Purpose: The aim of this study was to investigate the relationship between non-alcoholic fatty liver disease (NAFLD), non-alcoholic fatty pancreas (or pancreatic) disease (NAFPD) and HOMA indices in obese patients without a diagnosis of diabetes mellitus, using ultrasound (US) as a common non-invasive diagnostic tool during routine examinations. Methods: In this single-centre, retrospective study, the records of patients who applied to the obesity outpatient clinic in 2023 were reviewed. Digital records were scanned and patients with abdominal ultrasound reports indicating age, gender, body mass index(BMI), fasting plasma glucose, fasting plasma glucose, C-peptide level and degree of pancreatic and hepatic steatosis were included in the study. Patients with known chronic disease or diabetes mellitus and patients with specific drug use were excluded from the study. Homa indices were calculated using fasting plasma glucose and C-peptide levels. Results: A total of 62 patients were included. Body mass index was 39.1, 91% had NAFLD and 82% had NAFPD. There was a significant positive correlation between BMI and NAFLD and NAFPD. HOMA scores revealed a statistically significant impact of NAFLD on insulin resistance (HOMA-IR) and insulin sensitivity (HOMA-S) but not on beta-cell function (HOMA-B). No significant effect of NAFPD on HOMA scores was observed. Conclusion: The findings underline the association between NAFLD and insulin resistance and highlight the metabolic burden of ectopic fat deposition in obese patients. In contrast, there was no significant correlation between NAFPD and either insulin resistance or beta-cell function, suggesting that the metabolic impact of pancreatic steatosis may be different. These findings may help to guide clinical strategies for detecting and treating metabolic disorders in obesity.

Background/Objectives: Multimodal large language models (LLMs) may directly interpret medical images, including thyroid ultrasounds (USs). Whether these models can reliably assess thyroid nodules—where subtle echogenic and morphological details are critical—remains uncertain. The American College of Radiology (ACR) TI-RADS system provides a structured framework for benchmarking artificial intelligence. This study evaluates GPT-5.0’s ability to interpret thyroid US images according to TI-RADS criteria and contextualizes its performance relative to expert radiologist assessment, using FNA cytology as the reference standard. Methods: This retrospective study included 100 patients (mean age 49.8 ± 12.6 years; 72 women) with cytology-confirmed diagnoses: Bethesda II (benign) or Bethesda V–VI (malignant). Each nodule had longitudinal and transverse US images acquired with high-frequency linear probes. A board-certified radiologist (>10 years’ experience) and GPT-5.0 independently assessed TI-RADS features (composition, echogenicity, shape, margin, echogenic foci) and assigned final categories. Agreement was analyzed using Cohen’s κ, and diagnostic performance was calculated using TR4–TR5 as positive for malignancy. Results: Agreement was substantial for composition (κ = 0.62), shape (κ = 0.70), and margin (κ = 0.68); moderate for echogenicity (κ = 0.48); and poor for echogenic foci (κ = 0.12). GPT-5.0 demonstrated a systematic, risk-averse tendency to up-classify nodules, leading to increased TR4–TR5 assignments. Overall, the TI-RADS agreement was 58% (κ = 0.31). The radiologist showed superior diagnostic performance (sensitivity 89%, specificity 85%) compared with GPT-5.0 (sensitivity 67%, specificity 49%), largely driven by false-positive TR4 classifications among benign nodules. Conclusions: GPT-5.0 recognizes several high-level TI-RADS features but struggles with microcalcifications and tends to overestimate malignancy risk within a risk-stratification framework, limiting its standalone clinical use. Ultrasound-specific training and domain adaptation may enable meaningful adjunctive roles in thyroid nodule assessment.

Aim: This study aims to evaluate the efficacy of humerus cortical thickness on coronal T1-weighted images of the humerus in distinguishing patients with normal vs. abnormal bone mineral density (BMD). Methods: Patients (n:138) with shoulder magnetic resonance imaging (MRI) and dual-energy x-ray absorptiometry (DXA) scans were evaluated. Patients were grouped into normal and low BMD (osteopenia and osteoporosis) according to DXA. An average cortical bone thickness (CBTavg) and gauge cortical bone thickness (CBTg) were calculated from the proximal humerus on coronal T1W MRI. Sensitivity and specificity for the prediction of osteoporosis were determined for several cortical bone thickness thresholds. Results: Proximal humerus average cortical bone thickness measurements strongly correlated with DXA femur and lumbar scores ( p < 0.01). Gauge cortical thickness measurements also correlated with DXA femur and lumbar scores ( p < 0.01). Average cortical bone thickness measurement of 4.52 mm was determined to be a potential marker for predicting osteoporosis, with a sensitivity of 92.3% and a specificity of 84.9%. Conclusion: Average cortical bone thickness measurements obtained from shoulder MRI are correlated with DXA. It appears to be effective in differentiating patients with normal and abnormal BMD and may help to opportunistically predict patients with osteoporosis in a rapid, simple and practical way, potentially guiding further diagnostic assessments.