Patients and Healthy Controls using pupillary response characteristics measured by pupillometry. Sara Samadzadeh1, Roya Abolfazli2, Babak Khorsand3, ...
Machine-learning approach identifies a pattern of alterations that can discriminate Relapsing-Remitting Multiple Sclerosis Patients and Healthy Controls using pupillary response characteristics measured by pupillometry Sara Samadzadeh1, Roya Abolfazli2, Babak Khorsand3, Javad Zahiri4, Seyed Shahriar Arab5, Siamak Najafinia6, Christian Morcinek1, Peter Rieckmann7* 4-Department of Biophysics, Bioinformatics and Computational Omics Lab (BioCOOL), Iran 5-Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran 6-Mechanical Engineering Department, Amirkabir University of Technology, 7-Specialist Clinic for Neurology, Medical Park LOIPL
Objective and Methods
Introduction Multiple sclerosis is an immune-mediated disease of the central nervous system (CNS). Chronic neuroinflammation and neurodegeneration are the pathomorphological hallmarks of the disease and give rise to a great variety of clinical symptoms [1]. One of the most common symptoms of MS is optic neuritis, which can cause a relative afferent pupillary defect. MS is also considered as a secondary cause, of central origin, for autonomic dysfunction. The most common autonomic symptoms in MS are disorders of micturition, impotence, sudomotor and gastrointestinal disturbances, orthostatic intolerance, sleep disorders as well as pupillary autonomic dysfunction [2]. The iris has been considered as ‘an ideal indicator of autonomic reflex activity.’ This is particularly true since the introduction of pupillometric techniques, which have been applied with success in many different pathologies. In MS there is autonomic dysfunction with a reduction of parasympathetic tone and a relative increase in sympathetic dilatator tone to the pupils [3]. De Seze et al. have shown the high frequency of pupillary abnormalities in MS, as 60% of the MS patients had abnormal values in one or more pupillometric parameters [4]. The objective of this study was to identify major features of pupillary light response by using machine learning technique which enables us to discriminate healthy cases from patients and, thereby, to realize the value of manual quantitative pupillometry for assessing relative afferent pupillary defects (RAPD) in Relapsing-Remitting Multiple Sclerosis (RRMS) patients.
NeurOptics® NPi™-100 Pupilometer
This study was carried out in cooperation between two hospitals (a Multicenter Study). Written informed consent from study participants were obtained. The sample population consisted of 90 healthy control and 182 RRMS patients that were consecutive nonselected patients diagnosed with MS according to the McDonald criteria (2010) . Pupillary parameters were obtained and other characteristics such as Physician-confirmed history of optic neuritis were also extracted from patients’ records. Of a cohort of 182 RRMS patients, pupillometry parameters of 100 randomly selected subjects equal to the size of healthy control were selected. For decreasing the side effect of selecting 100 random negative data, we make 100 datasets and train models each time with one of these datasets and report the mean of evaluation measures as a result. For partitioning the data into train and test, a 10-fold cross validation procedure is used. Data is partitioned into 10 equal parts. Each time nine partitions are used for training and one remaining partition is used for testing the model. Average of evaluation measures of the ten testing sets are reported as final. We have used seven based learners including linear, poly and radial SVM, Random Forest (RF), Decision and Cart tree, K Nearest Neighbors (KNN). For combining the results of the based learners, Ensemble learning is used. In Ensemble learning we use two different methods for predicting the results: • Majority voting: The most frequent prediction is reported as final prediction. • Meta learner: Sending the results of all models as an input of a meta-learner and report the prediction of meta-learner as the final result. For detecting the most important features, in tree models, the percentage of training samples fallen into all terminal nodes is reported. The first split has the most score, and the score of features is reduced in lower level. In rule-based models, the number of rules determines the score of each predictor. Moreover, by using Principal Component Analysis (PCA), we use the sum of the loadings of PC1, PC2, and PC3 to detect most important features.
