A large number of human retinal diseases are characterized by a progressive loss of cones, the photoreceptors critical for visual acuity and color perception. Adaptive Optics (AO) imaging presents a potential method to study these cells in vivo. However, AO imaging in ophthalmology is a relatively new phenomenon and quantitative analysis of these images remains difficult and tedious using manual methods. This paper illustrates a novel semi-automated quantitative technique enabling registration of AO images to macular landmarks, cone counting and its radius quantification at specified distances from the foveal center. The new cone counting approach employs the circle Hough transform (cHT) and is compared to automated counting methods, as well as arbitrated manual cone identification. We explore the impact of varying the circle detection parameter on the validity of cHT cone counting and discuss the potential role of using this algorithm in detecting both cones and rods separately.

PurposeWe compared cone density measurements derived from the center of gaze-directed single images with reconstructed wide-field montages using the rtx1 adaptive optics (AO) retinal camera.MethodsA total of 29 eyes from 29 healthy subjects were imaged with the rtx1 camera. Of 20 overlapping AO images acquired, 12 (at 3.2°, 5°, and 7°) were used for calculating gaze-directed cone densities. Wide-field AO montages were reconstructed and cone densities were measured at the corresponding 12 loci as determined by field projection relative to the foveal center aligned to the foveal dip on optical coherence tomography. Limits of agreement in cone density measurement between single AO images and wide-field AO montages were calculated.ResultsCone density measurements failed in 1 or more gaze directions or retinal loci in up to 58% and 33% of the subjects using single AO images or wide-field AO montage, respectively. Although there were no significant overall differences between cone densities derived from single AO images and wide-field AO montages at any of the 12 gazes and locations (P = 0.01–0.65), the limits of agreement between the two methods ranged from as narrow as −2200 to +2600, to as wide as −4200 to +3800 cones/mm2.ConclusionsCone density measurement using the rtx1 AO camera is feasible using both methods. Local variation in image quality and altered visibility of cones after generating montages may contribute to the discrepancies.Translational RelevanceCone densities from single AO images are not interchangeable with wide-field montage derived–measurements.

Background:  The minimum clinically important difference (MCID) is the smallest difference in outcome between the groups that would be of clinical interest. It influences the estimates that are made to determine the required sample side. The aim of this study was to explore the reporting of the MCID in surgical trials. Method:  Surgical trials that were published between January 1981 and December 2006 in five prestigious surgical journals were evaluated. Selected for study were trials that studied two groups and reported the main outcome event as a proportion. Results:  Only 21% (100/486) of the admissible surgical trials mentioned a value for the MCID when estimating the sample size. There was a trend, however, for compliance with these factors to increase during the study period. The present post‐hoc calculations of the required sample size, which were based on the observed differences between the groups at the end of the study, suggested that one‐third of the trials should have accrued at least fivefold the number of patients. Although reporting an estimate of the sample size was associated with the study of more patients (median sample size 145 vs 100), it was not associated with the reporting of more positive results, that is, 61% (95/155) versus 65% (214/331). Conclusion:  There has been an improvement in the proportion of surgical trials reporting formal estimates of sample size during the last three decades. But the construct of these estimates is often suspect because of a failure to provide realistic values for the MCID.