Accurate detection of cerebellar smooth pursuit eye movement abnormalities via mobile phone video and machine learning.
Accurate detection of cerebellar smooth pursuit eye movement abnormalities via mobile phone video and machine learning.
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DOI：
10.1038/s41598-020-75661-x
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发表时间：
2020-10-29
影响因子：
4.6
通讯作者：
Gupta AS
中科院分区：
文献类型：
--
作者：
Chang Z;Chen Z;Stephen CD;Schmahmann JD;Wu HT;Sapiro G;Gupta AS
眼球运动在许多神经退行性疾病中被破坏，并且在影响小脑的疾病中是常见的和早期的特征。表征眼球运动对于诊断很重要，并且可能有助于跟踪疾病进展和对治疗的反应。评估是有限的，因为它们需要由神经科专科医生或专门和昂贵的设备进行亲自评估。我们检验了小脑疾病中重要的眼球运动异常（即，共济失调）可以从iPhone视频中捕捉到。从共济失调患者（n = 102）和对照人群（帕金森病或健康参与者，n = 61）中收集面部视频。利用计算机视觉算法跟踪人眼的位置，并将其转换为高时间分辨率的光谱特征。在眼动特征上训练的机器学习模型能够识别平滑追踪（一种关键的眼睛行为）的异常，并准确区分异常追踪的个体和对照组（灵敏度= 0.84，特异性= 0.77）。一种新的机器学习方法生成的严重程度估计值与临床医生评分相关性良好。我们证明了使用廉价且广泛使用的技术捕获眼动信息的可行性。这可能是一种有用的方法，用于疾病筛查和临床试验中测量严重程度。
Eye movements are disrupted in many neurodegenerative diseases and are frequent and early features in conditions affecting the cerebellum. Characterizing eye movements is important for diagnosis and may be useful for tracking disease progression and response to therapies. Assessments are limited as they require an in-person evaluation by a neurology subspecialist or specialized and expensive equipment. We tested the hypothesis that important eye movement abnormalities in cerebellar disorders (i.e., ataxias) could be captured from iPhone video. Videos of the face were collected from individuals with ataxia (n = 102) and from a comparative population (Parkinson’s disease or healthy participants, n = 61). Computer vision algorithms were used to track the position of the eye which was transformed into high temporal resolution spectral features. Machine learning models trained on eye movement features were able to identify abnormalities in smooth pursuit (a key eye behavior) and accurately distinguish individuals with abnormal pursuit from controls (sensitivity = 0.84, specificity = 0.77). A novel machine learning approach generated severity estimates that correlated well with the clinician scores. We demonstrate the feasibility of capturing eye movement information using an inexpensive and widely accessible technology. This may be a useful approach for disease screening and for measuring severity in clinical trials.
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影响因子：
3.4
作者：
Pretegiani E;Optican LM
通讯作者：
Optican LM
影响因子：
6
作者：
Vogel, Adam P.;Rommel, Natalie;Synofzik, Matthis
通讯作者：
Synofzik, Matthis
DOI：
10.1038/nrn.2017.92
发表时间：
2017-10
期刊：
Nature reviews. Neuroscience
影响因子：
--
作者：
Paulson HL;Shakkottai VG;Clark HB;Orr HT
通讯作者：
Orr HT
影响因子：
--
作者：
Christova, Peka;Anderson, John H.;Gomez, Christopher M.
通讯作者：
Gomez, Christopher M.
影响因子：
48
作者：
Jacobi, Heike;du Montcel, Sophie Tezenas;Klockgether, Thomas
通讯作者：
Klockgether, Thomas