2. Current Insights
In this entry, we conducted a bibliometric analysis of studies investigating eye tracking in different medical conditions. We observed a substantial growth of the use of this technology in the last few years and identified strong collaboration networks among the countries with the highest scientific production (USA, UK and Germany). This substantial growth might be explained by the increased availability of eye tracking systems at different price ranges as well as by open-source data acquisition software. Based on the most frequent keywords, autism spectrum disorders represented the most investigated condition, followed by other psychiatric disorders (schizophrenia and depression) and neurodegenerative disorders (Parkinson’s disease and Alzheimer’s disease). Different causes might explain the observed relevance of eye tracking technology for research on autism spectrum disorders. First, autism represents a neurodevelopmental disorder and eye tracking is a non-invasive technique that can be applied to studies including infants and children. Second, different studies support the hypothesis that patients with this condition focus their attention on different types of stimuli compared to typically developing individuals, especially in the case of stimuli related to social interactions. Indeed, an analysis of the most cited articles retrieved in our search and related to autism identified social interactions and face processing as the most investigated field. The majority of these articles used eye tracking to investigate differences in gaze patterns during vision exposure to social stimuli, such as faces or social events, either in patients with autism [
27,
28,
29,
30,
31,
32,
33,
34,
35,
37,
38,
40,
41,
42,
43], in their siblings [
26,
36] or participants at risk of autism [
39] compared with typically developing controls. Some of these studies combined eye tracking with additional techniques such as MRI in order to investigate whether the identified differences in gaze patterns were associated with neurobiological abnormalities [
26,
27,
45]. In the most cited article, Dalton and colleagues showed that patients with autism spent less time looking at the eye region when presented with photographs of human faces compared to typically developing controls [
27]. The authors showed that the amount of time spent observing the eye region positively correlated with the activation of a specific brain area (fusiform gyrus), measured with MRI. In a subsequent study, the authors also showed similar findings in unaffected siblings of patients with autism spectrum disorders compared with controls [
26]. Abnormalities identified using eye tracking might also be associated with the increased severity of illness as shown by Jones and colleagues in a study including 2-year-old children with autism [
28]. In this study, children with autism spent less time looking at the eyes and more time looking at the mouths of people pictured in videos compared with typically developing children or children with a developmental delay different from autism. Importantly, children with autism showing a low number of fixations located on eyes also showed higher levels of social disability [
28].
Our analysis of trend topics supported the relevance of autism spectrum disorders as a central theme and identified “deep learning” as the niche theme that is mostly reported in recent articles. This finding might be explained by the fact that, in the last few years, a number of studies have started to explore the utility of eye tracking markers to improve the diagnosis of autism spectrum disorders. While different pipelines to analyze eye tracking data are available, increasing attention is now focused on the development of visual attention models using machine learning methods [
46,
47,
48,
49,
50]. Since the diagnosis of autism is challenging and no biomarker is available [
51], the development of computational models based on early abnormalities such as the differences in gaze processing might be of substantial help to improve and anticipate the diagnosis, thus, making it possible to initiate treatment at an earlier stage, when it is most effective [
52]. Eye tracking measurements that might prove to be useful as early biomarkers include dysregulations in pupil dilation [
53,
54,
55], changes in saccadic behavior, differences in gaze patterns during vision exposure to social stimuli [
56,
57,
58] and analysis of scan paths or gaze patterns [
59,
60,
61,
62,
63,
64]. Some studies combined eye tracking data with other measurements such as resting-state EEG data [
65,
66,
67]. Using this approach, Kang and colleagues showed a classification accuracy of up to 85.44% using a support vector machine (SVM) classifier to discriminate children aged from 3 to 6 with autism from controls [
65]. Even higher performances were shown by another study conducted by Li and colleagues, using the three-layer Long Short-Term Memory (LSTM) network to discriminate 136 children with autism compared with 136 typically developing children based on gaze patterns during observations of 272 videos [
68]. In this study, accuracy improved from 86.4% to 92.6% using LSTM compared to SVM [
68]. Overall, results from these studies appear to be promising, although further research will be needed to evaluate their potential utility in the clinical setting and better assess the role of potential confounding factors that might affect gaze patterns (such as age, disease severity and duration, cognitive functioning and so on). Similarly, eye tracking measurements might be useful as prognostic markers [
69] or to evaluate the efficacy of different types of interventions [
70,
71,
72,
73].
While we conducted a comprehensive analysis with no date and language restrictions, findings from our article must be interpreted in light of some limitations. First, our search might have missed some articles in which the terms included in our search strategy were mentioned in the main text but not in the title, abstract or keywords. In addition, our search might include some articles that mentioned the terms included in our search strategy but in which eye tracking experiments were not conducted (for instance reviews incorrectly classified by WOS as research articles, or articles in which new analytical methods were proposed). Despite these limitations, our analysis of large numbers of documents provides an updated and comprehensive picture of the use of eye tracking to study different medical conditions and highlights relevant trends with regards to investigated disorders and specific applications.
In conclusion, the eye tracking technique is increasingly being used in the study of different medical conditions. In autism spectrum disorders, eye tracking is widely used to evaluate eye movement abnormalities during face processing or looking at other social stimuli. Studies developing machine learning models provided promising evidence to support the utility of eye tracking measurements as biomarkers to discriminate patients with autism spectrum disorders from typically developing controls.