Medical imaging technologies have undergone extensive development, enabling non-invasive visualization of clinical information. The traditional review of medical images by clinicians remains subjective, time-consuming, and prone to human error. With the recent availability of medical imaging data, quantification have become important goals in the field. Radiomics, a methodology aimed at extracting quantitative information from imaging data, has emerged as a promising approach to uncover hidden biological information and support decision-making in clinical practice. This paper presents a review of the radiomic pipeline from the clinical neuroimaging perspective, providing a detailed overview of each step with practical advice. It discusses the application of handcrafted and deep radiomics in neuroimaging, stratified by neurological diagnosis. Although radiomics shows great potential for increasing diagnostic precision and improving treatment quality in neurology, several limitations hinder its clinical implementation. Addressing these challenges requires collaborative efforts, advancements in image harmonization methods, and the establishment of reproducible and standardized pipelines with transparent reporting. By overcoming these obstacles, radiomics can significantly impact clinical neurology and enhance patient care.

Background and objectives: Patients suffering from neurological diseases may develop dysarthria, a motor speech disorder affecting the execution of speech. Close and quantitative monitoring of dysarthria evolution is crucial for enabling clinicians to promptly implement patient management strategies and maximizing effectiveness and efficiency of communication functions in term of restoring, compensating or adjusting. In the clinical assessment of orofacial structures and functions, at rest condition or during speech and non-speech movements, a qualitative evaluation is usually performed, throughout visual observation. Methods: To overcome limitations posed by qualitative assessments, this work presents a store-and-forward self-service telemonitoring system that integrates, within its cloud architecture, a convolutional neural network (CNN) for analyzing video recordings acquired by individuals with dysarthria. This architecture, called facial landmark Mask RCNN, aims at locating facial landmarks as a prior for assessing the orofacial functions related to speech and examining dysarthria evolution in neurological diseases. Results: When tested on the Toronto NeuroFace dataset, a publicly available annotated dataset of video recordings from patients with amyotrophic lateral sclerosis (ALS) and stroke, the proposed CNN achieved a normalized mean error equal to 1.79 on localizing the facial landmarks. We also tested our system in a real-life scenario on 11 bulbar-onset ALS subjects, obtaining promising outcomes in terms of facial landmark position estimation. Discussion and conclusions: This preliminary study represents a relevant step towards the use of remote tools to support clinicians in monitoring the evolution of dysarthria.

Neurological disorders are some of the leading sources of disability globally and Parkinson's disease is the fastest-growing neurological disease in the world. Parkinson's is difficult to diagnose as diagnosis primarily relies on the appearance of symptoms like tremors and slowness but these symptoms usually appear several years after the onset of the disease. The model also estimated the severity and progression of Parkinson's, in accordance with the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS), which is the standard rating scale used clinically. The research findings have been published in the journal Nature Medicine. The researchers trained the model by using nocturnal breathing data (data collected while subjects were asleep) from various hospitals in the US and some public datasets.

Intellectual and Developmental Disabilities (IDDs), such as Down syndrome, Fragile X syndrome, Rett syndrome, and autism spectrum disorder, usually manifest at birth or early childhood. IDDs are characterized by significant impairment in intellectual and adaptive functioning, and both genetic and environmental factors underpin IDD biology. Molecular and genetic stratification of IDDs remain challenging mainly due to overlapping factors and comorbidity. Advances in high throughput sequencing, imaging, and tools to record behavioral data at scale have greatly enhanced our understanding of the molecular, cellular, structural, and environmental basis of some IDDs. Fueled by the “big data” revolution, artificial intelligence (AI) and machine learning (ML) technologies have brought a whole new paradigm shift in computational biology. Evidently, the ML-driven approach to clinical diagnoses has the potential to augment classical methods that use symptoms and external observations, hoping to push the personalized treatment plan forward. Therefore, integrative analyses and applications of ML technology have a direct bearing on discoveries in IDDs. The application of ML to IDDs can potentially improve screening and early diagnosis, advance our understanding of the complexity of comorbidity, and accelerate the identification of biomarkers for clinical research and drug development. For more than five decades, the IDDRC network has supported a nexus of investigators at centers across the USA, all striving to understand the interplay between various factors underlying IDDs. In this review, we introduced fast-increasing multi-modal data types, highlighted example studies that employed ML technologies to illuminate factors and biological mechanisms underlying IDDs, as well as recent advances in ML technologies and their applications to IDDs and other neurological diseases. We discussed various molecular, clinical, and environmental data collection modes, including genetic, imaging, phenotypical, and behavioral data types, along with multiple repositories that store and share such data. Furthermore, we outlined some fundamental concepts of machine learning algorithms and presented our opinion on specific gaps that will need to be filled to accomplish, for example, reliable implementation of ML-based diagnosis technology in IDD clinics. We anticipate that this review will guide researchers to formulate AI and ML-based approaches to investigate IDDs and related conditions.

