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AI-supported image analysis for drug development

Pharmaceutical and biotech companies are our customers. We provide AI-supported image analysis and real-world evidence (RWE) to support drug development (Phase IIa through IIIb) and post-market safety surveillance (Phase IV).

Pioneers since 2009A growing portfolio of clinically validated imaging biomarkers (CE-certified) for care and clinical trials.
Regulatory compliantDIN EN ISO 9001:2015, ICH Good Clinical Practice E6(R1), and EU GDPR.
150+ centers in the EUAn established ecosystem of radiological-neurological centers of excellence for effective patient recruitment.

Scientific Publications

A selection of recent original journal articles. The complete list, including conference contributions, can be found on the jung diagnostics website.

Erhart DK, Balz LT, Opfer R, Spies L, et al. (2026)
Cognition and fatigue in clinically stable multiple sclerosis: EDSS and MRI metrics outperform serum biomarkers.
BMC Neurol 26, 260 (2026)
PubMed ↗
Behrendt F, Bhattacharya D, Maack L, Krüger J, Opfer R, Schlaefer A (2026)
A review of deep learning-based Unsupervised Anomaly Detection in brain MRI.
Medical Image Analysis 112 (2026) 104076
PubMed ↗
Opfer R, Spies L, Krüger J, Buddenkotte T, et al. (2025)
Whole brain volume loss is associated with a short-term disability progression in relapse-activity free multiple sclerosis.
J Neurol 272, 715 (2025)
PubMed ↗
Hedderich DM, Opfer R, Krüger J, Spies L, Yakushev I, Buchert R (2025)
Clinical validation of artificial intelligence-based single-subject morphometry without normative reference database.
Journal of Alzheimer's Disease, 2025
PubMed ↗
Opfer R, Schwab M, Bangoura S, et al. (2024)
Patients with relapsing-remitting multiple sclerosis show accelerated whole brain volume and thalamic volume loss early in disease.
Neuroradiology, 2024
PubMed ↗
Opfer R, Ziemssen T, Krüger J, Buddenkotte T, et al. (2024)
Higher effect sizes for the detection of accelerated brain volume loss and disability progression in multiple sclerosis using deep-learning.
Computers in Biology and Medicine 183 (2024) 109289
PubMed ↗
Villringer K, Sokiranski R, Opfer R, Spies L, et al. (2024)
An Artificial Intelligence Algorithm Integrated into the Clinical Workflow Can Ensure High Quality Acute Intracranial Hemorrhage CT Diagnostic.
Clin Neuroradiol, 2024
PubMed ↗
Opfer R, Krüger J, Buddenkotte T, Spies L, Behrendt F, Schippling S, Buchert R (2024)
BrainLossNet: a fast, accurate and robust method to estimate brain volume loss from longitudinal MRI.
Int J Comput Assist Radiol Surg, 2024
PubMed ↗

This is a curated selection. The full list of publications (over 40 original articles plus selected conference contributions) is available at jung-diagnostics.com/en/research.

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