SUMMARY
Timothy L. Kline, Ph.D., M.S., is a distinguished researcher at Mayo Clinic, where he leads the development and application of advanced artificial intelligence, machine learning, and deep learning methods to medical imaging. His work focuses on developing new algorithms for image acquisition and processing and strives to improve the accuracy and efficiency of diagnostic tools.
Dr. Kline is particularly renowned for his contributions to image processing, where he employs artificial intelligence and machine learning techniques to automate the segmentation of organs and tissues. His research involves identifying imaging biomarkers that correlate with genomic data, as well as predictive modeling, providing insights into conditions and their progression. Dr. Kline has pioneered the use of advanced imaging modalities, such as multiparametric magnetic resonance imaging, to improve the detection and analysis of complex medical conditions.
Dr. Kline leads and collaborates on projects at the Imaging and Analysis Core of the Mayo Clinic Robert M. and Billie Kelley Pirnie Translational Polycystic Kidney Disease Center. These projects use artificial intelligence and machine learning methodologies that improve patient care. His efforts aim to standardize image-based measurements and develop new biomarkers that may expedite the assessment of medical interventions. This work may ultimately contribute to more personalized and effective healthcare solutions.
Focus areas
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Polycystic kidney disease. In collaboration with the Mayo Clinic Pirnie Translational Polycystic Kidney Disease Center, Dr. Kline is searching for new image-based biomarkers involved in polycystic kidney disease. Image-based biomarkers, such as texture analysis, multiparametric magnetic resonance imaging and quantitative magnetic resonance imaging, are good indicators of the prognosis in polycystic kidney disease. These image-based biomarkers correlate with the clinical progression and have the ability to evaluate the effectiveness of interventions.
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Imaging genomics. In collaboration with the Radiology Informatics Lab of Bradley J. Erickson, M.D., Ph.D., Dr. Kline applies classical machine learning and deep learning techniques to automate segmentation of both organ and tissue regions. He also performs imaging genomics to identify imaging biomarkers that can identify the genomics of a condition.
Significance to patient care
Dr. Kline's work in automation and imaging may evolve patient care. He aims to standardize image-based measurements and provide new image-based biomarkers. Dr. Kline's research may greatly affect how patient outcomes are determined and allow healthcare teams to learn sooner how well treatments are working.
Professional highlights
- Society for Imaging Informatics in Medicine:
- Chair, Machine Learning Education Subcommittee, 2022-2025.
- Second place, Scientific Award, 2015.
- Best poster, European Society of Urogenital Radiology, 2019.
- Loan Repayment Program Award, National Institutes of Health, 2016.