Results Several models were constructed by different classifiers. Among them, as is shown in Figure 1, Linear SVM with 0.76, Random Forest with 0.71 and Decision tree with 0.65 have the highest sensitivity score. Poly SVM with 0.68, Radial SVM with 0.63 and Cart Decision tree with 0.58 have the most top specificity score. Poly SVM with 0.67, Linear SVM with 0.65 and Radial SVM with 0.62 have the highest accuracy score.
Figure 1 : Evaluation Measures
1-Department of Neurology, Academic Hospital Sozialstiftung Bamberg, 2-Amiralam Hospital-Tehran University of Medical Sciences, 3-Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
EPO1095
Majority voting on the base learners’ decision has been used to make the final decision about each sample. This ensemble learning method achieved the sensitivity, specificity, and accuracy of 0.85, 0.78, and 0.77. In our work, Feature importance is calculated by five different models, sum of the scores of features is shown that among all pupillary response features as is shown in Figure 2, Constriction Velocity (CV), Maximum of Constriction Velocity (MCV), Dilation Velocity (DV), Latency and discrepancy between two eyes in neurological pupil index (Npi) were more discriminative than other features.
Table 1. Description of measurement parameters Parameter Unit NPI (Neurological Scalar value 0-5 Pupil Index)
NPI 200
MAX/MIN
mm
%CH
%
LAT
seconds
CV/MCV
mm/sec
DV
mm/sec
Calculation Algorithm of all measured variables giving a composite score of a pupillary response in comparison to a normal model MAX : Initial resting pupil size MIN : Pupil size at peak of the constriction Constriction % or Percentage Change ( [MAX-MIN] / MAX)
Figure 2 : Important Features
Demographics and Clinical Characteristics
Latency : time difference between Initiation of retinal light stimulation and onset of Pupillary constriction Average Constriction Velocity (CV) : amount of the constriction divided by duration of constriction Maximum Constriction Velocity (MCV) : peak value of velocity during constriction Average Dilation Velocity (DV) : amount of pupil size recovery (after the constriction) divided by duration of recovery
Figure 1. The NeurOptics® NPi™-100 Pupilometer is a non-invasive, handheld tool that records a video of pupils before, during and after a light stimulus, and therefore captures the pupillary reactivity to light (6). Each frame of the video is automatically processed to detect borders of iris and pupil as well as its variation over time. All variables representing the pupil dynamics and the Npi parameter which is a composite score based on a multidimensional, normative model are recorded for each subjects.
References [1] Zettl UK, Stuve O, Patejdl R. Immune-mediated CNS diseases: a review on nosological classification and clinical features. Autoimmun Rev 2012;11: 167–73. [2] de Seze J, Arndt C, Stojkovic T,Ayachi M, Gauvrit JY, Bughin M, Saint Michel T, Pruvo JP,Hache JC,Vermersch P (2001) Pupillary disturbances in multiple sclerosis: correlation with MRI findings. J Neurol Sci 188:37–41 [3] Pozzessere G, Rossi P,Valle E, Froio CP, Petrucci AF,Morocutti C (1997) Autonomic involvement in multiple sclerosis:a pupillometric study. Clin Auton Res 7:315–319 [4] Chen JW, Gombart ZJ, Rogers S, Gardiner SK, Cecil S, Bullock RM. Pupillary reactivity as an early indicator of increased intracranial pressure: The introduction of the neurological pupil index. Surg Neurol Int 2011;2:82.
Key Findings and Conclusion We observed that applying machine learning technique to pupillometry data has potential to yield better discrimination of group differences. We can draw a conclusion that pupillary response features of pupil reflex velocity and initial latency are altered significantly under the course of MS. Our prospective study provides solid data that manual pupilometer (NeurOptics®NPi200TM) is an easy to use, highly reproducible new technique to quantify the visual pathway function in patients with MS.
Disclosure This study was supported by research funds from the Free State of Bavaria, Germany .
