Diagnosis of multiple sclerosis using multifocal ERG data feature fusion

López-Dorado, Almudena; Pérez, Jesús; Rodrigo, María Jesús; Miguel-Jiménez, J. M.; Ortiz, Miguel; Santiago, Luis de; López-Guillén, Elena; Blanco, R.; Cavalliere, C.; Morla, E. M. Sánchez; Boquete, Luciano; García‐Martín, Elena

doi:10.1016/j.inffus.2021.05.006

Cited by 10 publications

(9 citation statements)

References 55 publications

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“…However, it should be noted that the studies in the literature that achieved 100% results such as [ 19 , 39 , 44 , 51 , 66 , 81 , 91 , 101 ] suffer from several limitations.…”

Section: Discussionmentioning

confidence: 99%

“…Dorado et al conducted two studies for the diagnosis of MS. In the first study [ 66 ], they used multifocal ERG data for the diagnosis using a sample of 21 patients. In addition to the small dataset, the samples were skewed toward MS.…”

Section: Discussionmentioning

confidence: 99%

“…L’opez-Dorado et al [ 66 ] developed a CAD system to detect MS disease based on analyzing the outer retina as assessed by multifocal electroretinograms (mfERGs). To analyze the outer retina, 21 scans were taken from patients.…”

Section: Related Studiesmentioning

confidence: 99%

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

Aslam

Khan

Bashamakh

et al. 2022

Sensors

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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.

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“…However, it should be noted that the studies in the literature that achieved 100% results such as [ 19 , 39 , 44 , 51 , 66 , 81 , 91 , 101 ] suffer from several limitations.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

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

Aslam

Khan

Bashamakh

et al. 2022

Sensors

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“…Early-stage MS has a subclinical structural [ 30 ] and electrophysiological [ 77 , 78 ] effect on the retina, possibly as a consequence of retrograde and anterograde axonal degeneration and microglial involvement, among other factors. This damage is only detectable with state-of-the-art, high-resolution tests such as spectral-domain or swept-source OCT and is imperceptible to the human eye during examination of the fundus.…”

Section: Discussionmentioning

confidence: 99%

“…We also consider it worthwhile to research the capacity of artificial intelligence to diagnose MS and its potential development, taking into account other patient variables that could supplement diagnosis, such as MRI, electrophysiological signals (principally visual-evoked potentials) [ 77 ] and clinical data.…”

Section: Discussionmentioning

confidence: 99%

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

López-Dorado

Ortiz

Satué

et al. 2021

Sensors

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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.

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