Surgical Science Sweden AB (SUSRF) Fair Value & Analysis
Healthcare · US · Market cap $280M
Fair value as of: Jun 24, 2026
Analysis
Surgical Science Sweden AB (SUSRF) currently trades at $4.00, while our model-based Fair Value estimate is $5.19 — implying the stock looks roughly 29.8% undervalued today. We read business quality at 95/100 (high quality), in the Healthcare sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: high) — always confirm before acting.
About the company
Surgical Science Sweden AB (publ) develops and markets virtual reality simulators and customized training solutions for evidence-based medical training in Europe, North and South America, Asia, and internationally. The company offers Angio Mentor simulator for endovascular training; BRONCH Express, a portable version of BRONCH mentor simulator; ARTHRO Mentor for arthroscopic surgery skills acquisition; BRONCH Mentor, a simulator for optimizing bronchoscopy training; GI Mentor, a simulator for GI Endoscopy; HYST Mentor for True-To-Life diagnostic and therapeutic hysteroscopy training; URO Mentor, a virtual reality simulator for endourology training; EndoSim, a realistic scope simulator; FlexVR, a flexible and portable training platform which is designed to teach basic and advanced skills for robotic surgery; and LAP Mentor provides a wide array of laparoscopic training cases. It also provides LapSim training system comprising LapSim Haptic simulator that provides laparoscopic trainin…
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How we calculate Fair Value
Each company is valued through a stack of independent intrinsic-value models (DCF variants, residual-income, multiples and more), blended into one family-balanced consensus and weighted by how much trustworthy data backs it. A separate quality layer scores the fundamentals. Every input is real reported data — nothing guessed.
Educational research only · not financial advice · no buy/sell recommendation. Model-based estimates are not certainties; their reliability depends on data quality and assumptions.