Elon Musk announced his Neuralink is hosting a'show and tell' progress event on October 31, which will be the first progress update since the world watched a brain-chipped monkey play a video game with its mind in April 2021 - this animal later died during testing. The biotech firm is developing a brain-computer interface that it claims could one day make humans hyper-intelligent, and allow paralyzed people to walk again. Musk shared news of the Halloween event on Twitter, no other details were included, but it follows rumors that Neuralink has offered to buy its rival Synchron, which recently completed the first brain-chip in a human. In April Musk shared that Neuralink was moving along to start human trials at the end of 2022, which could very well be what the billionaire has in store at the October presentation. Neuralink showed its first progress update in August 2020 during a demonstration that showcased a pig with an early version of the brain chip.

Monkeys being tested on by Elon Musk-owned brain chip firm Neuralink were allegedly subject to'torture', an animal rights group claims. The biotech firm is developing a brain-computer interface, that it claims could one day make humans hyper-intelligent, and allow paralysed people to walk again. However, the Physicians Committee for Responsible Medicine (PCRM) alleges that between 2017 and 2020, test monkeys owned by Neuralink were subject to experiments that amounted to torture, with evidence of rashes, self-mutilation and brain hemorrhages in documentation seen by the group. The experiments were a partnership between University of California Davis, and Neuralink, with a reported 23 monkeys involved in the experiment, 15 of which died or were euthanized as a result of complications, or'inadequate animal care'. PCRM lodged a complaint with the US Department of Agriculture on Thursday against UC Davis, claiming the primates faced'extreme suffering as a result of inadequate animal care and the highly invasive experimental head implants during the experiments.'

Research across disciplinary boundaries: the Institute for Applied Mathematics at the University of Bonn and the Clinic for Neuroradiology at the University Hospital Bonn (UKB) have received funding of around 160,000 euros for a joint project on the automated detection of brain hemorrhages using artificial intelligence. The funding is provided by the Hausdorff Center for Mathematics (HCM) Cluster of Excellence at the University of Bonn. Cerebral hemorrhages are among the clinical emergencies in which rapid intervention is essential for the further course of the disease. In this context, radiology plays a central role, because only the reliable diagnosis of brain hemorrhage by means of CT (computed tomography) enables the correct classification of the hemorrhage and the initiation of further therapeutic steps. In order to be able to automatically detect brain hemorrhages in the future using artificial intelligence, mathematicians and physicians are working closely together in their project.

Brain hemorrhage is a sudden and very dangerous disorder. For example, subarachnoid hemorrhage, which is most common within the middle-aged, kills 75 percent of the patients within a year, if left unnoticed. "Brain hemorrhages are difficult to detect, because the symptoms can include only a headache," says Taru Hermens, project manager at the Helsinki University Hospital (HUS). Diagnosing brain hemorrhages is done by radiologists who interpret the results of head imaging scans. In the Finnish hospitals, six million medical imaging examinations are performed every year, and about 180,000 of those are computerised tomography (CT) scans of the head.

Deep learning and its applications have grown in recent years. Recently, researchers from ETH Zurich used the technique to study dark matter in an industry first. Now, a team working with the University of California, Berkeley and the University of California, San Francisco (UCSF) School of Medicine have trained a convolutional neural network dubbed "PatchFCN" that detects brain hemorrhages with remarkable accuracy. In a paper titled "Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning", the team claims that: We used a single-stage, end-to-end, fully convolutional neural network to achieve accuracy levels comparable to that of highly trained radiologists, including both identification and localization of abnormalities that are missed by radiologists. The team achieved an accuracy of 99 percent, which is the highest recorded accuracy to date for detecting brain hemorrhages. Our algorithm demonstrated the highest accuracy to date for this clinical application, with a receiver operating characteristic (ROC) area under the curve (AUC) of 0.991 0.006 for identification of examinations positive for acute intracranial hemorrhage, and also exceeded the performance of 2 of 4 radiologists.

AI is already capable of discovering medical conditions with a high degree of accuracy. However, brain hemorrhages are particularly challenging -- false positives slow things down, while missing even a tiny hemorrhage could be deadly. The technology might be ready for it, however. UC Berkeley and UCSF researchers have created an algorithm that detected brain hemorrhages with accuracy better than two out of four radiologists in a test. The key was the algorithm's finely-detailed training data.