Artificial Neural Network Techniques to Improve the Ability of Optical Coherence Tomography to Detect Optic Neuritis

García‐Martín, Elena; Herrero, Raquel; Bambo, M; Ara, J.R.; Martín, Javier Montero; Polo, Vicente; Larrosa, José M.; García‐Feijoó, Julián; Júlvez, Luis Pablo

doi:10.3109/08820538.2013.810277

Cited by 14 publications

(12 citation statements)

References 31 publications

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“…This result (acc: 95.8%; AUC: 0.958) was better than that obtained in previous works by Garcia-Martin et al . who also used Spectralis OCT: an AUC of 0.945 18 and an accuracy of 88.5% 16 using ANN. Compared to studies that used SS-OCT Triton to measure RNFL, Pérez del Palomar et al .…”

Section: Discussionmentioning

confidence: 99%

“…2,12,55 In this way, some authors demonstrated its potential, in combination with artificial intelligence (AI), as an early diagnostic tool. Garcia-Martin et al 16 applied artificial neural network (ANN) to pRNFL thickness in order to analyse the ability of Spectralis OCT to diagnose MS. Cavaliere et al 9 designed a computer-aided diagnosis method using support vector machine (SVM) with mRNFL and pRNFL measurements performed by Triton SS-OCT from 48 MS patients and 48 healthy controls. These database was also used by Garcia-Martin et al 17 with a feed-forward neural network as a deep learning technique.…”

Section: Introductionmentioning

confidence: 99%

“…Garcia-Martin et al . 16 applied artificial neural network (ANN) to pRNFL thickness in order to analyse the ability of Spectralis OCT to diagnose MS. Cavaliere et al . 9 designed a computer-aided diagnosis method using support vector machine (SVM) with mRNFL and pRNFL measurements performed by Triton SS-OCT from 48 MS patients and 48 healthy controls.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…In this paper we explore the use of machine learning techniques to detect the disease using data from SS-OCT. To the best of our knowledge, this is the first study evaluating MS eyes using swept source OCT technology in combination with machine learning techniques. Some preliminary studies [13][14] have analyzed the role of these techniques using specific protocols and other OCT devices. Here, different protocols obtained by DRI OCT Triton were analyzed and compared in order to specify the success ratio of different machine learning techniques for determining MS disease.…”

Section: Introductionmentioning

confidence: 99%

Swept source optical coherence tomography to early detect multiple sclerosis disease. The use of machine learning techniques

et al. 2019

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Objective To compare axonal loss in ganglion cells detected with swept-source optical coherence tomography (SS-OCT) in eyes of patients with multiple sclerosis (MS) versus healthy controls using different machine learning techniques. To analyze the capability of machine learning techniques to improve the detection of retinal nerve fiber layer (RNFL) and the complex Ganglion Cell Layer–Inner plexiform layer (GCL+) damage in patients with multiple sclerosis and to use the SS-OCT as a biomarker to early predict this disease. Methods Patients with relapsing-remitting MS (n = 80) and age-matched healthy controls (n = 180) were enrolled. Different protocols from the DRI SS-OCT Triton system were used to obtain the RNFL and GCL+ thicknesses in both eyes. Macular and peripapilar areas were analyzed to detect the zones with higher thickness decrease. The performance of different machine learning techniques (decision trees, multilayer perceptron and support vector machine) for identifying RNFL and GCL+ thickness loss in patients with MS were evaluated. Receiver-operating characteristic (ROC) curves were used to display the ability of the different tests to discriminate between MS and healthy eyes in our population. Results Machine learning techniques provided an excellent tool to predict MS disease using SS-OCT data. In particular, the decision trees obtained the best prediction (97.24%) using RNFL data in macular area and the area under the ROC curve was 0.995, while the wide protocol which covers an extended area between macula and papilla gave an accuracy of 95.3% with a ROC of 0.998. Moreover, it was obtained that the most significant area of the RNFL to predict MS is the macula just surrounding the fovea. On the other hand, in our study, GCL+ did not contribute to predict MS and the different machine learning techniques performed worse in this layer than in RNFL. Conclusions Measurements of RNFL thickness obtained with SS-OCT have an excellent ability to differentiate between healthy controls and patients with MS. Thus, the use of machine learning techniques based on these measures can be a reliable tool to help in MS diagnosis.

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“…In ihrer Studie aus dem Jahr 2015 erkannte der Algorithmus eine MS mit einer Sensitivität von 89,3 % und einer Spezifität von 87,6 %. Des Weiteren waren sie in der Lage bei MS-Patienten zwischen solchen mit Zustand nach Neuritis nervi optici und solche ohne zu entscheiden [36]. In ihrer Studie aus dem Jahr 2020 wurde für die Unterscheidung zwischen gesund und MS eine Sensitivität und eine Spezifität von 98 % erreicht [37].…”

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Anwendungsmöglichkeiten von „Künstlicher Intelligenz“ und „Big Data“ in der ophthalmologischen Diagnostik

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Artificial Neural Network Techniques to Improve the Ability of Optical Coherence Tomography to Detect Optic Neuritis

Abstract: ANN technique improves the capability of Spectralis OCT to detect MS disease and to distinguish MS eyes with or without antecedent ON.

Cited by 14 publications

References 31 publications

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

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

Swept source optical coherence tomography to early detect multiple sclerosis disease. The use of machine learning techniques

Anwendungsmöglichkeiten von „Künstlicher Intelligenz“ und „Big Data“ in der ophthalmologischen Diagnostik

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