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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Evaluations of artificial intelligence and machine learning algorithms in neurodiagnostics.

Kristin S Williams¹

¹Columbia University, New York, New York, United States.

Journal of Neurophysiology

|March 27, 2024

Summary

Artificial intelligence (AI) in neurology aids diagnostics for brain lesions and stroke. Ethical evaluation is crucial, as AI model performance impacts patient outcomes and requires careful hyperparameter tuning.

Keywords:

AI Bayesian artificial network analysis neurodiagnostics neurology

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Area of Science:

Neurology
Artificial Intelligence
Medical Ethics

Background:

Artificial intelligence (AI) and machine learning (ML) are increasingly used in neurological diagnostics.
AI applications include brain lesion detection, seizure focus localization, and ischemic stroke characterization.
Ethical considerations surrounding AI in clinical practice require thorough examination.

Purpose of the Study:

To evaluate the ethical implications of using AI algorithms in neurological diagnostic examinations.
To analyze the clinical utility and potential risks of AI/ML models in neurology.
To explore the mathematical underpinnings and performance dependencies of AI algorithms.

Main Methods:

Analysis of supervised, unsupervised, artificial neural network (ANN), and deep neural network (DNN) learning models.
Evaluation of ANN and DNN algorithms using Bayesian statistical analyses.
Mathematical exploration of AI algorithm configurations, hyperparameters, and neural inputs.

Main Results:

AI/ML models show potential in aiding neurological diagnoses.
Predictive accuracy and model performance are highly dependent on hyperparameter and neural input configurations.
Underperformance can lead to misdiagnosis and adverse patient outcomes.

Conclusions:

Ethical use of AI in neurology necessitates a comprehensive understanding of its mathematical basis and performance limitations.
Careful configuration and validation of AI models are essential to mitigate risks associated with misdiagnosis.
Further research into the ethical and clinical utility of AI in neurological diagnostics is warranted.