Stress echocardiography (SE) is one of the most commonly used diagnostic imaging tests for coronary artery disease (CAD) but requires clinicians to visually assess scans to identify patients who may benefit from invasive investigation and treatment. EchoGo Pro provides an automated interpretation of SE based on artificial intelligence (AI) image analysis. In reader studies, use of EchoGo Pro when making clinical decisions improves diagnostic accuracy and confidence. Prospective evaluation in real world practice is now important to understand the impact of EchoGo Pro on the patient pathway and outcome. PROTEUS is a randomized, multicenter, 2-armed, noninferiority study aiming to recruit 2,500 participants from National Health Service (NHS) hospitals in the UK referred to SE clinics for investigation of suspected CAD. All participants will undergo a stress echocardiogram protocol as per local hospital policy. Participants will be randomized 1:1 to a control group, representing current practice, or an intervention group, in which clinicians will receive an AI image analysis report (EchoGo Pro, Ultromics Ltd, Oxford, UK) to use during image interpretation, indicating the likelihood of severe CAD. The primary outcome will be appropriateness of clinician decision to refer for coronary angiography. Secondary outcomes will assess other health impacts including appropriate use of other clinical management approaches, impact on variability in decision making, patient and clinician qualitative experience and a health economic analysis. This will be the first study to assess the impact of introducing an AI medical diagnostic aid into the standard care pathway of patients with suspected CAD being investigated with SE. Clinicaltrials.gov registration number NCT05028179, registered on 31 August 2021; ISRCTN: ISRCTN15113915; IRAS ref: 293515; REC ref: 21/NW/0199.

Proposed roadmap of AI adoption into clinical practice. FDA, U.S. Food and Drug Administration; MDR, Medical Device Regulation.

Purpose: The study evaluates the efficacy of cone-beam computed tomography (CBCT)-based synthetic CTs (sCT) as a potential alternative to verification CT (vCT) for enhanced treatment monitoring and early adaptation in proton therapy. Methods: Seven common treatment sites were studied. Two sets of sCT per case were generated: direct-deformed (DD) sCT and image-correction (IC) sCT. The image qualities and dosimetric impact of the sCT were compared to the same-day vCT. Results: The sCT agreed with vCT in regions of homogeneous tissues such as the brain and breast; however, notable discrepancies were observed in the thorax and abdomen. The sCT outliers existed for DD sCT when there was an anatomy change and for IC sCT in low-density regions. The target coverage exhibited less than a 5% variance in most DD and IC sCT cases when compared to vCT. The D_max of serial organ-at-risk (OAR) in sCT plans shows greater deviation from vCT than small-volume dose metrics (D0.1cc). The parallel OAR volumetric and mean doses remained consistent, with average deviations below 1.5%. Conclusion: The use of sCT enables precise treatment and prompt early adaptation for proton therapy. The quality assurance of sCT is mandatory in the early stage of clinical implementation.

Echocardiography is a first-line, non-invasive imaging modality widely used to assess cardiac structure and function; however, its interpretation remains highly operator dependent and subject to variability. The integration of artificial intelligence (AI) into echocardiographic practice holds the potential to transform workflows, enhance efficiency, and improve the consistency of assessments across diverse clinical settings. Interest in the application of AI to echocardiography has grown significantly since the early 2000s with AI models that assist with image acquisition, disease detection, measurement automation, and prognostic stratification for various cardiac conditions. Despite this momentum, the safe and effective deployment of AI models relies on rigorous development and validation practices, yet these are infrequently described in the literature. This narrative review aims to provide a comprehensive overview of the essential steps in the development and validation of AI models for echocardiography. Additionally, it explores current challenges and outlines future directions for the integration of AI within echocardiography.

Background: Cardiac damage (CD) is prevalent across the spectrum of aortic stenosis (AS) and is a strong predictor of adverse outcomes. The natural history of CD in early AS and drivers of its progression, are poorly defined. We aimed to identify predictors and the prognostic significance of CD progression in patients with non-severe AS. Methods: This was a single centre cohort study with longitudinal follow-up. Patients with mild or moderate AS at baseline and over serial echocardiographic follow-up were evaluated for the presence and progression of CD over time. Results: 1751 patients were included. At baseline, CD was present in 1320 (75%), categorized as stage 1 in 231 (13%), stage 2 in 672 (38%), stage 3 in 109 (6%) and stage 4 in 308 (18%). Over a median follow-up of 2.1 [Q1–Q3 1.1-3.6] years, the prevalence of advanced-stage (stages 3 and 4) CD increased from 24% to 30%. Factors associated with progression to advanced-stage CD included comorbidity burden (adjusted OR [aOR] 1.23 per additional comorbidity; 95% CI: 1.10–1.38; p < 0.001) and the presence of atrial fibrillation (aOR 3.23; 95% CI: 1.15–9.07, p = 0.024) or left atrial dilation (aOR 1.55; 95% CI: 1.11–2.16, p = 0.010)], but not AS severity. Progression of CD was independently associated with all-cause mortality (adjusted HR [aHR] 1.35; 95% CI: 1.07–170; p = 0.012). Conclusions: In non-severe AS, CD is common and frequently progresses over time. CD progression appears to be driven by comorbidities and pre-existing myocardial remodelling and is an independent predictor of all-cause mortality.