106 compared to diabetic patients and healthy subjects.21 However, healthy controls showed the same NFA compared to both sarcoidosis groups. Therefore, we believe that NFA cannot be used as diagnostic criterion for distinguishing patients with sarcoidosis with and without SFN. Previous research described the relationship between CNFL and NFA using an exponential formula. We reproduced this formula based on our own data and compared it with the formula from the other study. With CNFL measured by ACCMetrics, a similar relationship could be described with a shift towards higher values. Compared to the previous study21, sarcoidosis measurements from our study were limited to the central part of their curve. In addition, the previous study included subjects with diabetes (distributed in the left lower part of the curve) and healthy subjects (distributed in the right upper part). The lack of diabetic patients in our dataset could partially explain the shift in our curve. Moreover, the fact that sarcoidosis patients already showed wider nerve fibers could be another explanation.21 The relationship between CNFL and NFA, described by the formulas based on CCMetrics and NeuronJ, unexpectedly showed a root relationship instead of an exponential relationship. Therefore, the previously provided formula was not generally applicable to any software program with CNFL as output. This could not be explained by physiological causes as the same population was used. As a result, this must be explained by technical differences. Despite the excellent correlation between the methods, a combination of selection bias and a small underestimation of the automatic analysis was thought to be responsible for distorting a fair comparison. Limitations of these methods were that only CNFL and NFA could be compared using different analysis systems and that CNFL was sometimes underestimated. CNFD and CNBD were not calculated with NeuronJ and therefore could not be compared with other analysis methods. Defining the density and branches required more complicated algorithms. The other limitation was that CNFL may have been underestimated if the quality of the images was not optimal. The outer edges of CCM images were sometimes blurred, which directly affected the CNFL. In addition, pressure lines sometimes interrupted the nerves when the patient’s cornea was difficult to photograph, although this was avoided as much as possible. This could result in a lower CNFL. To attenuate this limitation, decreased CNFL was observed in a minority of 35% of participants and equally distributed across the three groups. Therefore, the effect of image quality on the results of this study was estimated as limited. For future studies, implementing a quality index could add value to identifying the effects of image quality.35 Strengths of this study were the unique study population and new insights regarding the added value of NFA compared to multiple image analysis techniques. This study included a large group of patients with the rare diseases sarcoidosis and small fiber neuropathy, while the majority of research with CCM to date has been applied on diabetic patients. Because sarcoidosis is a very complicated disease with heterogeneous presentations, it was important to investigate whether current CCM methods were applicable to this group. Furthermore, no study has previously directly compared these four analysis methods. This study clearly identified the relation between different image analysis techniques and helped understand the origin of the differences between these systems. Discrimination between SFN and no SFN could not be achieved in this study population based on CNFL or NFA. 111 6
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