Computer-Aided Diagnosis of Multiple Sclerosis Using a Support Vector Machine and Optical Coherence Tomography Features

Discrimination of multiple sclerosis using scanning laser ophthalmoscopy images with autoencoder-based feature extraction

OBJECTIVE

Optical coherence tomography (OCT) investigations have revealed that the thickness of inner retinal layers becomes decreased in multiple sclerosis (MS) patients, compared to healthy control (HC) individuals. To date, a number of studies have applied machine learning to OCT thickness measurements, aiming to enable accurate and automated diagnosis of the disease. However, there have much less emphasis on other less common retinal imaging modalities, like infrared scanning laser ophthalmoscopy (IR-SLO), for classifying MS. IR-SLO uses laser light to capture high-resolution fundus images, often performed in conjunction with OCT to lock B-scans at a fixed position.

METHODS

We incorporated two independent datasets of IR-SLO images from the Isfahan and Johns Hopkins centers, consisting of 164 MS and 150 HC images. A subject-wise data splitting approach was employed to ensure that there was no leakage between training and test datasets. Several state-of-the-art convolutional neural networks (CNNs), including VGG-16, VGG-19, ResNet-50, and InceptionV3, and a CNN with a custom architecture were employed. In the next step, we designed a convolutional autoencoder (CAE) to extract semantic features subsequently given as inputs to four conventional ML classifiers, including support vector machine (SVM), k-nearest neighbor (K-NN), random forest (RF), and multi-layer perceptron (MLP).

RESULTS

The custom CNN (85 % accuracy, 85 % sensitivity, 87 % specificity, 93 % area under the receiver operating characteristics [AUROC], and 94 % area under the precision-recall curve [AUPRC]) outperformed state-of-the-art models (84 % accuracy, 83 % sensitivity, 87 % specificity, 92 % AUROC, and 94 % AUPRC); however, utilizing a combination of the CAE and MLP yields even superior results (88 % accuracy, 86 % sensitivity, 91 % specificity, 94 % AUROC, and 95 % AUPRC).

CONCLUSIONS

We utilized IR-SLO images to differentiate between MS and HC eyes, with promising results achieved using a combination of CAE and MLP. Future multi-center studies involving more heterogenous data are necessary to assess the feasibility of integrating IR-SLO images into routine clinical practice.

SLO-Net: Enhancing Multiple Sclerosis Diagnosis Beyond Optical Coherence Tomography Using Infrared Reflectance Scanning Laser Ophthalmoscopy Images

Purpose

Several machine learning studies have used optical coherence tomography (OCT) for multiple sclerosis (MS) classification with promising outcomes. Infrared reflectance scanning laser ophthalmoscopy (IR-SLO) captures high-resolution fundus images, commonly combined with OCT for fixed B-scan positions. However, no machine learning research has utilized IR-SLO images for automated MS diagnosis.

Methods

This study utilized a dataset comprised of IR-SLO images and OCT data from Isfahan, Iran, encompassing 32 MS and 70 healthy individuals. A number of convolutional neural networks (CNNs)-namely, VGG-16, VGG-19, ResNet-50, ResNet-101, and a custom architecture-were trained with both IR-SLO images and OCT thickness maps as two separate input datasets. The highest performing models for each modality were then integrated to create a bimodal model that receives the combination of OCT thickness maps and IR-SLO images. Subject-wise data splitting was employed to prevent data leakage among training, validation, and testing sets.

Results

Overall, images of the 102 patients from the internal dataset were divided into test, validation, and training subsets. Subsequently, we employed a bootstrapping approach on the training data through iterative sampling with replacement. The performance of the proposed bimodal model was evaluated on the internal test dataset, demonstrating an accuracy of 92.40% ± 4.1% (95% confidence interval [CI], 83.61-98.08), sensitivity of 95.43% ± 5.75% (95% CI, 83.71-100.0), specificity of 92.82% ± 3.72% (95% CI, 81.15-96.77), area under the receiver operating characteristic (AUROC) curve of 96.99% ± 2.99% (95% CI, 86.11-99.78), and area under the precision-recall curve (AUPRC) of 97.27% ± 2.94% (95% CI, 86.83-99.83). Furthermore, to assess the model generalization ability, we examined its performance on an external test dataset following the same bootstrap methodology, achieving promising results, with accuracy of 85.43% ± 0.08% (95% CI, 71.43-100.0), sensitivity of 97.33% ± 0.06% (95% CI, 83.33-100.0), specificity of 84.6% ± 0.10% (95% CI, 71.43-100.0), AUROC curve of 99.67% ± 0.02% (95% CI, 95.63-100.0), and AUPRC of 99.65% ± 0.02% (95% CI, 94.90-100.0).

