Successful implementation of an automated electronic support system for patient safety monitoring: The alemtuzumab in multiple sclerosis safety systems (AMS3) study.

BACKGROUND: Alemtuzumab is a highly effective treatment for relapsing-remitting multiple sclerosis (MS) but requires ongoing pathology monitoring for autoimmune adverse effects. The Alemtuzumab in MS Safety Systems (AMS3) study evaluated the implementation of an automated pathology-monitoring system. OBJECTIVES: To develop an efficient automated clinical decision support system (CDSS) to electronically prompt and track pathology collection and to provide prescribers and patients with customised alerts of abnormal results for identified risks. METHODS: A total of 10 patients with relapsing-remitting MS treated with alemtuzumab were enrolled to test the system. Standard care laboratory monitoring was performed and compared to the performance of the CDSS. RESULTS: The automated CDSS, an integrated patient smartphone application and an additional pre-screening tool were all successfully developed. Compliance with pathology monitoring was 96.7%. The automated analysis of pathology results was significantly faster than standard care neurologist review (p < 0.001). The system correctly identified and alerted abnormalities, including one case of immune thrombocytopenia (ITP) while the treating neurologist was on leave, enabling prompt treatment of serious adverse events. During the course of the study, the CDSS was deployed throughout Australia. CONCLUSION: We successfully developed automated pathology monitoring with a CDSS, demonstrating real-world benefits of high compliance and timely alerting of important results.

Real world validation of an AI-based CT hemorrhage detection tool.

Introduction: Intracranial hemorrhage (ICH) is a potentially life-threatening medical event that requires expedited diagnosis with computed tomography (CT). Automated medical imaging triaging tools can rapidly bring scans containing critical abnormalities, such as ICH, to the attention of radiologists and clinicians. Here, we retrospectively investigated the real-world performance of VeriScout™, an artificial intelligence-based CT hemorrhage detection and triage tool. Methods: Ground truth for the presence or absence of ICH was iteratively determined by expert consensus in an unselected dataset of 527 consecutively acquired non-contrast head CT scans, which were sub-grouped according to the presence of artefact, post-operative features and referral source. The performance of VeriScout™ was compared with the ground truths for all groups. Results: VeriScout™ detected hemorrhage with a sensitivity of 0.92 (CI 0.84-0.96) and a specificity of 0.96 (CI 0.94-0.98) in the global dataset, exceeding the sensitivity of general radiologists (0.88) with only a minor relative decrement in specificity (0.98). Crucially, the AI tool detected 13/14 cases of subarachnoid hemorrhage, a potentially fatal condition that is often missed in emergency department settings. There was no decrement in the performance of VeriScout™ in scans containing artefact or postoperative change. Using an integrated informatics platform, VeriScout™ was deployed into the existing radiology workflow. Detected hemorrhage cases were flagged in the hospital radiology information system (RIS) and relevant, annotated, preview images made available in the picture archiving and communications system (PACS) within 10 min. Conclusion: AI-based radiology worklist prioritization for critical abnormalities, such as ICH, may enhance patient care without adding to radiologist or clinician burden.

A real-world clinical validation for AI-based MRI monitoring in multiple sclerosis.

Modern management of MS targets No Evidence of Disease Activity (NEDA): no clinical relapses, no magnetic resonance imaging (MRI) disease activity and no disability worsening. While MRI is the principal tool available to neurologists for monitoring clinically silent MS disease activity and, where appropriate, escalating treatment, standard radiology reports are qualitative and may be insensitive to the development of new or enlarging lesions. Existing quantitative neuroimaging tools lack adequate clinical validation. In 397 multi-center MRI scan pairs acquired in routine practice, we demonstrate superior case-level sensitivity of a clinically integrated AI-based tool over standard radiology reports (93.3% vs 58.3%), relative to a consensus ground truth, with minimal loss of specificity. We also demonstrate equivalence of the AI-tool with a core clinical trial imaging lab for lesion activity and quantitative brain volumetric measures, including percentage brain volume loss (PBVC), an accepted biomarker of neurodegeneration in MS (mean PBVC -0.32% vs -0.36%, respectively), whereas even severe atrophy (>0.8% loss) was not appreciated in radiology reports. Finally, the AI-tool additionally embeds a clinically meaningful, experiential comparator that returns a relevant MS patient centile for lesion burden, revealing, in our cohort, inconsistencies in qualitative descriptors used in radiology reports. AI-based image quantitation enhances the accuracy of, and value-adds to, qualitative radiology reporting. Scaled deployment of these tools will open a path to precision management for patients with MS.

Reliability of MRI assessment of supraspinatus tendinopathy.

OBJECTIVE: To determine the interobserver and intraobserver reliability of the interpretation of MRIs for supraspinatus tendinosis. METHODS: In the interobserver trial, the MRIs of 52 athletes' shoulders were observed by 3 observers on one occasion within a 2-month period. All 52 images were read by the most experienced musculoskeletal radiologist on 3 different occasions on separate days without access to the previous readings for the intraobserver trial. Supraspinatus tendinosis was graded using a modified 4-point scale from grades 0 to grade 3. RESULTS: The grading of MRI-determined supraspinatus tendinosis was reliable, having an intraclass correlation (ICC) of 0.85 when assessed by the single well-trained observer. Interobserver reliability was only fair to good (ICC = 0.55). CONCLUSIONS: Supraspinatus tendinosis can be accurately identified on MRI with little variation by a single well-trained observer. Interobserver reliability was only fair to good. Our data indicated that the reliability of the assessment was much greater in more experienced radiologists than in those with less experience.