The Role of Optical Coherence Tomography Criteria and Machine Learning in Multiple Sclerosis and Optic Neuritis Diagnosis

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

Multiple sclerosis (MS) is a complex autoimmune disease characterized by inflammatory processes, demyelination, neurodegeneration, and axonal damage within the central nervous system (CNS). Retinal imaging, particularly Optical coherence tomography (OCT), has emerged as a crucial tool for investigating MS-related retinal injury. The integration of artificial intelligence(AI) has shown promise in enhancing OCT analysis for MS. Researchers are actively utilizing AI algorithms to accurately detect and classify MS-related abnormalities, leading to improved efficiency in diagnosis, monitoring, and personalized treatment planning. The prognostic value of AI in predicting MS disease progression has garnered substantial attention. Machine learning (ML) and deep learning (DL) algorithms can analyze longitudinal OCT data to forecast the course of the disease, providing critical information for personalized treatment planning and improved patient outcomes. Early detection of high-risk patients allows for targeted interventions to mitigate disability progression effectively. As such, AI-driven approaches yielded remarkable abilities in classifying distinct MS subtypes based on retinal features, aiding in disease characterization and guiding tailored therapeutic strategies. Additionally, these algorithms have enhanced the accuracy and efficiency of OCT image segmentation, streamlined diagnostic processes, and reduced human error. This study reviews the current research studies on the integration of AI,including ML and DL algorithms, with OCT in the context of MS. It 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.

The macular retinal ganglion cell layer as a biomarker for diagnosis and prognosis in multiple sclerosis: A deep learning approach

PURPOSE

The macular ganglion cell layer (mGCL) is a strong potential biomarker of axonal degeneration in multiple sclerosis (MS). For this reason, this study aims to develop a computer-aided method to facilitate diagnosis and prognosis in MS.

METHODS

This paper combines a cross-sectional study of 72 MS patients and 30 healthy control subjects for diagnosis and a 10-year longitudinal study of the same MS patients for the prediction of disability progression, during which the mGCL was measured using optical coherence tomography (OCT). Deep neural networks were used as an automatic classifier.

RESULTS

For MS diagnosis, greatest accuracy (90.3%) was achieved using 17 features as inputs. The neural network architecture comprised the input layer, two hidden layers and the output layer with softmax activation. For the prediction of disability progression 8 years later, accuracy of 81.9% was achieved with a neural network comprising two hidden layers and 400 epochs.

CONCLUSION

We present evidence that by applying deep learning techniques to clinical and mGCL thickness data it is possible to identify MS and predict the course of the disease. This approach potentially constitutes a non-invasive, low-cost, easy-to-implement and effective method.

[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.

Combining retinal structural and vascular measurements improves discriminative power for multiple sclerosis patients

Data on how retinal structural and vascular parameters jointly influence the diagnostic performance of detection of multiple sclerosis (MS) patients without optic neuritis (MSNON) are lacking. To investigate the diagnostic performance of structural and vascular changes to detect MSNON from controls, we performed a cross-sectional study of 76 eyes from 51 MS participants and 117 eyes from 71 healthy controls. Retinal macular ganglion cell complex (GCC), retinal nerve fiber layer (RNFL) thicknesses, and capillary densities from the superficial (SCP) and deep capillary plexuses (DCP) were obtained from the Cirrus AngioPlex. The best structural parameter for detecting MS was compensated RNFL from the optic nerve head (AUC = 0.85), followed by GCC from the macula (AUC = 0.79), while the best vascular parameter was the SCP (AUC = 0.66). Combining structural and vascular parameters improved the diagnostic performance for MS detection (AUC = 0.90; p<0.001). Including both structure and vasculature in the joint model considerably improved the discrimination between MSNON and normal controls compared to each parameter separately (p = 0.027). Combining optical coherence tomography (OCT)-derived structural metrics and vascular measurements from optical coherence tomography angiography (OCTA) improved the detection of MSNON. Further studies may be warranted to evaluate the clinical utility of OCT and OCTA parameters in the prediction of disease progression.

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.

