Machine Learning Meets Brain Imaging:
a New Era in Neuroscience

The intersection of brain imaging with machine learning (ML) is a major turning point in neuroscience. It is contributing to a revolution in comprehension and analysis of the human brain’s workings.

Machine learning algorithms are adept at processing and deciphering vast quantities of data obtained from brain imaging techniques such as CT, fMRI, and MRI. This marks a significant milestone in history, facilitating a deeper comprehension of the brain’s anatomy, physiology, and pathophysiology.

In recent days, the revolutionary power of machine learning has been found to decipher intricate patterns in brain imaging data. Agrawal et al.’s work “Automated intracranial hemorrhage detection in traumatic brain injury using 3D CNN” exemplifies the development of a neurotrauma screening tool. This tool can identify ICH from head CT scans of TBI patients. Additionally, neurology specialists and brain diagnostics teams are using ML algorithms in neuroimaging to improve diagnosis & treatment. This is demonstrated in the research “Quantifying performance of machine learning methods for neuroimaging data,” explained by Jollans et al. in 2019.

The multidisciplinary approach of using AI techniques for neuroimaging speeds up data analysis. Additionally, they enable medical professionals and researchers to draw important conclusions from sizable and intricate datasets. Machine learning (ML) improves efficiency in brain imaging by automating the extraction of pertinent information. It also creates new opportunities for focused therapies and customized medicine in neurological illnesses.

As we delve deeper into the intersection, where machine learning algorithms proficiently handle and interpret large amounts of data from brain imaging techniques such as CT, fMRI, and MRI, it becomes more clear about the possibility of making ground-breaking discoveries in neuroscience. This advancement is expected to significantly influence our understanding of the complex functions of the human brain.

The Development of Neuroscience: A Look Back

Source: Dipietro et al. 2023

Neuroscience is a multidisciplinary field that is devoted to the study of the nervous system. This nervous system is the combination of peripheral and central nerve systems. One of neuroscience’s primary goals is understanding the mechanisms governing and controlling nerve responses and brain function.

Neurosciences And The Brain

Researchers in neurosciences are now studying the brain as a control system. Its development started during the Greek era and is still ongoing today. The brain regulates the intricate network of nerves that make up the human body. Millions of neurons fire constantly throughout the day, sending precise information-carrying impulses to every organ in the body. The relationship between neuroscience and the brain is intricate and multifaceted.

Firstly, neuroscience aims to unravel the mysteries of the brain by investigating its structure, function, and development. Through techniques like neuroimaging, electrophysiology, and molecular biology, neuroscientists explore how neurons communicate with each other, form circuits, and process information.

Secondly, neuroscience informs our understanding of brain-related disorders and diseases. By studying the neural mechanisms underlying conditions like Alzheimer’s, Parkinson’s, and schizophrenia, researchers can develop treatments and interventions to improve patients’ lives.

Bridging the Gap: How AI Enhances Neuroscientific Research

Combining AI and neuroscience can offer much to the healthcare industry. Due to their complexity, neurological illnesses are often difficult to diagnose. This is when AI’s analytical strength comes through.

Repetitive strain injuries (Amendolara et al. 2023) provide a significant challenge to athletics, which has historically depended on past data and human experience for prevention. Current methodologies have not developed higher precision preventative strategies at a painfully sluggish pace.

Technological breakthroughs have made artificial intelligence (AI) and machine learning (ML) attractive toolkits to improve injury mitigation and rehabilitation methods. Sports medicine has been among the many sectors where machine learning (ML) has found application and implementation as computational resources have increased.

Use of AI

AI in healthcare market size worldwide 2021-2030

Source: from Article by Conor Stewart published in Statista on Sep 28, 2023

The global market for artificial intelligence (AI) in healthcare was estimated to be valued at approximately 11 billion dollars in 2021. According to projections, the value of the worldwide healthcare artificial intelligence industry was expected to reach almost 188 billion dollars by 2030, growing at a compound annual growth rate of 37% between 2022 and 2030.

