Erdal, Barbaros S.

Abstract Introduction The evaluation of diastolic function in diabesity patients undergoing incretin-based therapy could facilitate early detection and help prevent progression to heart failure. Methodology A group of 15 diabesity cases with chest x-ray examinations (< 1 month) before and after ≥ 12 months of incretin-based therapy (absent ventricular/valvular dysfunction) were identified. Standard diabesity descriptors and predictions of pulmonary venous hypertension (aka “pulmonary congestion”) [None; Stage 1: vascular distention/redistribution but minimal interstitial edema; or Stage ≥ 2: vascular congestion with ≥ mild interstitial or alveolar edema] by validated AI-enabled chest x-ray staging, representing mean left atrial pressure in diastolic dysfunction, were evaluated pre- and post-therapy. Results By weight loss, cases clustered into equal-sized subgroups: 1. Significant Responders (9–30% decreases); 2. Insignificant Responders (0–2% decreases); and 3. Non-Responders (2–12% increases). Regarding hemoglobin A1c changes: 1. Significant Responders collectively decreased; 2. Insignificant Responders varied; and 3. Non-Responders were largely stable. Pre-therapy, all 15 cases demonstrated AI-enabled chest x-ray evidence of diastolic dysfunction; post-therapy, 4 improved (especially cases of greatest weight loss or hemoglobin A1c reduction), 5 were stable, and 6 worsened. Per subgroup, AI-enabled chest x-ray indications therapeutic response were: 1. Significant Responders (all demonstrating unequivocally decreased obesity and improved diabetes) collectively showed stable-decreased dysfunction; 2. Insignificant Responders (all demonstrating minimally decreased obesity but stable-worsening diabetes in most) reflected stable-increased dysfunction in 60%; and 3. Non-Responders (all demonstrating increased obesity but stable-improved diabetes) showed increased dysfunction in 80%. Conclusion AI-enabled CXR staging detects background subclinical diastolic dysfunction in diabesity and monitors its response to incretin-based therapy.

Abstract Radiological protocol selection is a critical but time-consuming step in clinical workflow, requiring radiologists to match patient indications with the appropriate MRI or CT protocol. Manual selection can be prone to delays or potential errors, and automated approaches must contend with substantial data imbalance, site-specific variation, and evolving nomenclature. We investigated whether a large language model (LLM) can support reliable protocol selection at scale and whether retrieval-augmented generation (RAG) offers operational advantages over direct fine-tuning. Using 498,228 patient reports collected across three Mayo Clinic sites (Arizona, Florida, and Rochester) spanning six radiological divisions, we trained site-specific Llama 3.2 3B models for use with and without retrieval augmentation. Division-scoped Facebook AI Similarity Search (FAISS) indexes constructed from procedure and diagnosis text were used to supply contextual evidence in the RAG framework. Both fine-tuned and RAG-augmented models achieved strong performance across sites, with F1 scores of 0.88–0.90. RAG matched or modestly trailed direct fine-tuning overall but delivered consistent gains in specific divisions (e.g., musculoskeletal imaging). Importantly, the RAG model introduced abstention behavior (Not Enough Information), which concentrated in linguistically diverse divisions and provided an interpretable signal of uncertainty. These findings suggest that RAG-based models are viable for division-scoped protocol selection and offer practical advantages. Retrieval indexes can be refreshed far more easily and with fewer resources than retraining LLMs, enabling continual adaptation to evolving clinical workflows. Future prospective deployment will evaluate real-time accuracy, agreement with practitioners, and the role of abstention as a safety mechanism in clinical decision support.

Objective: Pulmonary air trapping is critical for diagnosing and prognostication of various lung diseases. Expiratory CT imaging serves as an accessible method to assess air trapping, which correlates with small airway disease outcomes. Air trapping manifests as mosaic attenuation on inspiratory chest CT that is difficult for visual estimation. The primary aim of this study was to develop an automated tool to quantify mosaic attenuation on inspiratory CT and air trapping on paired expiratory CT. Secondary aims included comparing CT-derived parameters with PFT measurements and dyspnea scores. Methods: This retrospective analysis of noncontrast chest CTs from 2 academic hospitals was conducted between January 1, 2018, and December 31, 2019. Patients with paired inspiratory and expiratory CT chest scans and PFTs performed on the same day were included. A chest radiologist manually annotated lung parenchyma in a reference cohort. Several histogram-based metrics were computed from lung parenchymal CT values, with the maximum peak position showing the strongest correlation with manually determined thresholds. This threshold, derived from the histogram peak, was applied in the adaptive thresholding process to quantify mosaic attenuation and air trapping. Results: We analyzed 267 patients (65.5% female, median age 68). Most exhibited normal physiological patterns (44.0%). Patients with elevated residual volume (RV) by PFTs (28.1%) had significantly higher inspiratory CT mosaic attenuation (1629.6 vs. 1311.5 mL, P <0.01) and expiratory CT air trapping volumes (1413.7 vs. 886.2 mL, P <0.01). Correlation analyses demonstrated strong relationships between CT-derived mosaic attenuation and air trapping measures and RV. The correlation with PFT parameters was even stronger in subgroup analyses in patients with obstructive PFT patterns. These models had good predictive ability for an abnormal RV (AUC of 0.92, sensitivity of 72.4%, and specificity of 92.0%) and clinical utility based on good correlation with the mMRC dyspnea score ( r =0.71; 95% CI: 0.65-0.77). Conclusions: This automated adaptive thresholding on inspiratory and expiratory chest CT scans showed a high correlation of lung volume and air trapping parameters with PFTs, revealing that measures of lung function have a complex interplay with air trapping.