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Injury volume extracted from MRI predicts neurologic outcome in acute spinal cord injury: A prospective TRACK-SCI pilot study

Mummaneni, Nikhil ; Burke, John F. ; DiGiorgio, Anthony M. ; [et al.]

Elsevier BV ; 2020

In: Journal of Clinical Neuroscience Vol. 82 ( 2020-12), p. 231-236

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In: Journal of Clinical Neuroscience, Elsevier BV, Vol. 82 ( 2020-12), p. 231-236

Type of Medium: Online Resource

ISSN: 0967-5868

URL: Article

DOI: 10.1016/j.jocn.2020.11.003

Language: English

Publisher: Elsevier BV

Publication Date: 2020

detail.hit.zdb_id: 2009190-4

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Expert-augmented automated machine learning optimizes hemodynamic predictors of spinal cord injury outcome

Chou, Austin ; Torres-Espin, Abel ; Kyritsis, Nikos ; [et al.]

Public Library of Science (PLoS) ; 2022

In: PLOS ONE Vol. 17, No. 4 ( 2022-4-7), p. e0265254-

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In: PLOS ONE, Public Library of Science (PLoS), Vol. 17, No. 4 ( 2022-4-7), p. e0265254-

Abstract: Artificial intelligence and machine learning (AI/ML) is becoming increasingly more accessible to biomedical researchers with significant potential to transform biomedicine through optimization of highly-accurate predictive models and enabling better understanding of disease biology. Automated machine learning (AutoML) in particular is positioned to democratize artificial intelligence (AI) by reducing the amount of human input and ML expertise needed. However, successful translation of AI/ML in biomedicine requires moving beyond optimizing only for prediction accuracy and towards establishing reproducible clinical and biological inferences. This is especially challenging for clinical studies on rare disorders where the smaller patient cohorts and corresponding sample size is an obstacle for reproducible modeling results. Here, we present a model-agnostic framework to reinforce AutoML using strategies and tools of explainable and reproducible AI, including novel metrics to assess model reproducibility. The framework enables clinicians to interpret AutoML-generated models for clinical and biological verifiability and consequently integrate domain expertise during model development. We applied the framework towards spinal cord injury prognostication to optimize the intraoperative hemodynamic range during injury-related surgery and additionally identified a strong detrimental relationship between intraoperative hypertension and patient outcome. Furthermore, our analysis captured how evolving clinical practices such as faster time-to-surgery and blood pressure management affect clinical model development. Altogether, we illustrate how expert-augmented AutoML improves inferential reproducibility for biomedical discovery and can ultimately build trust in AI processes towards effective clinical integration.

Type of Medium: Online Resource

ISSN: 1932-6203

URL: Article

DOI: 10.1371/journal.pone.0265254

DOI: 10.1371/journal.pone.0265254.g001

DOI: 10.1371/journal.pone.0265254.g002

DOI: 10.1371/journal.pone.0265254.g003

DOI: 10.1371/journal.pone.0265254.g004

DOI: 10.1371/journal.pone.0265254.g005

DOI: 10.1371/journal.pone.0265254.g006

DOI: 10.1371/journal.pone.0265254.g007

DOI: 10.1371/journal.pone.0265254.s001

DOI: 10.1371/journal.pone.0265254.s002

DOI: 10.1371/journal.pone.0265254.s003

DOI: 10.1371/journal.pone.0265254.s004

DOI: 10.1371/journal.pone.0265254.s005

DOI: 10.1371/journal.pone.0265254.s006

DOI: 10.1371/journal.pone.0265254.s007

DOI: 10.1371/journal.pone.0265254.s008

DOI: 10.1371/journal.pone.0265254.s009

DOI: 10.1371/journal.pone.0265254.s010

DOI: 10.1371/journal.pone.0265254.s011

DOI: 10.1371/journal.pone.0265254.s012

DOI: 10.1371/journal.pone.0265254.r001

DOI: 10.1371/journal.pone.0265254.r002

Language: English

Publisher: Public Library of Science (PLoS)

Publication Date: 2022

detail.hit.zdb_id: 2267670-3

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Transforming Research and Clinical Knowledge in Spinal Cord Injury (TRACK-SCI): an overview of initial enrollment and demographics

Tsolinas, Rachel E. ; Burke, John F. ; DiGiorgio, Anthony M. ; [et al.]

Journal of Neurosurgery Publishing Group (JNSPG) ; 2020

In: Neurosurgical Focus Vol. 48, No. 5 ( 2020-05), p. E6-

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In: Neurosurgical Focus, Journal of Neurosurgery Publishing Group (JNSPG), Vol. 48, No. 5 ( 2020-05), p. E6-

Abstract: Traumatic spinal cord injury (SCI) is a dreaded condition that can lead to paralysis and severe disability. With few treatment options available for patients who have suffered from SCI, it is important to develop prospective databases to standardize data collection in order to develop new therapeutic approaches and guidelines. Here, the authors present an overview of their multicenter, prospective, observational patient registry, Transforming Research and Clinical Knowledge in SCI (TRACK-SCI). METHODS Data were collected using the National Institute of Neurological Disorders and Stroke (NINDS) common data elements (CDEs). Highly granular clinical information, in addition to standardized imaging, biospecimen, and follow-up data, were included in the registry. Surgical approaches were determined by the surgeon treating each patient; however, they were carefully documented and compared within and across study sites. Follow-up visits were scheduled for 6 and 12 months after injury. RESULTS One hundred sixty patients were enrolled in the TRACK-SCI study. In this overview, basic clinical, imaging, neurological severity, and follow-up data on these patients are presented. Overall, 78.8% of the patients were determined to be surgical candidates and underwent spinal decompression and/or stabilization. Follow-up rates to date at 6 and 12 months are 45% and 36.3%, respectively. Overall resources required for clinical research coordination are also discussed. CONCLUSIONS The authors established the feasibility of SCI CDE implementation in a multicenter, prospective observational study. Through the application of standardized SCI CDEs and expansion of future multicenter collaborations, they hope to advance SCI research and improve treatment.

Type of Medium: Online Resource

ISSN: 1092-0684

URL: Article

DOI: 10.3171/2020.2.FOCUS191030

Language: Unknown

Publisher: Journal of Neurosurgery Publishing Group (JNSPG)

Publication Date: 2020

detail.hit.zdb_id: 2026589-X

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