AI applications in healthcare

About one-fifth of the global healthcare firms polled in 2021 said they were still in the early stages of implementing AI models. This indicated that the models had been produced for a short period of less than two years. Less than 10% of healthcare businesses have used artificial intelligence for over five years. The most commonly used features of AI software in the healthcare industry are Natural language processing (NLP) and healthcare data integration. Clinicians and providers were the primary intended users of AI at the mature adoption stage. However, 60% of respondents said patients should be able to use the implemented AI tools.

AI saves time

It was estimated that a doctor’s working hours in Europe were split roughly 50/50 between patient care and office duties. However, because administrative work would take up less time, it was predicted that doctors would be able to devote about 20% more of their time to patients as AI became more widely used in healthcare. Additionally, it was predicted that decreased time spent on administrative and regulatory tasks would allow nurses to spend about 8% more time with patients.

Introduction of AI-Powered Tool for Athletes

As a leader in technical innovation, VivoShield Sports is bringing in a new age for sports medicine practitioners and athletes. This AI-driven platform offers a comprehensive sports medicine precision analysis solution by seamlessly integrating data from multiple imaging modalities. Today, neurologists and neurosurgeons can control, alter, and analyze patient data using multi-modal cloud technologies, opening the door to diagnostic and preventative procedures.

The platform’s unique features, such as individualized body composition evaluations and early warning sign detection, are customized for each sport. This ensures athletes gain tailored insights into their body structure and function via a computerized physical examination. VivoShield Sports uses cutting-edge medical imaging and artificial intelligence to give athletes a unique fingerprint of their entire health, enabling more educated training, medicine, and lifestyle decisions.

Challenges and Opportunities

Recognizing the benefits and challenges of incorporating AI into sports medicine is crucial, even as we embrace VivoShield Sports’ potential to transform athlete wellness. Making sure the AI algorithms are accurate and dependable, particularly when working with complex data points from several imaging modalities, is difficult. Constant improvement and validation are essential to foster confidence between athletes and medical experts,

Preventive care and performance optimization are fields with lots of opportunities. Sports teams and doctors can all work together to improve athlete’s general health and performance.

Furthermore, VivoShield Sports’ AI-driven insights provide a distinct edge in customized training, injury prevention, and promoting ideal physiological functioning.

Final note,

The convergence of brain imaging and machine learning has reached a critical juncture in neuroscience. Developments such as the neurotrauma screening tool and improved diagnosis techniques highlight the significant influence of machine learning on neuroimaging. The combination of AI and neuroscience is revolutionizing healthcare beyond diagnostics by providing individualized therapies and streamlining workflows. The application of AI to sports medicine, as demonstrated by VivoShield Sports, highlights the technology’s significance by giving athletes individualized information. Even while there are still obstacles to overcome, the changing environment offers enormous potential for advances in healthcare and a deeper knowledge of the complex functions of the human brain.

References:

Agrawal, D., Poonamallee, L., Joshi, S., & Bahel, V. (2023). Automated intracranial hemorrhage detection in traumatic brain injury using 3D CNN. Journal of Neurosciences in Rural Practice, 14(4), 615.

Jollans, L., Boyle, R., Artiges, E., Banaschewski, T., Desrivières, S., Grigis, A., … & Whelan, R. (2019). Quantifying performance of machine learning methods for neuroimaging data. NeuroImage, 199, 351-365.

Amendolara, A., Pfister, D., Settelmayer, M., Shah, M., Wu, V., Donnelly, S., … & Bills, K. (2023). An overview of machine learning applications in sports injury prediction. Cureus, 15(9).

Dipietro, L., Gonzalez-Mego, P., Ramos-Estebanez, C., Zukowski, L. H., Mikkilineni, R., Rushmore, R. J., & Wagner, T. (2023). The evolution of Big Data in neuroscience and neurology. Journal of Big Data, 10(1), 116.

Machine Learning Meets Brain Imaging:a New Era in Neuroscience