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On November 16, 2018, TrojanAndy commented on Making the Leap: Can Machine Learning Take Root in Animal Healthcare? :

Good question! So the benefits of in-house diagnostics are that results can be determined while the patient is still at the clinic and thus the veterinarian can make a diagnosis and start a treatment plan on the same visit, which is incredibly valuable from a clinical and financial perspective given that follow-up appointment attendance is way lower than in human healthcare. SediVue Dx also offers an additional benefit in that in-house diagnostics have much lower rates of inconclusive tests than if the specimen is sent to a reference lab – urine samples can decay in transit causing a number of reference lab results to be invalidated.

On November 15, 2018, TrojanAndy commented on Open Innovation in the NFL: Player Safety :

This is an important article given the recent findings about the prevalence of various types of brain injuries being suffered by football players at all levels of the game. I agree with Alex that upping the research funding from a measly $1.3mm is critical in the immediate run. I would even posit that the NFL should host a moonshot, i.e. offer a $50mm prize for the entity that discovers a helmet that reduces concussion rates by 50% or a treatment protocol that can halt or even reverse the effects of brain injuries. Without drastic action across all time horizons, the NFL is going to face an existential crisis as more and more parents prohibit children from playing the sport because of real health concerns.


Really interesting to think of machine learning tied to civil aviation and its application to address the impeding gap in the supply of pilots. Given the extent of the gap, it seems like the entire air travel ecosystem (not just Boeing, but Airbus, the airlines, governments, etc.) needs to collaborate to think about how to address this problem and consider what needs to be done for autonomous planes to become acceptable to all stakeholders. It will be fascinating to watch how this plays out and I’m interested in whether military aviation starts moving towards autonomous planes for transporting troops and materiel.

Awesome article on machine learning applied to football — I think you raise great questions about how this data can be translated into improved player development and performance across the different teams for Benefica. I’d be really interested in learning how the insights from the data can also help the other core element of the player development process – scouting and choosing the right youth players to enter the academy.

On November 15, 2018, TrojanAndy commented on Your Personalized Dinner is Served :

Great read! Wellio’s approach to use machine learning to take into account existing inventory of food supplies is an innovative twist that might differentiate itself from the numerous other players in this sector. As mentioned above, the fact that Wellio isn’t requiring subscriptions can be a double-edged sword – possibly can help with acquiring clients but there could be adverse cash flow impacts that could make continued investments in data science more tenuous.

Allison – fascinating to see UA making the huge investments in R&D to take its products to the next level via additive manufacturing. As you touched on in the article and Stefan added to in his comment, the production time per unit seems prohibitive for the shoe being able to reach the mass market and improvements to production time will likely need to improve by a massive factor (not just incrementally). Given its recent financial issues, it will be interesting to see whether UA will view future products and associated investments related to additive manufacturing as critical to its product pipeline or something that might need to be curtailed.

Dr. Jordan — this article is outstanding and I’m incredibly hopeful about the potential for bio-printing (especially for cartilage) to address limitations currently facing the field – i.e., arthritis. Given that the top-end orthopedic surgeons that I know or have been seen by carry tremendous case loads, I’d be curious to get your thoughts on what you think the uptake would be with respect to using 3-D printed models for pre-surgery planning? Additionally, are the imaging methodologies precise enough for 3-D printing to generate models with sufficient accuracy?