## 4 Discussion  
IVCM is a non-invasive technique useful for the examination of the sub-basal nerve plexus [29]. Although the area observed in a single IVCM scan image may be too small to represent the whole corneal status, the analysis of these images and the objective measures that can be obtained from them have proven to be interesting biomarkers for different ocular disorders. In 2001, [PERSON] and [PERSON] [6] successfully applied IVCM to corneal nerve imaging. Since then, many authors have shown that several corneal nerve features are measurable from IVCM images and are related to both ocular and systemic diseases [7; 8; 9]. Among other features of nerve fibers, tortuosity has been outlined, since it can be used to interpret the degeneration and subsequent regeneration of nerves, which leads to active neural growth [9]. Many ocular disorders can be linked to the tortuosity of corneal nerve fibers. Particularly for DED, while there is contradictory information regarding some characteristics such as nerve density, including a decrease [15; 16], no change [46; 47] and an increase [48], increased tortuosity has been consistently related to this condition [15; 16; 47; 48; 49]. We therefore think that, among the different sub-basal corneal nerve measures, tortuosity could be one of the best parameters to evaluate corneal nerve health status. In recent years, the interest in corneal nerve research has been growing rapidly and the analysis of IVCM images, where sub-basal nerves appear well defined, has become a common practice. Surprisingly, the increasing use of IVCM in ophthalmic clinical practice and the advances in image analysis have not led to the proliferation of fully automated approaches that can facilitate quantitative corneal nerve analysis. Conventionally, corneal nerve abnormalities have been commonly identified through arduous manual or semi-automatic evaluation processes [50; 51; 52]. Such approaches result in time-consuming and subjective outcomes, with limited inter-rater reliability across observers [38]. As expected, in this type of tool, the operator's ability has a high impact on the accuracy of the measurements. In addition, manual and semiautomated software are inefficient when analyzing a high volume of IVCM images. Thus, the development of fully automated methods to quantify corneal nerve features from IVCM images is important in order to allow for an efficient and accurate evaluation of corneal nerve morphology. Although there are some automatic software developments, such as ACCMetrics (University of Manchester, Manchester, UK), an underestimation in their measurements compared to manual tools has been observed [40]. Regarding tortuosity, a possible reason for this discrepancy may be considering tortuosity at the single fiber level instead of at the whole image level, without taking into consideration that fibers with very different tortuosity patterns can be present in the same image [29]. The present research resulted in a fully automated tool for the quantification of corneal nerve tortuosity from IVCM images. The method computes the tortuosity at image level instead of fiber level, simplifying the tortuosity characterization to small length segments, rather than considering the wide variety of tortuosity patterns that can exhibit large fibers. To assess the ability of this new automatic analysis system, and to evaluate its superiority over the manual one commonly used, we recruited LASIK patients with DED symptoms, because DED patients as well as LASIK subjects usually show an altered sub-basal corneal nerve plexus [10; 53]. Thus, we used images with altered nerves instead of images from healthy subjects to further challenge the ability of this new automatic system. Moreover, we recruited two different groups of symptomatic LASIK patients (one group suffering from habitual DED symptoms and another also suffering from corneal pain), because we wanted to assess if the new automatic system could also detect differences in tortuosity among symptomatic LASIK patients.