Each image was evaluated by ChatGPT-4o through a standardized prompt, and its predictions (fracture vs. no fracture) were compared against the gold standard diagnoses. Diagnostic performance metrics such as sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), receiver operating characteristic (ROC) curve, Cohen's kappa, and F1 score were calculated. ChatGPT-4o demonstrated an overall accuracy of 82.5% in detecting hip fractures on pelvic radiographs, with a sensitivity of 78.0% and specificity of 87.0%. PPVs and NPVs were 85.7% and 79.8%, respectively. The area under the ROC curve (AUC) was 0.825, indicating good discriminative performance. Among 22 false-negative cases, 68.2% were non-displaced fractures, suggesting the model had greater difficulty identifying subtle radiographic findings. Cohen's kappa coefficient was 0.65, showing substantial agreement with actual diagnoses. Chi-square analysis revealed a strong correlation (χ² = 82.59, <i>P</i> < 0.001), while McNemar's test (<i>P</i> = 0.176) showed no significant asymmetry in error distribution. ChatGPT-4o shows promising accuracy in identifying hip fractures on pelvic X-rays, especially when fractures are displaced. However, its sensitivity drops significantly for non-displaced fractures, leading to many false negatives. This highlights the need for caution when interpreting negative AI results, particularly when clinical suspicion remains high. While not a replacement for expert assessment, ChatGPT-4o may assist in settings with limited specialist access.

Two of the most common complaints seen in neurology clinics are Alzheimer's disease (AD) and mild cognitive impairment (MCI), characterized by similar symptoms. The aim of this study was to develop and internally validate the diagnostic value of combined neurological and radiological predictors in differentiating mild AD from MCI as the outcome variable, which helps in preventing AD development. A cross-sectional study of 161 participants was conducted in a general healthcare setting, including 30 controls, 71 mild AD, and 60 MCI. Binary logistic regression was used to identify predictors of interest, with collinearity assessment conducted prior to model development. Model performance was assessed through calibration, shrinkage, and decision-curve analyses. Finally, the combined clinical and radiological model was compared to models utilizing only clinical or radiological predictors. The final model included age, sex, education status, Montreal cognitive assessment, Global Cerebral Atrophy Index, Medial Temporal Atrophy Scale, mean hippocampal volume, and Posterior Parietal Atrophy Index, with the area under the curve of 0.978 (0.934-0.996). Internal validation methods did not show substantial reduction in diagnostic performance. Combined model showed higher diagnostic performance compared to clinical and radiological models alone. Decision curve analysis highlighted the usefulness of this model for differentiation across all probability levels. A combined clinical-radiological model has excellent diagnostic performance in differentiating mild AD from MCI. Notably, the model leveraged straightforward neuroimaging markers, which are relatively simple to measure and interpret, suggesting that they could be integrated into practical, formula-driven diagnostic workflows without requiring computationally intensive deep learning models.

The aim of this investigation is to assess the clinical usefulness of a machine learning model using contrast-enhanced ultrasound (CEUS) radiomics in discriminating clear cell renal cell carcinoma (ccRCC) from non-ccRCC. A total of 292 patients with pathologically confirmed RCC subtypes underwent CEUS (development set. n = 231; validation set, n = 61) in a retrospective study. Radiomics features were derived from CEUS images acquired during the cortical and parenchymal phases. Radiomics models were developed using logistic regression (LR), support vector machine, decision tree, naive Bayes, gradient boosting machine, and random forest. The suitable model was identified based on the area under the receiver operating characteristic curve (AUC). Appropriate clinical CEUS features were identified through univariate and multivariate LR analyses to develop a clinical model. By integrating radiomics and clinical CEUS features, a combined model was established. A comprehensive evaluation of the models' performance was conducted. After the reduction and selection process were applied to 2250 radiomics features, the final set of 8 features was considered valuable. Among the models, the LR model had the highest performance on the validation set and showed good robustness. In both the development and validation sets, both the radiomics (AUC, 0.946 and 0.927) and the combined models (AUC, 0.949 and 0.925) outperformed the clinical model (AUC, 0.851 and 0.768), showing higher AUC values (all p < 0.05). The combined model exhibited favorable calibration and clinical benefit. The combined model integrating clinical CEUS and CEUS radiomics features demonstrated good diagnostic performance in discriminating ccRCC from non-ccRCC.

