Artificial Intelligence for Multiple Sclerosis Management Using Retinal Images: Pearl, Peaks, and Pitfalls

@article{FarabiMaleki2023ArtificialIF,
  title={Artificial Intelligence for Multiple Sclerosis Management Using Retinal Images: Pearl, Peaks, and Pitfalls},
  author={Shadi Farabi Maleki and Milad Yousefi and Sayeh Afshar and Siamak Pedrammehr and Chee Peng Lim and Ali Jafarizadeh and Houshyar Asadi},
  journal={Seminars in Ophthalmology},
  year={2023},
  volume={39},
  pages={271 - 288},
  url={https://api.semanticscholar.org/CorpusID:266226997}
}
This study reviews the current research studies on the integration of AI, including ML and DL algorithms, with OCT in the context of MS, and examines the advancements, challenges, potential prospects, and ethical concerns of AI-powered techniques in enhancing MS diagnosis, monitoring disease progression, revolutionizing patient care, the development of patient screening tools, and supported clinical decision-making based on OCT images.
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16 Citations
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16 Citations

Deep Learning and The Retina: A New Frontier in Multiple Sclerosis Diagnosis

Artificial intelligence-enhanced retinal imaging is emerging as a powerful, non-invasive tool that can complement traditional neurological assessments and support earlier, more personalized MS care.

Deep learning for multiple sclerosis lesion classification and stratification using MRI

Retinal imaging and Alzheimer's disease: a future powered by Artificial Intelligence.

The growing application of AI in medicine promises its future position in processing different aspects of patients with AD, but there needs cohort studies to determine whether it can help to follow up with healthy persons at risk of AD for a quicker diagnosis or assess the prognosis of patients with AD.

Artificial intelligence in assessing cardiovascular diseases and risk factors via retinal fundus images: A review of the last decade

An overview of the recent developments and difficulties in using AI and retinal imaging to diagnose cardiovascular diseases is provided and provides insights for further exploration in this field.

MRI-based detection of multiple sclerosis using an optimized attention-based deep learning framework.

The 2DRK-MSCAN framework offers a reliable and effective solution for early MS detection using MRI, and shows promising potential for aiding timely intervention and improving patient care.

Current and future roles of artificial intelligence in retinopathy of prematurity

It is concluded that traditional methods for ROP diagnosis suffer from subjectivity and manual analysis, leading to inconsistent clinical decisions, and AI holds great promise for improving ROP management.

Artificial intelligence for early detection of diabetes mellitus complications via retinal imaging

Retinal images can be used to diagnose DM complications including DR, neuropathy, nephropathy, and atherosclerotic cardiovascular disease, as well as to predict the risk of cardiovascular events, and have the potential to become a central tool for modern personalized medicine in patients with DM.

Retinal alterations induced by amyotrophic lateral sclerosis: An analysis using optical coherence tomography

Clinical Applications of Artificial Intelligence in Neurology Practice

Some of the potential applications of artificial intelligence in health care and neurology clinical practice are explored, with a focus on improving diagnostic testing, documentation, and clinical workflows and highlighting opportunities to address long-standing human biases and challenges and potential mitigation strategies.

Artificial Intelligence-Assisted Design of Nanomedicines for Breast Cancer Diagnosis and Therapy: Advances, Challenges, and Future Directions

The study emphasizes the need for multidisciplinary collaboration to eliminate existing barriers and generate advancements to transform breast cancer therapies into more effective, safer, and individualized methods.

94 References

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