Machine Learning Utility for Optical Coherence Tomography in Multiple Sclerosis: Is the Future Now?

[Retinal OCT biomarkers and neurodegenerative diseases of the central nervous system beyond Alzheimer's disease]

BACKGROUND

Optical coherence tomography (OCT) biomarkers are increasingly used by neurologists, psychiatrists, and ophthalmologists for the diagnosis, prognosis, and follow-up of neurodegenerative diseases. Long-term data on OCT biomarkers of selected primary and secondary neurodegenerative diseases of the central nervous system (CNS), such as multiple sclerosis (MS) or Parkinson's disease, are already available in part. In addition, there are rare neurodegenerative diseases with early disease onset that may show OCT abnormalities.

METHODS

A literature review on the association of OCT biomarkers with neurodegenerative diseases of the CNS beyond Alzheimer's disease is presented. Parkinson's disease, MS, Friedreich's ataxia, Huntington's disease, spinocerebellar ataxia, and lysosomal storage diseases are addressed.

RESULTS

Relevant OCT biomarkers of neurodegenerative diseases are the macular ganglion cell inner plexiform layer (GCIPL) and the peripapillary retinal nerve fiber layer (pRNFL) thickness. Different sectors may be affected depending on the disease entity in addition to global pRFNL reduction. OCT‑angiography (OCT-A) is also increasingly used as a biomarker in neurodegenerative diseases.

CONCLUSION

Optical coherence tomography biomarkers are used in an interdisciplinary context. Retinal pathologies should be excluded by an ophthalmologist. While OCT biomarkers are increasingly used clinically in MS, the benefit in other neurodegenerative diseases, especially the rare ones, is less well documented. Further longitudinal studies are required.

Artificial Intelligence and Multiple Sclerosis: Up-to-Date Review

Multiple sclerosis (MS) remains a challenging neurological disorder for the clinician in terms of diagnosis and management. The growing integration of AI-based algorithms in healthcare offers a golden opportunity for clinicians and patients with MS. AI models are based on statistical analyses of large quantities of data from patients including "demographics, genetics, clinical and radiological presentation." These approaches are promising in the quest for greater diagnostic accuracy, tailored management plans, and better prognostication of disease. The use of AI in multiple sclerosis represents a paradigm shift in disease management. With ongoing advancements in AI technologies and the increasing availability of large-scale datasets, the potential for further innovation is immense. As AI continues to evolve, its integration into clinical practice will play a vital role in improving diagnostics, optimizing treatment strategies, and enhancing patient outcomes for MS. This review is about conducting a literature review to identify relevant studies on AI applications in MS. Only peer-reviewed studies published in the last four years have been selected. Data related to AI techniques, advancements, and implications are extracted. Through data analysis, key themes and tendencies are identified. The review presents a cohesive synthesis of the current state of AI and MS, highlighting potential implications and new advancements.

Explainable artificial intelligence toward usable and trustworthy computer-aided diagnosis of multiple sclerosis from Optical Coherence Tomography

BACKGROUND

Several studies indicate that the anterior visual pathway provides information about the dynamics of axonal degeneration in Multiple Sclerosis (MS). Current research in the field is focused on the quest for the most discriminative features among patients and controls and the development of machine learning models that yield computer-aided solutions widely usable in clinical practice. However, most studies are conducted with small samples and the models are used as black boxes. Clinicians should not trust machine learning decisions unless they come with comprehensive and easily understandable explanations.

MATERIALS AND METHODS

A total of 216 eyes from 111 healthy controls and 100 eyes from 59 patients with relapsing-remitting MS were enrolled. The feature set was obtained from the thickness of the ganglion cell layer (GCL) and the retinal nerve fiber layer (RNFL). Measurements were acquired by the novel Posterior Pole protocol from Spectralis Optical Coherence Tomography (OCT) device. We compared two black-box methods (gradient boosting and random forests) with a glass-box method (explainable boosting machine). Explainability was studied using SHAP for the black-box methods and the scores of the glass-box method.

RESULTS

The best-performing models were obtained for the GCL layer. Explainability pointed out to the temporal location of the GCL layer that is usually broken or thinning in MS and the relationship between low thickness values and high probability of MS, which is coherent with clinical knowledge.

CONCLUSIONS

The insights on how to use explainability shown in this work represent a first important step toward a trustworthy computer-aided solution for the diagnosis of MS with OCT.