Scholars@Duke publication: Commentary: Machine Learning-Driven Clinical Image Analysis to Identify Craniosynostosis: A Pilot Study of Telemedicine and Clinic Patients.

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Commentary: Machine Learning-Driven Clinical Image Analysis to Identify Craniosynostosis: A Pilot Study of Telemedicine and Clinic Patients.

Publication , Journal Article

Quon, JL; Grant, GA

Published in: Neurosurgery

June 1, 2022

Published version (DOI) Link to item

Duke Scholars

Author Gerald Arthur Grant Neurosurgery

Published In

Neurosurgery

DOI

10.1227/neu.0000000000001943

EISSN

1524-4040

Publication Date

June 1, 2022

Volume

90

Issue

6

Start / End Page

e159 / e160

Location

United States

Related Subject Headings

Telemedicine
Pilot Projects
Neurology & Neurosurgery
Machine Learning
Image Processing, Computer-Assisted
Humans
Craniosynostoses
5202 Biological psychology
3209 Neurosciences
3202 Clinical sciences

Citation

APA

Chicago

ICMJE

MLA

NLM

Quon, J. L., & Grant, G. A. (2022). Commentary: Machine Learning-Driven Clinical Image Analysis to Identify Craniosynostosis: A Pilot Study of Telemedicine and Clinic Patients. Neurosurgery, 90(6), e159–e160. https://doi.org/10.1227/neu.0000000000001943

Quon, Jennifer L., and Gerald A. Grant. “Commentary: Machine Learning-Driven Clinical Image Analysis to Identify Craniosynostosis: A Pilot Study of Telemedicine and Clinic Patients.” Neurosurgery 90, no. 6 (June 1, 2022): e159–60. https://doi.org/10.1227/neu.0000000000001943.

Quon JL, Grant GA. Commentary: Machine Learning-Driven Clinical Image Analysis to Identify Craniosynostosis: A Pilot Study of Telemedicine and Clinic Patients. Neurosurgery. 2022 Jun 1;90(6):e159–60.

Quon, Jennifer L., and Gerald A. Grant. “Commentary: Machine Learning-Driven Clinical Image Analysis to Identify Craniosynostosis: A Pilot Study of Telemedicine and Clinic Patients.” Neurosurgery, vol. 90, no. 6, June 2022, pp. e159–60. Pubmed, doi:10.1227/neu.0000000000001943.

Quon JL, Grant GA. Commentary: Machine Learning-Driven Clinical Image Analysis to Identify Craniosynostosis: A Pilot Study of Telemedicine and Clinic Patients. Neurosurgery. 2022 Jun 1;90(6):e159–e160.

Journal cover image

Published In

Neurosurgery

DOI

10.1227/neu.0000000000001943

EISSN

1524-4040

Publication Date

June 1, 2022

Volume

90

Issue

6

Start / End Page

e159 / e160

Location

United States

Related Subject Headings

Telemedicine
Pilot Projects
Neurology & Neurosurgery
Machine Learning
Image Processing, Computer-Assisted
Humans
Craniosynostoses
5202 Biological psychology
3209 Neurosciences
3202 Clinical sciences