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

Kenney, Rachel; Liu, Mengling; Hasanaj, Lisena; Joseph, Binu; Al-Hassan, Abdullah Abu; Balk, Lisanne J.; Behbehani, Raed; Brandt, Alexander U.; Calabresi, Peter A.; Frohman, Elliot M.; Frohman, Teresa C.; Havla, Joachim; Hemmer, Bernhard; Jiang, Hong; Knier, Benjamin; Korn, Thomas; Leocani, Letizia; Martínez-Lapiscina, Elena H.; Papadopoulou, Athina; Paul, Friedemann; Petzold, Axel; Pisa, Marco; Villoslada, Pablo; Zimmermann, Hanna; Thorpe, Lorna E.; Ishikawa, Hiroshi; Schuman, Joel S.; Wollstein, Gadi; Chen, Yu; Saidha, Shiv; Galetta, Steven; Balcer, Laura J.

doi:10.1212/wnl.0000000000200883

Cited by 28 publications

(31 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, Kenney et al [ 65 ] suggested using ML classification to set thresholds for OCT inter-eye differences (IEDs) to aid in MS diagnosis. They measured the peripapillary RNFL and ganglion cell + inner plexiform (GCIPL) thicknesses using spectral-domain optical coherence tomography (SD-OCT).…”

Section: Related Studiesmentioning

confidence: 99%

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

Aslam

Khan

Bashamakh

et al. 2022

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Related Studiesmentioning

confidence: 99%

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

Aslam

Khan

Bashamakh

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…32,34 Machine learning based classification of eyes with ON history utilizing standard OCT parameters has been promising. 35 However, not all ON lead to measurable neuroaxonal damage, and in a previous study about 15% of patients do not suffer relevant damage during an ON episode. 36 This limits the value of methods relying on absolute OCT parameter thickness or inter-eye differences.…”

Section: Discussionmentioning

confidence: 90%

“…Clinically meaningful cut‐off values may be defined in comparison to healthy controls 20 or based on large reference studies with MS‐derived OCT measures 32,34 . Machine learning based classification of eyes with ON history utilizing standard OCT parameters has been promising 35 . However, not all ON lead to measurable neuroaxonal damage, and in a previous study about 15% of patients do not suffer relevant damage during an ON episode 36 .…”

Section: Discussionmentioning

confidence: 99%

Prior optic neuritis detection on peripapillary ring scans using deep learning

Motamedi

Yadav

Kenney

et al. 2022

Ann Clin Transl Neurol

Self Cite

View full text Add to dashboard Cite

Background: The diagnosis of multiple sclerosis (MS) requires demyelinating events that are disseminated in time and space. Peripapillary retinal nerve fiber layer (pRNFL) thickness as measured by optical coherence tomography (OCT) distinguishes eyes with a prior history of acute optic neuritis (ON) and may provide evidence to support a demyelinating attack. Objective: To investigate whether a deep learning (DL)-based network can distinguish between eyes with prior ON and healthy control (HC) eyes using peripapillary ring scans. Methods: We included 1033 OCT scans from 415 healthy eyes (213 HC subjects) and 510 peripapillary ring scans from 164 eyes with prior acute ON (140 patients with MS). Data were split into 70% training, 15% validation, and 15% test data. We included 102 OCT scans from 80 healthy eyes (40 HC) and 61 scans from 40 ON eyes (31 MS patients) from an independent second center. Receiver operating characteristic curve analyses with area under the curve (AUC) were used to investigate performance. Results: We used a dilated residual convolutional neural network for the classification. The final network had an accuracy of 0.85 and an AUC of 0.86, whereas pRNFL only had an AUC of 0.77 in recognizing ON eyes. Using data from a second center, the network achieved an accuracy of 0.77 and an AUC of 0.90 compared to pRNFL, which had an AUC of 0.84. Interpretation: DL-based disease classification of prior ON is feasible and has the potential to outperform thickness-based classification of eyes with and without history of prior ON.

show abstract

“…The only study to date involving a large dataset has been recently conducted by Kenney et al [16]. The authors used Cirrus high-definition OCT and converted Spectralis HP-OCT measurements of the pRNFL and GCL+ layers.…”

Section: Machine Learning In the Diagnosis Of Ms Usable Solutionsmentioning

confidence: 99%

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

Hernández¹,

Ramon-Julvez²,

Viladés³

et al. 2023

Preprint

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 28 publications

References 16 publications

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

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

Prior optic neuritis detection on peripapillary ring scans using deep learning

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

Contact Info

Product

Resources

About