Patient-Specific Applications and Validation - Mark Taylor

Description

The transcript begins with an individual seeking direction to move forward, setting a stage for a presentation focused on advanced modeling techniques in the medical field, particularly regarding patient outcomes. The speaker emphasizes their role as a hardcore modeler and introduces the idea that novel analytical approaches pave the way for predicting clinical outcomes on a patient-specific basis. The ultimate objective proposed is the translation of preclinical modeling tools into effective planning tools for health professionals.



However, the speaker acknowledges several hurdles that exist, particularly the need to establish the credibility of these models in predicting actual clinical outcomes. They stress the importance of moving from in silico (computer simulations) to in vivo (live subjects) validations and highlight the difficulty of measuring clinically relevant metrics, such as deformations and stresses, in real-world scenarios.



Throughout the discussion, the speaker makes a case for reliable and quantitative metrics, mentioning patient-reported outcomes (PROMs) and suggesting that more objective measures such as kinematics and bone density changes due to implanting devices are more appropriate for validation studies. They detail methods to gather data using motion capture labs and Dual-energy X-ray Absorptiometry (DXA) to assess implant kinematics and bone density alterations successfully.



The speaker also refers to existing literature, revealing that while some predictability of clinical outcomes has been attempted in areas such as fracture risk assessment through finite element models, orthopedic fields still have significant gaps in their predictive capabilities, especially related to arthroplasty outcomes.



In concluding, the speaker defiantly calls for improved methodologies within their practice, equipped with AI advancements while addressing the stagnation in proving the clinical relevance of their models. They urge attendees to take these challenges seriously in aiming for meaningful impacts in clinical predictive modeling.

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