PROTEUS Study: A Prospective Randomized Controlled Trial Evaluating the Use of Artificial Intelligence in Stress Echocardiography.

BACKGROUND: 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. METHODS: 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. DISCUSSION: 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. TRIAL REGISTRATION: Clinicaltrials.gov registration number NCT05028179, registered on 31 August 2021; ISRCTN: ISRCTN15113915; IRAS ref: 293515; REC ref: 21/NW/0199.

Accuracy and safety of an autonomous artificial intelligence clinical assistant conducting telemedicine follow-up assessment for cataract surgery.

Background: Artificial intelligence deployed to triage patients post-cataract surgery could help to identify and prioritise individuals who need clinical input and to expand clinical capacity. This study investigated the accuracy and safety of an autonomous telemedicine call (Dora, version R1) in detecting cataract surgery patients who need further management and compared its performance against ophthalmic specialists. Methods: 225 participants were recruited from two UK public teaching hospitals after routine cataract surgery between 17 September 2021 and 31 January 2022. Eligible patients received a call from Dora R1 to conduct a follow-up assessment approximately 3 weeks post cataract surgery, which was supervised in real-time by an ophthalmologist. The primary analysis compared decisions made independently by Dora R1 and the supervising ophthalmologist about the clinical significance of five symptoms and whether the patient required further review. Secondary analyses used mixed methods to examine Dora R1's usability and acceptability and to assess cost impact compared to standard care. This study is registered with ClinicalTrials.gov (NCT05213390) and ISRCTN (16038063). Findings: 202 patients were included in the analysis, with data collection completed on 23 March 2022. Dora R1 demonstrated an overall outcome sensitivity of 94% and specificity of 86% and showed moderate to strong agreement (kappa: 0.758-0.970) with clinicians in all parameters. Safety was validated by assessing subsequent outcomes: 11 of the 117 patients (9%) recommended for discharge by Dora R1 had unexpected management changes, but all were also recommended for discharge by the supervising clinician. Four patients were recommended for discharge by Dora R1 but not the clinician; none required further review on callback. Acceptability, from interviews with 20 participants, was generally good in routine circumstances but patients were concerned about the lack of a 'human element' in cases with complications. Feasibility was demonstrated by the high proportion of calls completed autonomously (195/202, 96.5%). Staff cost benefits for Dora R1 compared to standard care were £35.18 per patient. Interpretation: The composite of mixed methods analysis provides preliminary evidence for the safety, acceptability, feasibility, and cost benefits for clinical adoption of an artificial intelligence conversational agent, Dora R1, to conduct follow-up assessment post-cataract surgery. Further evaluation in real-world implementation should be conducted to provide additional evidence around safety and effectiveness in a larger sample from a more diverse set of Trusts. Funding: This manuscript is independent research funded by the National Institute for Health Research and NHSX (Artificial Intelligence in Health and Care Award, AI_AWARD01852).

LiverMultiScan as an alternative to liver biopsy to monitor autoimmune hepatitis in the National Health Service in England: an economic evaluation.

BACKGROUND: Autoimmune hepatitis (AIH) is a rare chronic progressive liver disease, managed with corticosteroids and immunosuppressants and monitored using a combination of liver biochemistry and histology. Liver biopsy (gold standard) is invasive, costly and has risk of complications. Non-invasive imaging using multiparametric magnetic resonance (mpMR) can detect the presence and extent of hepatic fibroinflammation in a risk-free manner. OBJECTIVE: To conduct early economic modelling to assess the affordability of using mpMR as an alternative to liver biopsy. METHODS: Medical test costs associated with following 100 patients over a 5-year time horizon were assessed from a National Health Service payor perspective using tariff costs and average biopsy-related adverse events costs. Sensitivity analyses modelling the cost consequences of increasing the frequency of mpMR monitoring within the fixed cost of liver biopsy were performed. RESULTS: Per 100 moderate/severe AIH patients receiving an annual mpMR scan (in place of biopsy), early economic modelling showed minimum cost savings of £232 333. Per 100 mild/moderate AIH patients receiving three mpMR scans over 5 years estimated minimum cost savings were £139 400. One-way sensitivity analyses showed increasing the frequency of mpMR scans from 5 to 10 over 5 years in moderate/severe AIH patients results in a cost saving of £121 926.20. In patients with mild/moderate AIH, an increase from 3 to 6 mpMR scans over 5 years could save £73 155.72. In a minimalistic approach, the use of 5 mpMR scans was still cost saving (£5770.48) if they were to replace two biopsies over the 5-year period for all patients with moderate/severe or mild/moderate AIH. CONCLUSIONS: Integration of mpMR scans in AIH patient pathways leads to significant cost savings when liver biopsy frequency is either reduced or eliminated, in addition to improved patient experience and clinician acceptability as well as providing detailed phenotyping to improve patient outcomes. TRIAL REGISTRATION: NCT03979053.