Dr. Mutlu Demirer Profile

Dr. Mutlu Demirer

at Mayo Clinic Florida

SPIE Involvement:

Author

Area of Expertise:

Artificial Intelligence , Image Processing

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Company Website

Profile Summary

Mutlu Demirer is an assistant professor of radiology. Before becoming a principal data science analyst in the Department of Radiology at Mayo Clinic, Florida (2020-present), he was a postdoctoral researcher and research scientist at the Department of Radiology of Ohio State University (2015–2020). He received his PhD in electrical and electronics engineering from Uludag University in Bursa, Turkey (2002–2010). His research interests include image processing and artificial intelligence.

Publications (1)

SPIE Journal Paper | 26 October 2022 Open Access

Pre-deployment assessment of an AI model to assist radiologists in chest X-ray detection and identification of lead-less implanted electronic devices for pre-MRI safety screening: realized implementation needs and proposed operational solutions

Richard D. White, Mutlu Demirer, Vikash Gupta, Ronnie Sebro, Frederick Kusumoto, Barbaros Erdal

JMI, Vol. 9, Issue 05, 054504, (October 2022) https://doi.org/10.1117/12.10.1117/1.JMI.9.5.054504

KEYWORDS: Artificial intelligence, Chest imaging, Performance modeling, Data modeling, X-ray detectors, Magnetic resonance imaging, Safety, Process modeling, Visualization, Instrument modeling

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PurposeChest X-ray (CXR) use in pre-MRI safety screening, such as for lead-less implanted electronic device (LLIED) recognition, is common. To assist CXR interpretation, we “pre-deployed” an artificial intelligence (AI) model to assess (1) accuracies in LLIED-type (and consequently safety-level) identification, (2) safety implications of LLIED nondetections or misidentifications, (3) infrastructural or workflow requirements, and (4) demands related to model adaptation to real-world conditions.ApproachA two-tier cascading methodology for LLIED detection/localization and identification on a frontal CXR was applied to evaluate the performance of the original nine-class AI model. With the unexpected early appearance of LLIED types during simulated real-world trialing, retraining of a newer 12-class version preceded retrialing. A zero footprint (ZF) graphical user interface (GUI)/viewer with DICOM-based output was developed for inference-result display and adjudication, supporting end-user engagement and model continuous learning and/or modernization.ResultsDuring model testing or trialing using both the nine-class and 12-class models, robust detection/localization was consistently 100%, with mAP 0.99 from fivefold cross-validation. Safety-level categorization was high during both testing (AUC ≥ 0.98 and ≥0.99, respectively) and trialing (accuracy 98% and 97%, respectively). LLIED-type identifications by the two models during testing (1) were 98.9% and 99.5% overall correct and (2) consistently showed AUC ≥ 0.92 (1.00 for 8/9 and 9/12 LLIED-types, respectively). Pre-deployment trialing of both models demonstrated overall type-identification accuracies of 94.5% and 95%, respectively. Of the small number of misidentifications, none involved MRI-stringently conditional or MRI-unsafe types of LLIEDs. Optimized ZF GUI/viewer operations led to greater user-friendliness for radiologist engagement.ConclusionsOur LLIED-related AI methodology supports (1) 100% detection sensitivity, (2) high identification (including MRI-safety) accuracy, and (3) future model deployment with facilitated inference-result display and adjudication for ongoing model adaptation to future real-world experiences.

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