Conclusions

Incorporating both modalities improves the performance of automated diagnosis of MS, showcasing the potential of utilizing IR-SLO as a complementary tool alongside OCT.

Translational Relevance

Should the results of our proposed bimodal model be validated in future work with larger and more diverse datasets, diagnosis of MS based on both OCT and IR-SLO can be reliably integrated into routine clinical practice.

Oculomics analysis in multiple sclerosis: Current ophthalmic clinical and imaging biomarkers

Multiple Sclerosis (MS) is a chronic autoimmune demyelinating disease of the central nervous system (CNS) characterized by inflammation, demyelination, and axonal damage. Early recognition and treatment are important for preventing or minimizing the long-term effects of the disease. Current gold standard modalities of diagnosis (e.g., CSF and MRI) are invasive and expensive in nature, warranting alternative methods of detection and screening. Oculomics, the interdisciplinary combination of ophthalmology, genetics, and bioinformatics to study the molecular basis of eye diseases, has seen rapid development through various technologies that detect structural, functional, and visual changes in the eye. Ophthalmic biomarkers (e.g., tear composition, retinal nerve fibre layer thickness, saccadic eye movements) are emerging as promising tools for evaluating MS progression. The eye's structural and embryological similarity to the brain makes it a potentially suitable assessment of neurological and microvascular changes in CNS. In the advent of more powerful machine learning algorithms, oculomics screening modalities such as optical coherence tomography (OCT), eye tracking, and protein analysis become more effective tools aiding in MS diagnosis. Artificial intelligence can analyse larger and more diverse data sets to potentially discover new parameters of pathology for efficiently diagnosing MS before symptom onset. While there is no known cure for MS, the integration of oculomics with current modalities of diagnosis creates a promising future for developing more sensitive, non-invasive, and cost-effective approaches to MS detection and 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.

A systematic review of the application of machine-learning algorithms in multiple sclerosis

INTRODUCTION

The applications of artificial intelligence, and in particular automatic learning or "machine learning" (ML), constitute both a challenge and a great opportunity in numerous scientific, technical, and clinical disciplines. Specific applications in the study of multiple sclerosis (MS) have been no exception, and constitute an area of increasing interest in recent years.

OBJECTIVE

We present a systematic review of the application of ML algorithms in MS.

MATERIALS AND METHODS

We used the PubMed search engine, which allows free access to the MEDLINE medical database, to identify studies including the keywords "machine learning" and "multiple sclerosis." We excluded review articles, studies written in languages other than English or Spanish, and studies that were mainly technical and did not specifically apply to MS. The final selection included 76 articles, and 38 were rejected.

CONCLUSIONS

After the review process, we established 4 main applications of ML in MS: 1) classifying MS subtypes; 2) distinguishing patients with MS from healthy controls and individuals with other diseases; 3) predicting progression and response to therapeutic interventions; and 4) other applications. Results found to date have shown that ML algorithms may offer great support for health professionals both in clinical settings and in research into MS.

Innovations in Multiple Sclerosis Care: The Impact of Artificial Intelligence via Machine Learning on Clinical Research and Decision-Making

Artificial intelligence (AI) and its specialized subcomponent machine learning are becoming increasingly popular analytic techniques. With this growth, clinicians and health care professionals should soon expect to see an increase in diagnostic, therapeutic, and rehabilitative technologies and processes that use elements of AI. The purpose of this review is twofold. First, we provide foundational knowledge that will help health care professionals understand these modern algorithmic techniques and their implementation for classification and clustering tasks. The phrases artificial intelligence and machine learning are defined and distinguished, as are the metrics by which they are assessed and delineated. Subsequently, 7 broad categories of algorithms are discussed, and their uses explained. Second, this review highlights several key studies that exemplify advances in diagnosis, treatment, and rehabilitation for individuals with multiple sclerosis using a variety of data sources-from wearable sensors to questionnaires and serology-and elements of AI. This review will help health care professionals and clinicians better understand AI-dependent diagnostic, therapeutic, and rehabilitative techniques, thereby facilitating a greater quality of care.

Discrimination of multiple sclerosis using OCT images from two different centers

BACKGROUND

Multiple sclerosis (MS) is one of the most prevalent chronic inflammatory diseases caused by demyelination and axonal damage in the central nervous system. Structural retinal imaging via optical coherence tomography (OCT) shows promise as a noninvasive biomarker for monitoring of MS. There are successful reports regarding the application of Artificial Intelligence (AI) in the analysis of cross-sectional OCTs in ophthalmologic diseases. However, the alteration of thicknesses of various retinal layers in MS is noticeably subtle compared to other ophthalmologic diseases. Therefore, raw cross-sectional OCTs are replaced with multilayer segmented OCTs for discrimination of MS and healthy controls (HCs).

