Cone photoreceptor quantity and distribution in human eyes: patterns, determinants and measurement frameworks

Cone photoreceptor density is a key quantitative marker of healthy macular microstructure and retinal remodeling related to refractive status and disease. However, absolute density estimates at similar eccentricities remain difficult to compare across studies. We conducted a scoping review of 57 in vivo studies of cone density in healthy adults. Using a three-domain framework comprising Participant/Ocular Factors, Imaging Geometry/Optics, and Analysis Methods/Algorithms, we examined how participant and ocular characteristics, imaging geometry and optics, and analysis methods and algorithms influence cone visibility, detectability, and countability. We synthesized spatial topography, differences among retinal directions, and sources of cross-study dispersion. Cone density was associated with age, refractive status, and axial length, and was further shaped by center definition, scale conversion, optical conditions, frame averaging, image-quality assessment, sampling-window design, counting algorithms, and manual correction rules. Absolute density estimates varied substantially even at similar eccentricities and retinal directions, with the largest dispersion near the fovea, where spatial gradients are steepest. After harmonizing coordinate systems, directional conventions, and spatial units, however, cone topography across retinal directions was highly consistent. Cone density declined continuously with eccentricity in all four cardinal directions, showing stable radial decay; within 0.5–6°, separate exponential models for the four cardinal directions showed robust fits (R 2 = 0.899–0.950). Healthy adult cone topography is therefore reproducible and parameterizable, but reliable cross-study comparison requires complete reporting of participant and ocular characteristics, imaging geometry and optical conditions, and analysis methods and algorithms. A minimum reporting and reference framework for the visibility–detectability–countability chain is essential for translating cone density into a comparable structural outcome measure.