Inner Retinal Layer Changes Reflect Changes in Ambulation Score in Patients with Primary Progressive Multiple Sclerosis

The establishment of surrogate markers to detect disability progression in persons with multiple sclerosis (PwMS) is important to improve monitoring of clinical deterioration. Optical coherence tomography (OCT) could be such a tool. However, sufficient longitudinal data of retinal neuroaxonal degeneration as a marker of disease progression exist only for PwMS with a relapsing-remitting course (RRMS) so far. In contrast, longitudinal data of retinal layers in patients with primary-progressive MS (PPMS) are inconsistent, and the association of OCT parameters with ambulatory performance in PwMS has rarely been investigated. We aimed to investigate the relative annual rates of change in retinal layers in PwMS (RRMS and PPMS) compared with healthy controls (HC) using OCT and to evaluate their association with ambulatoryfunctionalscore (AS) worsening in PPMS. A retrospective analysis of a longitudinal OCT dataset of the retinal layers of PwMS and HC from two MS centers in Germany was performed. Walking ability was measured over a standardized distance of 500 m, and changes during the observation period were categorized using the AS and the expanded disability status scale (EDSS). 61 HC with 121 eyes and 119 PwMS (PPMS: 57 patients with 108 eyes; RRMS: 62 patients with 114 eyes) were included. The median follow-up time for PwMS was 3 years. The relative annual change of pRNFL (peripapillary retinal nerve fiber layer) and INL (inner nuclear layer) was significantly different in PwMS compared with HC. RRMS and PPMS subgroups did not differ in the annual atrophy rates. In patients with PPMS, worsening of the AS was significantly associated with increased thinning of the TMV (total macular volume), GCIP (ganglion cell and inner plexiform layer), and ONPL (outer nuclear and outer plexiform layer) (all p-value < 0.05, r > 0.30). For every -0.1% decrease in the TMV, GCIP, and ONPL, the risk of a deterioration in the AS increased by 31% (hazard ratio (HR): 1.309), 11% (HR: 1.112), and 16% (HR: 1.161), respectively. In addition, worsening EDSS in PPMS was significantly associated with the relative annual atrophy rates of pRNFL, TMV, and GCIP (all p-value < 0.05). Disability progression in PPMS can be measured using OCT, and increasing annual atrophy rates of the inner retinal layers are associated with worsening ambulation. OCT is a robust and side-effect-free imaging tool, making it suitable for routine monitoring of PwMS.

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.

Longitudinal stability of inter-eye differences in optical coherence tomography measures for identifying unilateral optic nerve lesions in multiple sclerosis

INTRODUCTION

Optical coherence tomography (OCT)-derived peripapillary retinal nerve fiber layer (pRNFL) and ganglion cell+inner plexiform layer (GCIPL) thickness inter-eye differences (IEDs) are robust measurements for identifying clinical history acute ON in people with MS (PwMS). This study investigated the utility and durability of these measures as longitudinal markers to identify optic nerve lesions.

METHODS

Prospective, multi-center international study of PwMS (with/without clinical history of ON) and healthy controls. Data from two sites in the International MS Visual System Consortium (IMSVISUAL) were analyzed. Mixed-effects models were used to compare inter-eye differences based on MS and acute ON history.

RESULTS

Average age of those with MS (n = 210) was 39.1 ± 10.8 and 190 (91%) were relapsing-remitting. Fifty-nine (28.1%) had a history of acute unilateral ON, while 9/210 (4.3%) had >1 IB episode. Median follow-up between OCT scans was 9 months. By mixed-effects modeling, IEDs were stable between first and last visits within groups for GCIPL for controls (p = 0.18), all PwMS (p = 0.74), PwMs without ON (p = 0.22), and PwMS with ON (p = 0.48). For pRNFL, IEDs were within controls (p = 0.10), all PwMS (p = 0.53), PwMS without ON history (p = 0.98), and PwMS with history of ON (p = 0.81).

CONCLUSION

We demonstrated longitudinal stability of pRNFL and GCIPL IEDs as markers for optic nerve lesions in PwMS, thus reinforcing the role for OCT in demonstrating optic nerve lesions.