METHODS

To conform to the principles of trustworthy AI, interpretability is provided by visualizing the regional layer contribution to classification performance with the proposed occlusion sensitivity approach. The robustness of the classification is also guaranteed by showing the effectiveness of the algorithm while being tested on the new independent dataset. The most discriminative features from different topologies of the multilayer segmented OCTs are selected by the dimension reduction method. Support vector machine (SVM), random forest (RF), and artificial neural network (ANN) are used for classification. Patient-wise cross-validation (CV) is utilized to evaluate the performance of the algorithm, where the training and test folds contain records from different subjects.

RESULTS

The most discriminative topology is determined to square with a size of 40 pixels and the most influential layers are the ganglion cell and inner plexiform layer (GCIPL) and inner nuclear layer (INL). Linear SVM resulted in 88% Accuracy (with standard deviation (std) = 0.49 in 10 times of execution to indicate the repeatability), 78% precision (std=1.48), and 63% recall (std=1.35) in the discrimination of MS and HCs using macular multilayer segmented OCTs.

CONCLUSION

The proposed classification algorithm is expected to help neurologists in the early diagnosis of MS. This paper distinguishes itself from other studies by employing two distinct datasets, which enhances the robustness of its findings in comparison with previous studies with lack of external validation. This study aims to circumvent the utilization of deep learning methods due to the limited quantity of the available data and convincingly demonstrates that favorable outcomes can be achieved without relying on deep learning techniques.

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.

Evaluation of multiple sclerosis severity using a new OCT tool

PURPOSE

To assess the ability of a new posterior pole protocol to detect areas with significant differences in retinal nerve fiber layer (RNFL) and ganglion cell layer (GCL) thickness in patients with multiple sclerosis versus healthy control subjects; in addition, to assess the correlation between RNFL and GCL thickness, disease duration, and the Expanded Disability Status Scale (EDSS).

METHODS

We analyzed 66 eyes of healthy control subjects and 100 eyes of remitting-relapsing multiple sclerosis (RR-MS) patients. Double analysis based on first clinical symptom onset (CSO) and conversion to clinically definite MS (CDMS) was performed. The RR-MS group was divided into subgroups by CSO and CDMS year: CSO-1 (≤ 5 years) and CSO-2 (≥ 6 years), and CDMS-1 (≤ 5 years) and CDMS-2 (≥ 6 years).

RESULTS

Significant differences in RNFL and GCL thickness were found between the RR-MS group and the healthy controls and between the CSO and CDMS subgroups and in both layers. Moderate to strong correlations were found between RNFL and GCL thickness and CSO and CDMS. Furthermore, we observed a strong correlation with EDSS 1 year after the OCT examination.

CONCLUSIONS

The posterior pole protocol is a useful tool for assessing MS and can reveal differences even in early stages of the disease. RNFL thickness shows a strong correlation with disability status, while GCL thickness correlates better with disease duration.

Ocular images-based artificial intelligence on systemic diseases

PURPOSE

To provide a summary of the research advances on ocular images-based artificial intelligence on systemic diseases.

METHODS

Narrative literature review.

RESULTS

Ocular images-based artificial intelligence has been used in a variety of systemic diseases, including endocrine, cardiovascular, neurological, renal, autoimmune, and hematological diseases, and many others. However, the studies are still at an early stage. The majority of studies have used AI only for diseases diagnosis, and the specific mechanisms linking systemic diseases to ocular images are still unclear. In addition, there are many limitations to the research, such as the number of images, the interpretability of artificial intelligence, rare diseases, and ethical and legal issues.

CONCLUSION

While ocular images-based artificial intelligence is widely used, the relationship between the eye and the whole body should be more clearly elucidated.

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.

Diagnostic performance of artificial intelligence in multiple sclerosis: a systematic review and meta-analysis

BACKGROUND

The expansion of the availability of advanced imaging methods needs more time, expertise, and resources which is in contrast to the primary goal of the imaging techniques. To overcome most of these difficulties, artificial intelligence (AI) can be used. A number of studies used AI models for multiple sclerosis (MS) diagnosis and reported diverse results. Therefore, we aim to perform a comprehensive systematic review and meta-analysis study on the role of AI in the diagnosis of MS.

METHODS

We performed a systematic search using four databases including PubMed, Scopus, Web of Science, and IEEE. Studies that applied deep learning or AI to the diagnosis of MS based on any modalities were considered eligible in our study. The accuracy, sensitivity, specificity, precision, and area under curve (AUC) were pooled with a random-effects model and 95% confidence interval (CI).

