Washington [US], December 17 (ANI): Researchers from the University of Jyvaskyla and the Central Finland Health Care District have developed an AI based neural network to detect an early knee osteoarthritis from x-ray images. AI was able to match a doctors' diagnosis in 87 per cent of cases. The result is important because x-rays are the primary diagnostic method for early knee osteoarthritis. An early diagnosis can save the patient from unnecessary examinations, treatments and even knee joint replacement surgery. Osteoarthritis is the most common joint-related ailment globally.

A study conducted at the University of Jyväskylä Faculty of Information Technology's Digital Health Intelligence Laboratory used machine learning to predict anterior cruciate ligament injuries. The largest data set collected for this purpose was used, but the results show that even machine learning cannot develop a sufficiently effective model to predict injuries in individual athletes. Anterior cruciate ligament (ACL) injuries are common in team sports and cutting sports. Preventing them is important for both elite and amateur athletes. Multiple injury risk factors have been recognized in previous research, but the actual prediction of ACL injuries is still a matter of controversy.

Researchers at the Nanoscience Center and Faculty of Information Technology in the University of Jyväskylä, Finland, have achieved a significant step forward in predicting atomic structures of hybrid nanoparticles. A research article published in Nature Communications on 3 September 2019, demonstrates a new algorithm that learns to predict binding sites of molecules at the metal-molecule interface of hybrid nanoparticles by using already published experimental structural information on nanoparticle reference systems. The algorithm can in principle be applied to any nanometer-size structure consisting of metals and molecules provided that some structural information already exists on the corresponding systems. The research was funded by the AIPSE research program of the Academy of Finland (Novel Applications of Artificial Intelligence in Physical Sciences and Engineering Research). Nanometre-sized hybrid metal nanoparticles have many applications in different processes, including catalysis, nanoelectronics, nanomedicine and biological imaging.

Researchers have developed an AI-based computing model which can count cells from histopathological cancer tumour images. A team from the University of Jyväskylä state that they have taken the first step towards developing a digital service centre based on artificial intelligence where doctors and pathologists analyse tumour tissue samples visually with the help of software. The computing model is able to determine the T-cell count in cancer tissue based on nothing but a digital image and with an error margin of a few percent, according to tests. The researchers tested the model against five 523 images of intestinal cancer tumours where previously, the T-cell count of each image was determined by histopathologists. The team found that the model was successful in 90% of cases.

This is the MATLAB code for the K-RVEA algorithm published in the following article: T. Chugh, Y. Jin, K. Miettinen, J. Hakanen, and K. Sindhya, A surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive many-objective optimization, IEEE Transactions on Evolutionary Computation, vol. More details about it can be found in the thesis: T. Chugh. Please read the licence file before using the code and cite the article and the thesis if you use the code.