  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 of the beam is achieved
on the pre-instructed region of interest. The AI model deduces infrared,
fluorescence, and visual spectrum optical alignment by estimating
pre-instructed features quantitatively. 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 percent inferior and 130
percent 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 with STD
0.46 microns in the mouse retina in the axial direction, and (b) better than
228 line pair per millimeter (lp per mm) or 2 microns for all three spectrums,
i.e., 488 nm, 840 nm, and 520 to 550 nm emission in coronal, frontal or 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.