Magnetic resonance imaging (MRI), combined with artificial intelligence techniques, has improved our understanding of brain structural change and enabled the estimation of brain age. Neurodegenerative disorders, such as Alzheimer's disease (AD), have been linked to accelerated brain aging. In this study, we aimed to develop a deep-learning framework that processes and integrates MRI images to more accurately predict brain age, cognitive function, and amyloid pathology. In this study, we aimed to develop a deep-learning framework that processes and integrates MRI images to more accurately predict brain age, cognitive function, and amyloid pathology.We collected over 10,000 T1-weighted MRI scans from more than 7,000 individuals across six cohorts. We designed a multi-modal deep-learning framework that employs 3D convolutional neural networks to analyze MRI and additional neural networks to evaluate demographic data. Our initial model focused on predicting brain age, serving as a foundational model from which we developed separate models for cognition function and amyloid plaque prediction through transfer learning. The brain age prediction model achieved the mean absolute error (MAE) for cognitive normal population in the ADNI (test) datasets of 3.302 years. The gap between predicted brain age and chronological age significantly increases while cognition declines. The cognition prediction model exhibited a root mean square error (RMSE) of 0.334 for the Clinical Dementia Rating (CDR) regression task, achieving an area under the curve (AUC) of approximately 0.95 in identifying ing dementia patients. Dementia related brain regions, such as the medial temporal lobe, were identified by our model. Finally, amyloid plaque prediction model was trained to predict amyloid plaque, and achieved an AUC about 0.8 for dementia patients. These findings indicate that the present predictive models can identify subtle changes in brain structure, enabling precise estimates of brain age, cognitive status, and amyloid pathology. Such models could facilitate the use of MRI as a non-invasive diagnostic tool for neurodegenerative diseases, including AD.

Accurate preoperative prediction of major pathological response or pathological complete response after neoadjuvant chemo-immunotherapy remains a critical unmet need in resectable non-small-cell lung cancer (NSCLC). Conventional size-based imaging criteria offer limited reliability, while biopsy confirmation is available only post-surgery. We retrospectively assembled 509 consecutive NSCLC cases from four Chinese thoracic-oncology centers (March 2018 to March 2023) and prospectively enrolled 50 additional patients. Three 3-dimensional convolutional neural networks (pre-treatment CT, pre-surgical CT, dual-phase CT) were developed; the best-performing dual-phase model (NeoPred) optionally integrated clinical variables. Model performance was measured by area under the receiver-operating-characteristic curve (AUC) and compared with nine board-certified radiologists. In an external validation set (n=59), NeoPred achieved an AUC of 0.772 (95% CI: 0.650 to 0.895), sensitivity 0.591, specificity 0.733, and accuracy 0.627; incorporating clinical data increased the AUC to 0.787. In a prospective cohort (n=50), NeoPred reached an AUC of 0.760 (95% CI: 0.628 to 0.891), surpassing the experts' mean AUC of 0.720 (95% CI: 0.574 to 0.865). Model assistance raised the pooled expert AUC to 0.829 (95% CI: 0.707 to 0.951) and accuracy to 0.820. Marked performance persisted within radiological stable-disease subgroups (external AUC 0.742, 95% CI: 0.468 to 1.000; prospective AUC 0.833, 95% CI: 0.497 to 1.000). Combining dual-phase CT and clinical variables, NeoPred reliably and non-invasively predicts pathological response to neoadjuvant chemo-immunotherapy in NSCLC, outperforms unaided expert assessment, and significantly enhances radiologist performance. Further multinational trials are needed to confirm generalizability and support surgical decision-making.

