Mr. Steve G. Kohlmyer Profile

Steve G. Kohlmyer

at MAXQ-AI

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 15 February 2021 Presentation + Paper

A translational clinical assessment workflow for the validation of external artificial intelligence models

Chintan Shah, Steve Kohlmyer, Kyle Hunter, Stephen Jones, Po-Hao Chen

Proceedings Volume 11601, 116010F (2021) https://doi.org/10.1117/12.2581771

KEYWORDS: Artificial intelligence, Process modeling, Data modeling, Radiology, Head, Computer security

Read Abstract +

Due to variations in unique patient populations, imaging hardware, and drift, artificial intelligence (AI) models perform differently in different clinical practices. The validation of externally developed models is a critical step in implementation, but several challenges related to data de-identification, security, and exchange exist. We created a workflow allowing our clinical radiology practice to safely evaluate external AI models. The workflow encompassed four steps: study selection, extraction, inference, and assessment. A commercially available AI model for intracranial hemorrhage (ICH) was used as a proof of concept. Noncontrast head CT cases were collected using both an internal search engine and a neuroradiologist teaching file that contained 16 exams in which ICH had been missed on the original radiology interpretation. These challenge cases were included to enrich the cohort. Our DICOM de-identification and processing pipeline (D2P2) processed the header and stripped identifiable information. The cleaned data was made available to the external party for AI model processing. The processed results are matched against the ground truth, and performance metrics were calculated, including subgroup analyses. The overall precision was 0.98, the recall was 0.75, and the specificity was 0.97, with an F1 score of 0.85. This result was similar to the original radiologist performance for this enriched challenge cohort. The estimated combined performance of the original radiologist with AI improved the recall to 0.91. Subgroup analyses suggested that combined performance was improved over radiologists or AI alone for subtle hemorrhages or those without mass effect. An integrated workflow to handle the assessment and validation of external deep learning models before implementation is feasible and can aid radiology practices seeking to deploy AI products, particularly in assessing the performance in the local setting and clinical value-added scenarios.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years