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AI-driven software is 96% accurate at diagnosing Parkinson’s

Existing research indicates that the accuracy of a Parkinson’s disease diagnosis hovers between 55% and 78% in the first five years of assessment. That’s partly because Parkinson’s sibling movement disorders share similarities, sometimes making a definitive diagnosis initially difficult.

Although Parkinson’s disease is a well-recognized illness, the term can refer to a variety of conditions, ranging from idiopathic Parkinson’s, the most common type, to other like multiple system atrophy, a Parkinsonian variant; and progressive supranuclear palsy. Each shares motor and nonmotor features, like changes in gait, but possesses a distinct pathology and prognosis.

Roughly one in four patients, or even one in two patients, is misdiagnosed.

AI-driven MRI analysis improves accuracy in distinguishing Parkinsonian disorders

University of Florida researchers have led a multicenter study demonstrating that Automated Imaging Differentiation for Parkinsonism (AIDP), a machine-learning method using magnetic resonance imaging (MRI), accurately distinguishes Parkinson’s disease (PD) from atypical parkinsonian disorders. Findings suggest this approach could significantly improve diagnostic precision and clinical care.

Liquid Cooling, Meeting The Demands Of AI Data Centers

Adopting liquid cooling technology could significantly reduce electricity costs across the data center.

Many Porsche “purists” reflect forlornly upon the 1997, 5th generation, 996 version of the iconic 911 sports car. It was the first year of the water-cooled engine versions of the 911, which had previously been based on air-cooled engines since their entry into the market in 1964. The 911 was also the successor to the popular air-cooled 356. For over three decades, Porsche’s flagship 911 was built around an air-cooled engine. The two main reasons often provided for the shift away from air-cooled to water-cooled engines were 1) environmental (emission standards) and 2) performance (in part cylinder head cooling). The writing was on the wall: If Porsche was going to remain competitive in the sports car market and racing world, the move to water-cooled engines was unavoidable.

Fast forward to current data centers trying to meet the demands for AI computing. For similar reasons, we’re seeing a shift towards liquid cooling. Machines relying on something other than air for cooling date back at least to the Cray-1 supercomputer which used a freon-based system and the Cray-2 which used Fluorinert, a non-conductive liquid in which boards were immersed. The Cray-1 was rated at about 115kW and the Cray-2 at 195kW, both a far cry from the 10’s of MWs used by today’s most powerful supercomputers. Another distinguishing feature here is that these are “supercomputers” and not just data center servers. Data centers have largely run on air-cooled processors, but with the incredible demand for computing created by the explosive increase in AI applications, data centers are being called on to provide supercomputing-like capabilities.

Mapping minerals remotely: how hyperspectral imaging can support exploration

Discovering new deposits of critical and rare earth minerals is paramount to delivering global net-zero ambitions. However, finding new ore bodies is becoming more challenging due to increasing costs and geopolitical tensions. What is more, much of the low-hanging fruit, so to speak, has already been exploited.

Could technological advances help broaden the search and speed up the process? Dr Bryony Richards, a senior research scientist with the Energy & Geoscience Institute at the University of Utah in the US, believes so.

Richards and her colleagues are incorporating NASA’s and Japan’s global Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery with that of new satellite data, advances in computing power and AI. With this approach, they are developing a comprehensive first-of-a-kind method to uncover the ‘fingerprints’ of mineral deposits that could eventually provide a more cost and time-effective way of mapping minerals in remote areas.


Researchers in Utah are combining satellites, hyperspectral imaging and AI to discover mineral deposits in remote locations.

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