Prof. Jie Lu | Health Sciences | Best Scholar Award

Prof. Jie Lu | Health Sciences | Best Scholar Award

Vice advisor, The Affiliated Hospital of Qingdao University, China

Dr. Jie Lu is a distinguished medical researcher and Associate Professor at The Affiliated Hospital of Qingdao University. With a strong background in internal medicine, endocrinology, and metabolism, Dr. Lu has made significant contributions to the field of hyperuricemia and gout research. His expertise in molecular and clinical studies has led to groundbreaking findings, including the development of a spontaneous hyperuricemia mouse model and critical insights into the relationship between hyperuricemia and metabolic disorders. His research has been widely recognized, earning him prestigious awards and global collaborations.

Publication Profile

🎓 Education

Dr. Jie Lu completed his MD and PhD in Internal Medicine from Qingdao University (2015-2018), following an MS in Endocrine and Metabolism (2012-2015) from the same institution. His academic journey began with a Bachelor’s in Medicine from Shandong Second Medical University (2007-2012), laying a strong foundation for his career in medical research.

👨‍🔬 Experience

Dr. Lu has been serving as an Associate Professor at The Affiliated Hospital of Qingdao University since 2022. Prior to this, he worked as a Research Fellow at the same institution (2018-2022), where he played a pivotal role in advancing hyperuricemia research. His international training experience spans institutions such as the University of Otago, Henry Ford Immunology Program in the USA, and the Chinese Academy of Sciences, enriching his expertise in bioinformatics, immunology, and molecular biology.

🏆 Awards and Honors

Dr. Lu’s excellence in medical research has been recognized through numerous prestigious awards. In 2022, he received the 13th Qingdao Youth Science and Technology Award. His contributions to scientific progress earned him the First Award of Shandong Province Science and Technology Progress in 2020. Throughout his academic career, he has been honored with multiple scholarships, including the National Graduate Scholarship (2017), Excellent Student Scholarship (2016-2017), and Freshman Scholarship (2015). His outstanding performance as a researcher and student has been acknowledged with multiple “Excellent Graduate” awards from Shandong Province and Qingdao University.

🔬 Research Focus

Dr. Lu’s research is dedicated to understanding the clinical and molecular mechanisms of hyperuricemia and gout. His pioneering work in constructing a spontaneous hyperuricemia mouse model has provided invaluable insights into disease progression and treatment strategies. His studies explore the relationship between hyperuricemia and renal function, diabetes, and arterial sclerosis, with high-impact publications in leading journals such as Nature Reviews Rheumatology, Kidney International, and Diabetes. He has also been instrumental in identifying the impact of COVID-19 vaccination on gout attacks, proposing colchicine as a preventive treatment.

🏁 Conclusion

Dr. Jie Lu is a leading researcher in hyperuricemia and gout, with a remarkable academic and research career. His extensive publications, international collaborations, and pioneering research have significantly contributed to the medical field. Recognized for his scientific achievements, Dr. Lu continues to make impactful discoveries that shape the future of metabolic disease treatment.

📚 Publications

Colchicine prophylaxis is associated with fewer gout flares after COVID-19 vaccinationAnnals of the Rheumatic Diseases, 2022 🔗

Mouse models for human hyperuricaemia: a critical reviewNature Reviews Rheumatology, 2019 🔗

Knockout of the urate oxidase gene provides a stable mouse model of hyperuricemia associated with metabolic disordersKidney International, 2018 🔗

Hyperuricemia predisposes to the onset of diabetes via promoting pancreatic β-cell death in uricase-deficient male miceDiabetes, 2020 🔗

Urate-lowering therapy alleviates atherosclerosis inflammatory response factors and neointimal lesions in a mouse modelThe FEBS Journal, 2019 🔗

Superiority of low-dose benzbromarone to low-dose febuxostat in a prospective, randomized comparative effectiveness trial in gout patients with renal uric acid underexcretionArthritis Rheumatol, 2022 🔗

Metabolomics and Machine Learning Identify Metabolic Differences and Potential Biomarkers for Frequent Versus Infrequent Gout FlaresArthritis Rheumatol, 2023 🔗

Profiling of Serum Oxylipins Identifies Distinct Spectrums and Potential Biomarkers in Young People with Very Early Onset GoutRheumatology (Oxford), 2023 🔗

