Systematic review protocol to assess artificial intelligence diagnostic accuracy performance in detecting acute ischaemic stroke and large-vessel occlusions on CT and MR medical imaging

Barriers to stroke treatment: The price of long-distance from thrombectomy centers

BACKGROUND

Endovascular thrombectomy, the preferred treatment for acute large-vessel occlusion stroke, is highly time-dependent. Many patients live far from thrombectomy centers due to large geographical variations in stroke services. This study aimed to explore the consequences of long transport distance on the proportion of thrombectomy-eligible patients who underwent thrombectomy, the clinical outcomes with or without thrombectomy, the timelines for patients transported, and the diagnostic accuracy of large-vessel occlusion in primary stroke centers.

METHODS

We conducted a retrospective observational study in a county with only primary stroke centers, ∼ 300 km from the nearest thrombectomy center. All stroke patients admitted over a year were retrieved from the Norwegian Stroke Registry. A neuroradiologist identified all computed tomography images with large-vessel occlusions. A panel determined whether these patients had a corresponding clinical indication for thrombectomy.

RESULTS

A total of 50% of the eligible patients did not receive thrombectomy. These patients had a significantly higher risk of severe disability or death compared to the patients who underwent thrombectomy. The median time from computed tomography imaging at the primary stroke center to arrival at the thrombectomy center was over 3 hours. Additionally, 30% of the large-vessel occlusions were initially undiagnosed, and half of these patients had a corresponding clinical indication for thrombectomy.

CONCLUSIONS

In a county with a long transport distance to a thrombectomy center, a high proportion of eligible patients did not undergo thrombectomy, negatively impacting clinical outcomes. The transport time was considerable. A high rate of large-vessel occlusions was initially not diagnosed.

The Artificial Intelligence Revolution in Stroke Care: A Decade of Scientific Evidence in Review

BACKGROUND

The emergence of artificial intelligence (AI) has significantly influenced the diagnostic evaluation of stroke and has revolutionized acute stroke care delivery. The scientific evidence evaluating the role of AI, especially in areas of stroke treatment and rehabilitation is limited but continues to accumulate. We performed a systemic review of current scientific evidence evaluating the use of AI in stroke evaluation and care and examined the publication trends during the past decade.

METHODS

A systematic search of electronic databases was conducted to identify all studies published from 2012 to 2022 that incorporated AI in any aspect of stroke care. Studies not directly relevant to stroke care in the context of AI and duplicate studies were excluded. The level of evidence and publication trends were examined.

RESULTS

A total of 623 studies were examined, including 101 reviews (16.2%), 9 meta-analyses (1.4%), 140 original articles on AI methodology (22.5%), 2 case reports (0.3%), 2 case series (0.3%), 31 case-control studies (5%), 277 cohort studies (44.5%), 16 cross-sectional studies (2.6%), and 45 experimental studies (7.2%). The highest published area of AI in stroke was diagnosis (44.1%) and the lowest was rehabilitation (12%). A 10-year trend analysis revealed a significant increase in AI literature in stroke care.

CONCLUSIONS

Most research on AI is in the diagnostic area of stroke care, with a recent noteworthy trend of increased research focus on stroke treatment and rehabilitation.

Accuracy of Automated Computer-Aided Diagnosis for Stroke Imaging: A Critical Evaluation of Current Evidence

There is increasing interest in computer applications, using artificial intelligence methodologies, to perform health care tasks previously performed by humans, particularly in medical imaging for diagnosis. In stroke, there are now commercial artificial intelligence software for use with computed tomography or MR imaging to identify acute ischemic brain tissue pathology, arterial obstruction on computed tomography angiography or as hyperattenuated arteries on computed tomography, brain hemorrhage, or size of perfusion defects. A rapid, accurate diagnosis may aid treatment decisions for individual patients and could improve outcome if it leads to effective and safe treatment; or conversely, to disaster if a delayed or incorrect diagnosis results in inappropriate treatment. Despite this potential clinical impact, diagnostic tools including artificial intelligence methods are not subjected to the same clinical evaluation standards as are mandatory for drugs. Here, we provide an evidence-based review of the pros and cons of commercially available automated methods for medical imaging diagnosis, including those based on artificial intelligence, to diagnose acute brain pathology on computed tomography or magnetic resonance imaging in patients with stroke.

Checklist for Evaluation of Image-Based Artificial Intelligence Reports in Dermatology: CLEAR Derm Consensus Guidelines From the International Skin Imaging Collaboration Artificial Intelligence Working Group

IMPORTANCE

The use of artificial intelligence (AI) is accelerating in all aspects of medicine and has the potential to transform clinical care and dermatology workflows. However, to develop image-based algorithms for dermatology applications, comprehensive criteria establishing development and performance evaluation standards are required to ensure product fairness, reliability, and safety.

OBJECTIVE

To consolidate limited existing literature with expert opinion to guide developers and reviewers of dermatology AI.

EVIDENCE REVIEW

In this consensus statement, the 19 members of the International Skin Imaging Collaboration AI working group volunteered to provide a consensus statement. A systematic PubMed search was performed of English-language articles published between December 1, 2008, and August 24, 2021, for "artificial intelligence" and "reporting guidelines," as well as other pertinent studies identified by the expert panel. Factors that were viewed as critical to AI development and performance evaluation were included and underwent 2 rounds of electronic discussion to achieve consensus.

FINDINGS

A checklist of items was developed that outlines best practices of image-based AI development and assessment in dermatology.

CONCLUSIONS AND RELEVANCE

Clinically effective AI needs to be fair, reliable, and safe; this checklist of best practices will help both developers and reviewers achieve this goal.