Diagnosis of multiple sclerosis using optical coherence tomography supported by artificial intelligence

BACKGROUND

Current procedures for diagnosing multiple sclerosis (MS) present a series of limitations, making it critically important to identify new biomarkers. The aim of the study was to identify new biomarkers for the early diagnosis of MS using spectral-domain optical coherence tomography (OCT) and artificial intelligence.

METHODS

Spectral domain OCT was performed on 79 patients with relapsing-remitting multiple sclerosis (RRMS) (disease duration ≤ 2 years, no history of optic neuritis) and on 69 age-matched healthy controls using the posterior pole protocol that incorporates the anatomic Positioning System. Median retinal thickness values in both eyes and inter-eye difference in healthy controls and patients were evaluated by area under the receiver operating characteristic (AUROC) curve analysis in the foveal, parafoveal and perifoveal areas and in the overall area spanned by the three rings. The structures with the greatest discriminant capacity - retinal thickness and inter-eye difference - were used as inputs to a convolutional neural network to assess the diagnostic capability.

RESULTS

Analysis of retinal thickness and inter-eye difference in RRMS patients revealed that greatest alteration occurred in the ganglion cell (GCL), inner plexiform (IPL), and inner retinal (IRL) layers. By using the average thickness of the GCL (AUROC = 0.82) and the inter-eye difference in the IPL (AUROC = 0.71) as inputs to a two-layer convolutional neural network, automatic diagnosis attained accuracy = 0.87, sensitivity = 0.82, and specificity = 0.92.

CONCLUSION

This study adds weight to the argument that neuroretinal structure analysis could be incorporated into the diagnostic criteria for MS.

Where's the Vision? The Importance of Visual Outcomes in Neurologic Disorders: The 2021 H. Houston Merritt Lecture

Neurologists have long recognized the importance of the visual system in the diagnosis and monitoring of neurologic disorders. This is particularly true because approximately 50% of the brain's pathways subserve afferent and efferent aspects of vision. During the past 30 years, researchers and clinicians have further refined this concept to include investigation of the visual system for patients with specific neurologic diagnoses, including multiple sclerosis (MS), concussion, Parkinson disease (PD), and conditions along the spectrum of Alzheimer disease (AD, mild cognitive impairment, and subjective cognitive decline). This review highlights the visual "toolbox" that has been developed over the past 3 decades and beyond to capture both structural and functional aspects of vision in neurologic disease. Although the efforts to accelerate the emphasis on structure-function relationships in neurologic disorders began with MS during the early 2000s, such investigations have broadened to recognize the need for outcomes of visual pathway structure, function, and quality of life for clinical trials of therapies across the spectrum of neurologic disorders. This review begins with a patient case study highlighting the importance using the most modern technologies for visual pathway assessment, including optical coherence tomography. We emphasize that both structural and functional tools for vision testing can be used in parallel to detect what might otherwise be subclinical events or markers of visual and, perhaps, more global neurologic decline. Such measures will be critical because clinical trials and therapies become more available across the neurologic disease spectrum.

Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities

Multiple Sclerosis (MS) is a disease that impacts the central nervous system (CNS), which can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated to suffer from MS. Globally, a new case of MS is reported every five minutes. In this review, we discuss the proposed approaches to diagnosing MS using machine learning (ML) published between 2011 and 2022. Numerous models have been developed using different types of data, including magnetic resonance imaging (MRI) and clinical data. We identified the methods that achieved the best results in diagnosing MS. The most implemented approaches are SVM, RF, and CNN. Moreover, we discussed the challenges and opportunities in MS diagnosis to improve AI systems to enable researchers and practitioners to enhance their approaches and improve the automated diagnosis of MS. The challenges faced by automated MS diagnosis include difficulty distinguishing the disease from other diseases showing similar symptoms, protecting the confidentiality of the patients' data, achieving reliable ML models that are also easily understood by non-experts, and the difficulty of collecting a large reliable dataset. Moreover, we discussed several opportunities in the field such as the implementation of secure platforms, employing better AI solutions, developing better disease prognosis systems, combining more than one data type for better MS prediction and using OCT data for diagnosis, utilizing larger, multi-center datasets to improve the reliability of the developed models, and commercialization.