RESULTS

After the screening, 41 articles with 5989 individuals met the inclusion criteria and were included in our qualitative and quantitative synthesis. Our analysis showed that the overall accuracy among studies was 94% (95%CI: 93%, 96%). The pooled sensitivity and specificity were 92% (95%CI: 90%, 95%) and 93% (95%CI: 90%, 96%), respectively. Furthermore, our analysis showed 92% precision in MS diagnosis for AI studies (95%CI: 88%, 97%). Also, the overall pooled AUC was 93% (95%CI: 89%, 96%).

CONCLUSION

Overall, AI models can further improve our diagnostic practice in MS patients. Our results indicate that the use of AI can aid the clinicians in accurate diagnosis of MS and improve current diagnostic approaches as most of the parameters including accuracy, sensitivity, specificity, precision, and AUC were considerably high, especially when using MRI data.

Identifying Complex Emotions in Alexithymia Affected Adolescents Using Machine Learning Techniques

Many scientific researchers' study focuses on enhancing automated systems to identify emotions and thus relies on brain signals. This study focuses on how brain wave signals can be used to classify many emotional states of humans. Electroencephalography (EEG)-based affective computing predominantly focuses on emotion classification based on facial expression, speech recognition, and text-based recognition through multimodality stimuli. The proposed work aims to implement a methodology to identify and codify discrete complex emotions such as pleasure and grief in a rare psychological disorder known as alexithymia. This type of disorder is highly elicited in unstable, fragile countries such as South Sudan, Lebanon, and Mauritius. These countries are continuously affected by civil wars and disaster and politically unstable, leading to a very poor economy and education system. This study focuses on an adolescent age group dataset by recording physiological data when emotion is exhibited in a multimodal virtual environment. We decocted time frequency analysis and amplitude time series correlates including frontal alpha symmetry using a complex Morlet wavelet. For data visualization, we used the UMAP technique to obtain a clear district view of emotions. We performed 5-fold cross validation along with 1 s window subjective classification on the dataset. We opted for traditional machine learning techniques to identify complex emotion labeling.

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.

Predicting disease activity in patients with multiple sclerosis: An explainable machine‐learning approach in the Mavenclad trials

Multiple sclerosis (MS) is among the most common autoimmune disabling neurological conditions of young adults and affects more than 2.3 million people worldwide. Predicting future disease activity in patients with MS based on their pathophysiology and current treatment is pivotal to orientate future treatment. In this respect, we used machine learning to predict disease activity status in patients with MS and identify the most predictive covariates of this activity. The analysis is conducted on a pooled population of 1935 patients enrolled in three cladribine tablets clinical trials with different outcomes: relapsing–remitting MS (from CLARITY and CLARITY‐Extension trials) and patients experiencing a first demyelinating event (from the ORACLE‐MS trial). We applied gradient‐boosting (from XgBoost library) and Shapley Additive Explanations (SHAP) methods to identify patients' covariates that predict disease activity 3 and 6 months before their clinical observation, including patient baseline characteristics, longitudinal magnetic resonance imaging readouts, and neurological and laboratory measures. The most predictive covariates for early identification of disease activity in patients were found to be treatment duration, higher number of new combined unique active lesion count, higher number of new T1 hypointense black holes, and higher age‐related MS severity score. The outcome of this analysis improves our understanding of the mechanism of onset of disease activity in patients with MS by allowing their early identification in clinical settings and prompting preventive measures, therapeutic interventions, or more frequent patient monitoring.

Role of artificial intelligence in MS clinical practice

•

For medical applications, machine learning (including deep learning) are the most commonly used artificial intelligence (AI) approaches.

•

It can improve multiple sclerosis (MS) diagnosis, prognostication and treatment monitoring.

•

Thanks to AI, MRI and cognitive phenotypes of MS patients were identified.

•

AI can shorten MRI protocols for MS, allowing the application of advanced techniques.

•

It can reduce the human effort for MRI analysis, especially for lesion segmentation.

Machine learning (ML) and its subset, deep learning (DL), are branches of artificial intelligence (AI) showing promising findings in the medical field, especially when applied to imaging data. Given the substantial role of MRI in the diagnosis and management of patients with multiple sclerosis (MS), this disease is an ideal candidate for the application of AI techniques. In this narrative review, we are going to discuss the potential applications of AI for MS clinical practice, together with their limitations. Among their several advantages, ML algorithms are able to automate repetitive tasks, to analyze more data in less time and to achieve higher accuracy and reproducibility than the human counterpart. To date, these algorithms have been applied to MS diagnosis, prognosis, disease and treatment monitoring. Other fields of application have been improvement of MRI protocols as well as automated lesion and tissue segmentation. However, several challenges remain, including a better understanding of the information selected by AI algorithms, appropriate multicenter and longitudinal validations of results and practical aspects regarding hardware and software integration. Finally, one cannot overemphasize the paramount importance of human supervision, in order to optimize the use and take full advantage of the potential of AI approaches.

