Deans' stroke musings: The predictive performance of artificial intelligence on the outcome of stroke: a systematic review and meta-analysis

Changing stroke rehab and research worldwide now.Time is Brain! trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 523 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.

What this blog is for:

My blog is not to help survivors recover, it is to have the 10 million yearly stroke survivors light fires underneath their doctors, stroke hospitals and stroke researchers to get stroke solved. 100% recovery. The stroke medical world is completely failing at that goal, they don't even have it as a goal. Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It lays out what needs to be done to get stroke survivors closer to 100% recovery. It's quite disgusting that this information is not available from every stroke association and doctors group.

Sunday, September 24, 2023

The predictive performance of artificial intelligence on the outcome of stroke: a systematic review and meta-analysis

Ohhh! A shiny new thing, but still just predicting failure to recover. Great distraction though! Useless!

The predictive performance of artificial intelligence on the outcome of stroke: a systematic review and meta-analysis

Yujia Yang¹

Li Tang¹

Yiting Deng¹

Xuzi Li¹

Anling Luo¹

Zhao Zhang¹

Li He¹

Cairong Zhu²^*

Muke Zhou¹^*

¹Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
²Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China

Objectives: This study aimed to assess the accuracy of artificial intelligence (AI) models in predicting the prognosis of stroke.

Methods: We searched PubMed, Embase, and Web of Science databases to identify studies using AI for acute stroke prognosis prediction from the database inception to February 2023. Selected studies were designed cohorts and had complete data. We used the Quality Assessment of Diagnostic Accuracy Studies tool to assess the qualities and bias of included studies and used a random-effects model to summarize and analyze the data. We used the area under curve (AUC) as an indicator of the predictive accuracy of AI models.

Results: We retrieved a total of 1,241 publications and finally included seven studies. There was a low risk of bias and no significant heterogeneity in the final seven studies. The total pooled AUC under the fixed-effects model was 0.872 with a 95% CI of (0.862–0.881). The DL subgroup showed its AUC of 0.888 (95%CI 0.872–0.904). The LR subgroup showed its AUC 0.852 (95%CI 0.835–0.869). The RF subgroup showed its AUC 0.863 (95%CI 0.845–0.882). The SVM subgroup showed its AUC 0.905 (95%CI 0.857–0.952). The Xgboost subgroup showed its AUC 0.905 (95%CI 0.805–1.000).

Conclusion: The accuracy of AI models in predicting the outcomes of ischemic stroke is good from our study. It could be an assisting tool for physicians in judging the outcomes of stroke patients. With the update of AI algorithms and the use of big data, further AI predictive models will perform better.