To develop and validate an automated diabetic macular oedema (DME) classification system based on the images from different three-dimensional optical coherence tomography (3-D OCT) devices. A multicentre, platform-based development study using retrospective and cross-sectional data. Data were subjected to a two-level grading system by trained graders and a retina specialist, and categorised into three types: no DME, non-centre-involved DME and centre-involved DME (CI-DME). The 3-D convolutional neural networks algorithm was used for DME classification system development. The deep learning (DL) performance was compared with the diabetic retinopathy experts. Data were collected from Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Chaozhou People's Hospital and The Second Affiliated Hospital of Shantou University Medical College from January 2010 to December 2023. 7790 volumes of 7146 eyes from 4254 patients were annotated, of which 6281 images were used as the development set and 1509 images were used as the external validation set, split based on the centres. Accuracy, F1-score, sensitivity, specificity, area under receiver operating characteristic curve (AUROC) and Cohen's kappa were calculated to evaluate the performance of the DL algorithm. In classifying DME with non-DME, our model achieved an AUROCs of 0.990 (95% CI 0.983 to 0.996) and 0.916 (95% CI 0.902 to 0.930) for hold-out testing dataset and external validation dataset, respectively. To distinguish CI-DME from non-centre-involved-DME, our model achieved AUROCs of 0.859 (95% CI 0.812 to 0.906) and 0.881 (95% CI 0.859 to 0.902), respectively. In addition, our system showed comparable performance (Cohen's κ: 0.85 and 0.75) to the retina experts (Cohen's κ: 0.58-0.92 and 0.70-0.71). Our DL system achieved high accuracy in multiclassification tasks on DME classification with 3-D OCT images, which can be applied to population-based DME screening.

To study the feasibility of multiple factors in improving the diagnostic accuracy of clinically significant prostate cancer (csPCa). A retrospective study with 131 patients analyzes age, PSA, PHI and pathology. Patients with ISUP > 2 were classified as csPCa, and others are non-csPCa. The mpMRI images were processed by a homemade AI algorithm, obtaining positive or negative AI results. Four logistic regression models were fitted, with pathological findings as the dependent variable. The predicted probability of the patients was used to test the prediction efficacy of the models. The DeLong test was performed to compare differences in the area under the receiver operating characteristic (ROC) curves (AUCs) between the models. The study includes 131 patients: 62 were diagnosed with csPCa and 69 were non-csPCa. Statically significant differences were found in age, PSA, PIRADS score, AI results, and PHI values between the 2 groups (all P ≤ 0.001). The conventional model (R² = 0.389), the AI model (R² = 0.566), and the PHI model (R² = 0.515) were compared to the full model (R² = 0.626) with ANOVA and showed statistically significant differences (all P < 0.05). The AUC of the full model (0.921 [95% CI: 0.871-0.972]) was significantly higher than that of the conventional model (P = 0.001), AI model (P < 0.001), and PHI model (P = 0.014). Combining multiple factors such as age, PSA, PIRADS score and PHI, adding AI algorithm based on mpMRI, the diagnostic accuracy of csPCa can be improved.

To evaluate the value of combining American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) with the Demetics ultrasound diagnostic system in reducing the rate of fine-needle aspiration (FNA) biopsies for thyroid nodules. A retrospective study analyzed 548 thyroid nodules from 454 patients, all meeting ACR TI-RADS guidelines (category ≥3 and diameter ≥10 mm) for FNA. Nodule was reclassified using the combined ACR TI-RADS and Demetics system (De TI-RADS), and the biopsy rates were compared. Using ACR TI-RADS alone, the biopsy rate was 70.6% (387/548), with a positive predictive value (PPV) of 52.5% (203/387), an unnecessary biopsy rate of 47.5% (184/387) and a missed diagnosis rate of 11.0% (25/228). Incorporating Demetics reduced the biopsy rate to 48.1% (264/548), the unnecessary biopsy rate to 17.4% (46/265) and the missed diagnosis rate to 4.4% (10/228), while increasing PPV to 82.6% (218/264). All differences between ACR TI-RADS and De TI-RADS were statistically significant (p < 0.05). The integration of ACR TI-RADS with the Demetics system improves nodule risk assessment by enhancing diagnostic and efficiency. This approach reduces unnecessary biopsies and missed diagnoses while increasing PPV, offering a more reliable tool for clinicians and patients.