Artificial Intelligence in Predicting Systemic Parameters and Diseases From Ophthalmic Imaging

Artificial Intelligence (AI) analytics has been used to predict, classify, and aid clinical management of multiple eye diseases. Its robust performances have prompted researchers to expand the use of AI into predicting systemic, non-ocular diseases and parameters based on ocular images. Herein, we discuss the reasons why the eye is well-suited for systemic applications, and review the applications of deep learning on ophthalmic images in the prediction of demographic parameters, body composition factors, and diseases of the cardiovascular, hematological, neurodegenerative, metabolic, renal, and hepatobiliary systems. Three main imaging modalities are included—retinal fundus photographs, optical coherence tomographs and external ophthalmic images. We examine the range of systemic factors studied from ophthalmic imaging in current literature and discuss areas of future research, while acknowledging current limitations of AI systems based on ophthalmic images.

Comparison of Machine Learning Methods Using Spectralis OCT for Diagnosis and Disability Progression Prognosis in Multiple Sclerosis

Machine learning approaches in diagnosis and prognosis of multiple sclerosis (MS) were analysed using retinal nerve fiber layer (RNFL) thickness, measured by optical coherence tomography (OCT). A cross-sectional study (72 MS patients and 30 healthy controls) was used for diagnosis. These 72 MS patients were involved in a 10-year longitudinal follow-up study for prognostic purposes. Structural measurements of RNFL thickness were performed using different Spectralis OCT protocols: fast macular thickness protocol to measure macular RNFL, and fast RNFL thickness protocol and fast RNFL-N thickness protocol to measure peripapillary RNFL. Binary classifiers such as multiple linear regression (MLR), support vector machines (SVM), decision tree (DT), k-nearest neighbours (k-NN), Naïve Bayes (NB), ensemble classifier (EC) and long short-term memory (LSTM) recurrent neural network were tested. For MS diagnosis, the best acquisition protocol was fast macular thickness protocol using k-NN (accuracy: 95.8%; sensitivity: 94.4%; specificity: 97.2%; precision: 97.1%; AUC: 0.958). For MS prognosis, our model with a 3-year follow up to predict disability progression 8 years later was the best predictive model. DT performed best for fast macular thickness protocol (accuracy: 91.3%; sensitivity: 90.0%; specificity: 92.5%; precision: 92.3%; AUC: 0.913) and SVM for fast RNFL-N thickness protocol (accuracy: 91.3%; sensitivity: 87.5%; specificity: 95.0%; precision: 94.6%; AUC: 0.913). This work concludes that measurements of RNFL thickness obtained with Spectralis OCT have a good ability to diagnose MS and to predict disability progression in MS patients. This machine learning approach would help clinicians to have valuable information.

Early Diagnosis of Multiple Sclerosis Using Swept-Source Optical Coherence Tomography and Convolutional Neural Networks Trained with Data Augmentation

BACKGROUND

The aim of this paper is to implement a system to facilitate the diagnosis of multiple sclerosis (MS) in its initial stages. It does so using a convolutional neural network (CNN) to classify images captured with swept-source optical coherence tomography (SS-OCT).

METHODS

SS-OCT images from 48 control subjects and 48 recently diagnosed MS patients have been used. These images show the thicknesses (45 × 60 points) of the following structures: complete retina, retinal nerve fiber layer, two ganglion cell layers (GCL+, GCL++) and choroid. The Cohen distance is used to identify the structures and the regions within them with greatest discriminant capacity. The original database of OCT images is augmented by a deep convolutional generative adversarial network to expand the CNN's training set.

RESULTS

The retinal structures with greatest discriminant capacity are the GCL++ (44.99% of image points), complete retina (26.71%) and GCL+ (22.93%). Thresholding these images and using them as inputs to a CNN comprising two convolution modules and one classification module obtains sensitivity = specificity = 1.0.

CONCLUSIONS

Feature pre-selection and the use of a convolutional neural network may be a promising, nonharmful, low-cost, easy-to-perform and effective means of assisting the early diagnosis of MS based on SS-OCT thickness data.