Iktos is working with Astrogen to use artificial intelligence (AI) to identify small molecules as candidates for the treatment of Parkinson's disease. Under the collaboration's terms, Iktos will apply its proprietary machine-learning algorithm to virtually "sketch"molecules directed against a defined target and shortlist candidates for preclinical studies, the companies reported in a press release. That target is not yet public. In turn, Astrogen will screen the candidates in the lab and inside living organisms, and it will guide the development process from preclinical to clinical stages. The companies will work together in the production and selection of molecules that show the most promise as a Parkinson's treatment.

Neurological diseases, such as cerebrovascular disease, Parkinson's disease (PD), Alzheimer's disease, have become the leading cause of death in China. Neurological function evaluation is crucial for the diagnosis and intervention of neurological diseases. Clinically, neurological function is evaluated by various scales, tests, and questionnaires. However, these methods rely on costly professional equipment and medical personnel. They cannot be used as a means of daily evaluation of neurological diseases.

During the spring, a troublesome pattern plays out as marine birds along the California coast die from domoic acid poisoning, which is caused by harmful algal blooms. An early clue indicates when and where this problem starts spreading: rescued California brown pelicans, red-throated loons, and other species start turning up at wildlife rehabilitation centers with signs of neurological disease. Yet, though they pepper the state map, these centers are not interconnected enough to nip the issue in the bud. When staffers at one center diagnose a sick bird, others another 40 miles up the road might not be privy to that information. So researchers at UC Davis recently tested an early detection system that uses artificial intelligence to classify admissions to rehabilitation centers, in the hope of sending wildlife agencies and researchers warnings about growing problems among marine birds and many other kinds of animals.

Artificial intelligence is everywhere, and now a group of developers have created AI software that can tell whether you are likely to die from Covid-19 using health data. University of Copenhagen researchers fed a computer program with health data from 3,944 Danish COVID-19 patients, as well as any underlying conditions. They then trained it to look for patterns in a patients' prior illness to determine the risk factors and potential outcome from Covid-19 and found that BMI, age and being male were the highest risk factors when it came to the likelihood of dying. The results show that AI can, with up to 90 per cent certainty, determine whether an uninfected person will die of the disease if they are unlucky enough to catch it. Results from the new tool could help health officials determine who should be at the front of the line for a limited supply of vaccines, said lead author Mads Nielsen. They say this should be considered when determining who should get the vaccine first.

The below blog is a transcript of James Cai, Head of Data Science at Roche Innovation Centre, presenting on the application of AI in the clinical development of drugs, a topic which is extremely prevalent in the current environment. AI is transforming many industries including healthcare and pharma. Where are the opportunities for AI in the early clinical development of new drugs, where scientific hypotheses first meet real patients in clinical trials? Can AI generate new insights to inform translational research or improve the efficiency of clinical trials? In this talk, I will highlight opportunities created by big data and AI, e.g., digital biomarkers for neurological diseases, and share my thoughts on what it will take to operationalize AI in drug development.

Neurological conditions such as Parkinson's and Alzheimer's could be diagnosed from simple eye scans performed by high street opticians thanks to a new NHS artificial intelligence (AI) project. Newcastle University is working on the project with medics at North East hospitals as part of a national £50 million boost to use AI in a range of health schemes. Early diagnosis in progressive neurological diseases such as Parkinson's and Alzheimer's, which affect more than one million people in the UK, is important, so speeding up the process could be crucial. Anya Hurlbert, professor of visual neuroscience at Newcastle University, is leading the Octahedron project. She said: "The retina at the back of the eye is basically an outpost of the brain and the only part of the central nervous system we can see directly from the outside. "We know that in Alzheimer's disease and Parkinson's disease the retina is affected." Very detailed images of the retina can be captured by optical coherence tomography, or OCT scanning, which is quick and cheap and increasingly available at high street opticians. Further analysis of these scans will now be developed with the use of AI, to recognise signs of neurological disease. Prof Hurlbert said: "The aim of the project is to use NHS data to teach computers how to detect early signs of neurological disease via retinal imaging.