RESUMO
Background: Osteoarthritis (OA) of the knee, in most instances primarily, affects medial compartment of knee. Combining Osteochondral Autologous Transfer System (OATS) with Medial Open-Wedge High Tibial Osteotomy (MOWHTO) may represent an integrated approach to sustaining long-term knee functionality in OA patients. Materials and methods: From 2009 to 2016, combined OATS and MOWHTO was performed in 66 knees of 63 patients with medial compartment knee OA. Cartilage regeneration was assessed by 2nd look arthroscopy and Knee function was assessed by knee society scoring (KSS) pre-operatively and post-operatively. The survival rate of MOWHTO plus OATS was assessed. Failure is characterized by the need to convert into total knee replacement. Results: The KSS knee score (from 48.3 to 90.4) and function score (from 42.6 to 88.7) showed a statistically significant improvement (p-value of <0.0001) at a mean follow-up period of 9.49 years. Second look arthroscopy done at the time of implant removal showed 100 % cartilage regeneration with even hyaline cartilage regeneration in 49 out of 57 knees assessed and partial regeneration in 8 knees. The Kaplan Meier survivorship analysis was 96.7 % at the mean 9.49 years after surgery. Only 2 patients needed TKA conversion in follow-up. Conclusion: Combining OATs and valgus MOWHTO provides good option to successfully manage patients of OA and varus malalignment. This resulted in significant improvement in knee function, lowering pain intensity, good cartilage regeneration, and a high survivorship rate for 10 years postoperatively.
RESUMO
The use of artificial intelligence (AI) in a variety of research fields is speeding up multiple digital revolutions, from shifting paradigms in healthcare, precision medicine and wearable sensing, to public services and education offered to the masses around the world, to future cities made optimally efficient by autonomous driving. When a revolution happens, the consequences are not obvious straight away, and to date, there is no uniformly adapted framework to guide AI research to ensure a sustainable societal transition. To answer this need, here we analyze three key challenges to interdisciplinary AI research, and deliver three broad conclusions: 1) future development of AI should not only impact other scientific domains but should also take inspiration and benefit from other fields of science, 2) AI research must be accompanied by decision explainability, dataset bias transparency as well as development of evaluation methodologies and creation of regulatory agencies to ensure responsibility, and 3) AI education should receive more attention, efforts and innovation from the educational and scientific communities. Our analysis is of interest not only to AI practitioners but also to other researchers and the general public as it offers ways to guide the emerging collaborations and interactions toward the most fruitful outcomes.