Diagnosis of multiple sclerosis using multifocal ERG data feature fusion

The purpose of this paper is to implement a computer-aided diagnosis (CAD) system for multiple sclerosis (MS) based on analysing the outer retina as assessed by multifocal electroretinograms (mfERGs). MfERG recordings taken with the RETI-port/scan 21 (Roland Consult) device from 15 eyes of patients diagnosed with incipient relapsing-remitting MS and without prior optic neuritis, and from 6 eyes of control subjects, are selected. The mfERG recordings are grouped (whole macular visual field, five rings, and four quadrants). For each group, the correlation with a normative database of adaptively filtered signals, based on empirical model decomposition (EMD) and three features from the continuous wavelet transform (CWT) domain, are obtained. Of the initial 40 features, the 4 most relevant are selected in two stages: a) using a filter method and b) using a wrapper-feature selection method. The Support Vector Machine (SVM) is used as a classifier. With the optimal CAD configuration, a Matthews correlation coefficient value of 0.89 (accuracy = 0.95, specificity = 1.0 and sensitivity = 0.93) is obtained. This study identified an outer retina dysfunction in patients with recent MS by analysing the outer retina responses in the mfERG and employing an SVM as a classifier. In conclusion, a promising new electrophysiological-biomarker method based on feature fusion for MS diagnosis was identified.

Automatic Quantification of Anterior Lamina Cribrosa Structures in Optical Coherence Tomography Using a Two-Stage CNN Framework

In this study, we propose a new intelligent system to automatically quantify the morphological parameters of the lamina cribrosa (LC) of the optical coherence tomography (OCT), including depth, curve depth, and curve index from OCT images. The proposed system consisted of a two-stage deep learning (DL) model, which was composed of the detection and the segmentation models as well as a quantification process with a post-processing scheme. The models were used to solve the class imbalance problem and obtain Bruch's membrane opening (BMO) as well as anterior LC information. The detection model was implemented by using YOLOv3 to acquire the BMO and LC position information. The Attention U-Net segmentation model is used to compute accurate locations of the BMO and LC curve information. In addition, post-processing is applied using polynomial regression to attain the anterior LC curve boundary information. Finally, the numerical values of morphological parameters are quantified from BMO and LC curve information using an image processing algorithm. The average precision values in the detection performances of BMO and LC information were 99.92% and 99.18%, respectively, which is very accurate. A highly correlated performance of R2 = 0.96 between the predicted and ground-truth values was obtained, which was very close to 1 and satisfied the quantification results. The proposed system was performed accurately by fully automatic quantification of BMO and LC morphological parameters using a DL model.

Analysis of Retinal Layers in Fibromyalgia Patients with Premium Protocol in Optical Tomography Coherence and Quality of Life

PURPOSE

To evaluate the inner retinal layers in fibromyalgia (FM) patients compared to control subjects using posterior pole protocol (PPole) analysis in optical coherence tomography (OCT) and to correlate structural retinal changes with subjective quality of life.

METHODS

Seventy-four eyes of healthy subjects and 55 eyes of those with FM were analyzed. All subjects underwent retinal evaluation using the PPole protocol for Spectralis OCT (Heidelberg Engineering) to obtain measurements of the retinal nerve fiber layer (RNFL) and the ganglion cell layer (GCL) in the macular area. The EuroQol (EQ-5D) questionnaire and Fibromyalgia Impact Questionnaire (FIQ) were performed to analyze health-related quality of life. Additionally, the FM group was divided into three groups depending on the disease phenotype (atypical, depressive and biological).

RESULTS

: Patients with FM presented with a reduction of the RNFL thickness compared to controls in 17/64 cells of the PPole area, and a reduction of the GCL thickness in 47/64 cells. Depressive FM phenotype showed the greatest number of cells with significant reduction compared with the control group in both RNFL and GCL layers. A correlation between temporal-inferior cells of the GCL and the EuroQol 5D questionnaire results was observed.

CONCLUSIONS

Patients with FM present with a reduction of the inner retinal layers in the macular area. This degeneration correlates with disease severity/reduced quality of life in these patients. The PPole protocol for OCT is a non-invasive and fast tool that might help clinicians diagnose and monitor neurodegeneration in FM patients.

Machine learning in diagnosis and disability prediction of multiple sclerosis using optical coherence tomography

BACKGROUND

Multiple sclerosis (MS) is a neurodegenerative disease that affects the central nervous system, especially the brain, spinal cord, and optic nerve. Diagnosis of this disease is a very complex process and generally requires a lot of time. In addition, treatments are applied without any information on the disability course in each MS patient. For these two reasons, the objective of this study was to improve the MS diagnosis and predict the long-term course of disability in MS patients based on clinical data and retinal nerve fiber layer (RNFL) thickness, measured by optical coherence tomography (OCT).