Effects of fenofibrate therapy on renal function in primary gout patientsRheumatology (Oxford), 2021 🔗

Trends in the manifestations of 9754 gout patients in a Chinese clinical center: A 10-year observational studyJoint Bone Spine, 2020 🔗

Doohee Lee | Medical imaging | Computer Vision Contribution Award

Mr. Doohee Lee | Medical imaging | Computer Vision Contribution Award

ZIOVISION Co., Ltd., South Korea

Doohee Lee is the Chief Operating Officer (COO) at ZIOVISION Co., Ltd., where he leads advancements in AI-driven medical imaging solutions. With over a decade of experience in the medical AI field, he has collaborated with notable institutions such as Seoul National University Hospital and MEDICALIP Co., Ltd. He has published over 15 peer-reviewed articles and holds multiple U.S. and international patents in medical imaging and AI technologies. His innovative contributions in healthcare aim to revolutionize diagnostics and improve patient outcomes through advanced imaging technologies. 🔬💡

Publication Profile

Education:

Doohee Lee holds a B.S. and M.S. in Computer Science Engineering and is currently a Ph.D. candidate at Kangwon National University. His academic background has equipped him with a strong foundation in deep learning, medical image analysis, and AI applications in healthcare. 🎓📚

Experience:

With extensive experience in both academia and industry, Doohee Lee has contributed significantly to the field of medical AI. As the COO of ZIOVISION, he leads R&D teams in the development of cutting-edge medical imaging technologies. His previous roles at MEDICALIP Co., Ltd. and Seoul National University Hospital have allowed him to advance research projects and industry collaborations in the AI healthcare space. 💼🧑‍💻

Awards and Honors:

Doohee Lee’s groundbreaking work has earned him numerous accolades, including recognition in AI-driven medical imaging advancements. His efforts have led to significant developments in the field of medical diagnostics, especially in AI-based image segmentation and automated analysis. 🏆👏

Research Focus:

Doohee Lee specializes in AI-driven medical image analysis, focusing on deep learning-based segmentation, 3D image analysis, and clinical AI applications. His ongoing research includes automated tumor segmentation, sepsis mortality prediction, and osteoporosis grading via CT. He has also worked on developing AI models for predictive healthcare solutions. 🧠💻

Conclusion:

Doohee Lee’s expertise in medical AI and his leadership at ZIOVISION continue to drive innovation in healthcare. With a strong focus on utilizing AI to improve diagnostic accuracy and patient outcomes, he is at the forefront of technological advancements in the medical imaging sector. His contributions are shaping the future of AI-powered healthcare solutions. 🌐💪

Publications:

A Refined Approach to Segmenting and Quantifying Inter-Fracture Spaces in Facial Bone CT Imaging (2025) – Applied Sciences
DOI: 10.3390/app15031539
Cited by: 10 citations 📑

Very fast, high-resolution aggregation 3D detection CAM to quickly and accurately find facial fracture areas  (2024) – Computer Methods and Programs in Biomedicine
DOI: 10.1016/j.cmpb.2024.108379
Cited by: 5 citations 📑

Deep Learning-Based Dual-Stage Model for Accurate Nasogastric Tube Positioning in Chest Radiographs (2024) – SSRN
DOI: 10.2139/ssrn.4965848
Cited by: 3 citations 📑

Early Prediction of Mortality for Septic Patients Visiting Emergency Room Based on Explainable Machine Learning: A Real-World Multicenter Study (2024) – Journal of Korean Medical Science
DOI: 10.3346/jkms.2024.39.e53
Cited by: 15 citations 📑

Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep Learning (2022) – Journal of Magnetic Resonance Imaging
DOI: 10.1002/jmri.28332
Cited by: 50 citations 📑

Development of a deep learning-based auto-segmentation algorithm for hepatocellular carcinoma (HCC) and application to predict microvascular invasion of HCC using CT texture analysis: preliminary results. (2022) – Acta radiologica (Stockholm, Sweden: 1987)
DOI: 10.1177/02841851221100318
Cited by: 20 citations 📑

Clinical application of patient-specific 3D printing brain tumor model production system for neurosurgery (2021) – Scientific Reports
DOI: 10.1038/s41598-021-86546-y
Cited by: 30 citations 📑