Dr. Jovana Panic, Osaka University, Japan
Dr. Jovana Panic is a Postdoctoral Researcher at the Institute of Datability Science, Osaka University, Japan. Born on November 14, 1994, she specializes in biomedical engineering, artificial intelligence, and medical image analysis. With strong intercultural skills developed through volunteering and theater experiences, she is a multilingual collaborator fluent in Italian, English, Serbian, and Japanese. She holds a Ph.D. in Bioengineering and Surgical Sciences from the Polytechnic University of Turin. Her work focuses on AI-driven solutions for medical imaging, particularly retinal and oncological applications. Dr. Panic is passionate about merging technology and healthcare for precision diagnostics. 🌍🧠📊
Publication Profile
Google Scholar
🎓 Education
Dr. Panic earned her Ph.D. in Bioengineering and Medical-Surgical Sciences from the Polytechnic University of Turin and the University of Turin in March 2024 with an “Excellent” grade. Her research focused on AI-driven MRI systems for cancer detection. She holds a Master’s degree in Biomedical Engineering (2019) with a grade of 109/110, and a Bachelor’s degree in Biomedical Engineering (2016) with 100/110, both from the Polytechnic University of Turin. Her academic background includes biomedical signal processing, clinical informatics, and bioimaging. She completed high school at Scientific High School “Carlo Cattaneo,” Turin, in 2013 with a score of 93/100. 🎓📘🔬
💼 Experience
Dr. Panic began her postdoctoral role at Osaka University in April 2024, working on AI systems for retinal fundus image analysis. From 2020 to 2024, she was a Ph.D. student and teaching assistant in AI and biomedical engineering at the Polytechnic and University of Turin. She contributed to research at the Candiolo Cancer Institute, developing AI models for rectal and prostate cancer MRI analysis. Her earlier experiences include a research fellowship (2019–2020), clinical engineering at Cellini Humanitas Hospital, and a master’s thesis on DL-based segmentation of rectal cancer. Her multidisciplinary roles demonstrate her commitment to medical AI innovation. 🏥🖥️📈
🏅 Awards & Honors
Dr. Panic has been involved in multiple prestigious European and national research projects, including AIRC, Horizon 2020, and Ministry of Health grants. These projects supported her work in AI-based segmentation, response prediction, and virtual biopsy systems for rectal and prostate cancers. Her Ph.D. research received an “Excellent” grade, recognizing her contributions to oncological imaging. She also served as a teaching assistant in graduate and medical programs, reflecting academic trust in her expertise. Additionally, she received internship and research fellow opportunities at leading institutions like the Candiolo Cancer Institute and Humanitas Hospital, underscoring her promising research trajectory. 🧠🏆📚
🔬 Research Focus
Dr. Panic’s research integrates artificial intelligence and biomedical imaging for clinical applications. Her primary focus is on radiomics and deep learning algorithms for automated cancer detection and characterization in MRI and retinal images. She developed convolutional neural networks (CNNs) for rectal and prostate cancer analysis and is currently exploring AI-based retinal fundus image systems. Her work spans radiology, surgical sciences, and data science, aiming to enhance diagnostic precision through AI-powered clinical decision tools. Through her interdisciplinary research, she bridges computer science and medicine to revolutionize cancer imaging and eye disease diagnostics. 🧬💻🩺
Publication Top Notes
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🧠📊 Radiomics and MRI of rectal cancer: from engineering to clinical practice – 51 citations, 2021
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🧬🤖 MRI-based radiomics to predict response in rectal cancer: manual vs automatic segmentation – 28 citations, 2022
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🧠📈 CNN-based system for colorectal cancer segmentation on MRI – 23 citations, 2020
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💉🧮 Radiomics to predict chemo response in liver metastases (CT images) – 15 citations, 2020
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📊🏥 Normalization strategies in multi-center abdominal MRI radiomics: review/meta-analysis – 12 citations, 2023
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🧪⚙️ Normalization improving robustness of MRI radiomics – 9 citations, 2023
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🧬💊 Predicting resistance to 1st line chemo in liver metastatic colorectal cancer – 9 citations, 2021
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🔍🧠 Deep learning for prostate segmentation on T2w MRI – 7 citations, 2021
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🩻🔬 Virtual biopsy in abdominal pathology: current status – 6 citations, 2023
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🤖🧠 Fully automatic DL segmentation of rectal cancer (multi-center) – 6 citations, 2022