MATERIAL AND METHODS

A total of 104 healthy controls and 108 MS patients, 82 of whom had a 10-year follow-up, were enrolled. Classification algorithms such as multiple linear regression (MLR), support vector machines (SVM), decision tree (DT), k-nearest neighbours (k-NN), Naïve Bayes (NB), ensemble classifier (EC) and long short-term memory (LSTM) recurrent neural network were tested to develop two predictive models: MS diagnosis model and MS disability course prediction model.

RESULTS

For MS diagnosis, the best result was obtained using EC (accuracy: 87.7%; sensitivity: 87.0%; specificity: 88.5%; precision: 88.7%; AUC: 0.8775). In line with this good performance, the accuracy was 85.4% using k-NN and 84.4% using SVM. And, for long-term prediction of MS disability course, LSTM recurrent neural network was the most appropriate classifier (accuracy: 81.7%; sensitivity: 81.1%; specificity: 82.2%; precision: 78.9%; AUC: 0.8165). The use of MLR, SVM and k-NN also showed a good performance (AUC ≥ 0.8).

CONCLUSIONS

This study demonstrated that machine learning techniques, using clinical and OCT data, can help establish an early diagnosis and predict the course of MS. This advance could help clinicians select more specific treatments for each MS patient. Therefore, our findings underscore the potential of RNFL thickness as a reliable MS biomarker.

Artificial Intelligence in Diagnosis and Prediction of the Multiple Sclerosis Progression: A Scoping Review (Preprint).. [DOI: 10.2196/preprints.29720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

BACKGROUND

Multiple Sclerosis (MS) is an autoimmune disease that results from the demyelination of the nerves in the Central Nervous System. The diagnosis depends on clinical history, neurological examination, and radiological images. Artificial Intelligence proved to be an effective tool in enhancing the diagnostic tools of MS.

OBJECTIVE

To explore how AI assisted in diagnosis and predicting the progression of MS.

METHODS

We used three bibliographic databases in our search: PubMed IEEE Xplore and Cochrane in our search. The study selection process included: removal of duplicated articles, screening titles and abstracts, and reading the full text. This process was performed by two reviewers. The data extracted from the included studies have been filled in an Excel sheet. This step had been done by each reviewer accordingly to the assigned articles. The extracted data sheet was checked by two reviewers to have accuracy ensured. The narrative approach is applied in data synthesis.

RESULTS

The search conducted resulted in 320 articles Removing duplicates and excluding the ineligible articles due to irrelevancy to the population, intervention, and outcomes resulted in excluding 299 articles. Thus, our review will include 21 articles for data extraction and data synthesis.

CONCLUSIONS

Artificial Intelligence is becoming a trend in the medical field. Its contribution in enhancing the diagnostic tools of many diseases, as in MS, is prominent and can be built on in further development plans. However, the implementation of Artificial Intelligence in Multiple Sclerosis is not widespread to confirm the benefits gained, and the datasets involved in the current practice are relatively small. It is recommended to have more studies that focus on the relationship between the employment of AI in diagnosis and monitoring progression and the accuracy gained by this employment.

A systematic review of the application of machine-learning algorithms in multiple sclerosis

INTRODUCTION

The applications of artificial intelligence, and in particular automatic learning or "machine learning" (ML), constitute both a challenge and a great opportunity in numerous scientific, technical, and clinical disciplines. Specific applications in the study of multiple sclerosis (MS) have been no exception, and constitute an area of increasing interest in recent years.

OBJECTIVE

We present a systematic review of the application of ML algorithms in MS.

MATERIALS AND METHODS

We used the PubMed search engine, which allows free access to the MEDLINE medical database, to identify studies including the keywords "machine learning" and "multiple sclerosis." We excluded review articles, studies written in languages other than English or Spanish, and studies that were mainly technical and did not specifically apply to MS. The final selection included 76 articles, and 38 were rejected.

CONCLUSIONS

After the review process, we established 4 main applications of ML in MS: 1) classifying MS subtypes; 2) distinguishing patients with MS from healthy controls and individuals with other diseases; 3) predicting progression and response to therapeutic interventions; and 4) other applications. Results found to date have shown that ML algorithms may offer great support for health professionals both in clinical settings and in research into MS.

E-health and multiple sclerosis

PURPOSE OF REVIEW

To outline recent applications of e-health data and digital tools for improving the care and management of healthcare for people with multiple sclerosis.

