Self-Calibrating Intelligent OCT-SLO System

A unique sample-independent 3D self-calibration methodology is tested on a unique optical coherence tomography and multi-spectral scanning laser ophthalmoscope (OCT-SLO) hybrid system. Operators’ visual cognition is replaced by computer vision using the proposed novel fully automatic AI-driven system design. Sample-specific automatic contrast adjustment/focusing of the beam is achieved on the pre-instructed region of interest. The AI model dedicates infrared, fluorescence, and visual spectrum optical alignment by quantitatively estimating pre-instructed features. The tested approach, however, is flexible enough to utilize any apt AI model. Relative comparison with classical signal-to-noise-driven automation is shown to be ∼200% inferior and ∼130% slower than the AI-driven approach. The best spatial resolution of the system is found to be: (a) 2.41 microns in glass bead eye phantom, 0.76 $ \pm $ 0.46 microns in the mouse retina in the axial direction, and (b) better than 228-line pair per millimeter (lp/mm) or 2 microns for all three spectrums, i.e., 488 nm, 840 nm, and 520–550 nm emission in coronal/frontal/x-y plane. Intelligent automation reduces the possibility of developing cold cataracts (especially in mouse imaging) and patient-associated discomfort due to delay during manual alignment by facilitating easy handling for swift ocular imaging and better accuracy. The automatic novel tabletop compact system provides true functional 3D images in three different spectrums for dynamic sample profiles. This is especially useful for photodynamic imaging treatment.