ReviewResearch (Washington, D.C.)2026
Overcoming Mechanical Blindness: Adaptive Surgical Instrument Design for the Heterogeneous Landscape of Uterine Fibroids.
Review in Research (Washington, D.C.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Uterine fibroids pose a major global health burden, affecting approximately 120 million women worldwide. While minimally invasive surgeries, such as electromechanical morcellation, preserve fertility, they are severely hampered by the risk of unpredictable tissue dissemination-a hazard that prompted a U.S. Food and Drug Administration black-box warning. Resolving this clinical dilemma requires a paradigm shift in instrument design. This review identifies a persistent "mechanical blind spot": Current surgical tools operate with static parameters that fundamentally clash with the profound multi-scale mechanical heterogeneity of fibroids. Driven by FIGO-defined microenvironments and degeneration-driven remodeling, this extreme variability in tissue stiffness shapes the dynamic-and often hazardous-cutting challenges during surgery. To bridge this critical gap, we review the mechanobiological origins of this heterogeneity and analyze its direct impact on morcellation-induced fragmentation. We then synthesize multiscale mechanical quantification methods (ex vivo and in vivo) and evaluate the evolution of instrument-optimization strategies. Moving beyond conventional structural refinements, we highlight the potential of bio-inspired end-effectors for safe tissue interaction. Crucially, we propose a technological roadmap integrating these physical mechanisms with artificial intelligence-enabling preoperative "mechanical mapping" and real-time adaptive control. Taken together, this work offers new perspectives for the clinical management of this prevalent condition and provides a comprehensive theoretical framework for developing next-generation, mechanics-aware surgical ecosystems.
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Registered trials
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