RECENT FINDINGS

The digitization of most clinical data, along with developments in communication technologies, miniaturization of sensors and computational advances are enabling aggregation and clinically meaningful analyses of real-world data from patient registries, digital patient-reported outcomes and electronic health records (EHR). These data are allowing more confident descriptions of prognoses for multiple sclerosis patients and the long-term relative benefits and safety of disease-modifying treatments (DMT). Registries allow detailed, multiple sclerosis-specific data to be shared between clinicians more easily, provide data needed to improve the impact of DMT and, with EHR, characterize clinically relevant interactions between multiple sclerosis and other diseases. Wearable sensors provide continuous, long-term measures of performance dynamics in relevant ecological settings. In conjunction with telemedicine and online apps, they promise a major expansion of the scope for patients to manage aspects of their own care. Advances in disease understanding, decision support and self-management using these Big Data are being accelerated by machine learning and artificial intelligence.

SUMMARY

Both health professionals and patients can employ e-health approaches and tools for development of a more patient-centred learning health system.

Retinal applications of swept source optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA)

The advent of optical coherence tomography (OCT) revolutionized both clinical assessment and research of vitreoretinal conditions. Since then, extraordinary advances have been made in this imaging technology, including the relatively recent development of swept-source OCT (SS-OCT). SS-OCT enables a fast scan rate and utilizes a tunable swept laser, thus enabling the incorporation of longer wavelengths than conventional spectral-domain devices. These features enable imaging of larger areas with reduced motion artifact, and a better visualization of the choroidal vasculature, respectively. Building on the principles of OCT, swept-source OCT has also been applied to OCT angiography (SS-OCTA), thus enabling a non-invasive in depth-resolved imaging of the retinal and choroidal microvasculature. Despite their advantages, the widespread use of SS-OCT and SS-OCTA remains relatively limited. In this review, we summarize the technical details, advantages and limitations of SS-OCT and SS-OCTA, with a particular emphasis on their relevance for the study of retinal conditions. Additionally, we comprehensively review relevant studies performed to date to the study of retinal health and disease, and highlight current gaps in knowledge and opportunities to take advantage of swept source technology to improve our current understanding of many medical and surgical chorioretinal conditions. We anticipate that SS-OCT and SS-OCTA will continue to evolve rapidly, contributing to a paradigm shift to more widespread adoption of new imaging technology to clinical practice.

Prediction of disease progression and outcomes in multiple sclerosis with machine learning

Multiple Sclerosis is a chronic inflammatory disease, affecting the Central Nervous System and leading to irreversible neurological damage, such as long term functional impairment and disability. It has no cure and the symptoms vary widely, depending on the affected regions, amount of damage, and the ability to activate compensatory mechanisms, which constitutes a challenge to evaluate and predict its course. Additionally, relapsing-remitting patients can evolve its course into a secondary progressive, characterized by a slow progression of disability independent of relapses. With clinical information from Multiple Sclerosis patients, we developed a machine learning exploration framework concerning this disease evolution, more specifically to obtain three predictions: one on conversion to secondary progressive course and two on disease severity with rapid accumulation of disability, concerning the 6th and 10th years of progression. For the first case, the best results were obtained within two years: AUC=[Formula: see text], sensitivity=[Formula: see text] and specificity=[Formula: see text]; and for the second, the best results were obtained for the 6th year of progression, also within two years: AUC=[Formula: see text], sensitivity=[Formula: see text], and specificity=[Formula: see text]. The Expanded Disability Status Scale value, the majority of functional systems, affected functions during relapses, and age at onset were described as the most predictive features. These results demonstrate the possibility of predicting Multiple Sclerosis progression by using machine learning, which may help to understand this disease's dynamics and thus, advise physicians on medication intake.

Empirical Mode Decomposition-Based Filter Applied to Multifocal Electroretinograms in Multiple Sclerosis Diagnosis

As multiple sclerosis (MS) usually affects the visual pathway, visual electrophysiological tests can be used to diagnose it. The objective of this paper is to research methods for processing multifocal electroretinogram (mfERG) recordings to improve the capacity to diagnose MS. MfERG recordings from 15 early-stage MS patients without a history of optic neuritis and from 6 control subjects were examined. A normative database was built from the control subject signals. The mfERG recordings were filtered using empirical mode decomposition (EMD). The correlation with the signals in a normative database was used as the classification feature. Using EMD-based filtering and performance correlation, the mean area under the curve (AUC) value was 0.90. The greatest discriminant capacity was obtained in ring 4 and in the inferior nasal quadrant (AUC values of 0.96 and 0.94, respectively). Our results suggest that the combination of filtering mfERG recordings using EMD and calculating the correlation with a normative database would make mfERG waveform analysis applicable to assessment of multiple sclerosis in early-stage patients.