Dr. Vikash Gupta Profile

Dr. Vikash Gupta

at Mayo Clinic

SPIE Involvement:

Author

Publications (3)

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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Proceedings Article | 17 November 2017 Paper

Brain cortical structural differences between non-central nervous system cancer patients treated with and without chemotherapy compared to non-cancer controls: a cross-sectional pilot MRI study using clinically indicated scans

Mark Shiroishi, Vikash Gupta, Bavrina Bigjahan, Steven Cen, Faisal Rashid, Darryl Hwang, Alexander Lerner, Orest Boyko, Chia-Shang Jason Liu, Meng Law, Paul Thompson, Neda Jahanshad

Proceedings Volume 10572, 105720G (2017) https://doi.org/10.1117/12.2285971

KEYWORDS: Cancer, Neuroimaging, Brain, Breast cancer, Magnetic resonance imaging, Lung cancer, Oncology, Image segmentation, Statistical analysis

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Proceedings Article | 26 January 2017 Paper

Clustering white matter fibers using support vector machines: a volumetric conformal mapping approach

Vikash Gupta, Gautam Prasad, Paul Thompson

Proceedings Volume 10160, 101601A (2017) https://doi.org/10.1117/12.2256974

KEYWORDS: Biomedical optics, Brain, Brain mapping, Statistical analysis, Neurodegeneration, Optical spheres, Medical imaging, Associative arrays, Spherical lenses, Diffusion, Differential equations, Image registration, Binary data

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