Systems and methods for AI-gating photomodulation
View Patent ↗An AI-controlled photonic system that performs therapeutic photomodulation and reflective optical diagnostics, including OCT, OCTA, near-infrared reflectance, and fundus autofluorescence, is described here. The system incorporates an AI-Predictive Gating module that identifies optimal temporal windows for energy delivery or data acquisition based on reflectance dynamics, retinal motion, physiologic parameters, mitochondrial biomarkers, and image-quality metrics. By synchronizing photonic emission and diagnostic capture to predicted physiologic states, the system improves diagnostic fidelity, enhances therapeutic precision, and enables earlier detection of degenerative, inflammatory, and pharmacologic retinal injury.
1 . A system for AI-Gating photonic interaction with ocular tissue, comprising:
at least one optical modality that acquires real-time input signals from the ocular tissue;
one or more processors that receive the real-time input signals and store the real-time input signals as historical input signals;
a predictive model executed by the one or more processors that forecasts a future interval of physiologic stability of the ocular tissue based on temporal patterns in the real-time input signals and the historical input signals, where the physiologic stability exhibits transient plateau behavior; and
an AI-Gating engine executed by the one or more processors that generates a gating signal that regulates acquisition only during the future interval, wherein the gating signal optimizes operation of the at least one optical modality.
2 . The system of claim 1 , wherein the AI-Gating engine is an external supervisory controller connected to the at least one optical modality through a digital trigger line.
3 . The system of claim 1 , wherein the real-time input signals and the historical input signals include reflectance signals.
4 . The system of claim 1 , wherein the predictive model analyzes temporal patterns to identify a stability interval defined by reflectance coherence plateau behavior.
5 . The system of claim 1 , wherein the AI-Gating engine suppresses acquisition during predicted instability intervals.
6 . The system of claim 1 , wherein post-acquisition outcomes update the predictive model over time.
7 . The system of claim 1 , wherein the gating signal controls delivery of photomodulation energy.
8 . The system of claim 1 , wherein prediction is based on stabilization of autofluorescence intensity.
9 . The system of claim 1 , wherein at least one modality is directly gated and at least one modality is passively synchronized.
10 . The system of claim 1 , wherein the AI-Gating engine receives preview data without full-resolution imaging transfer.
11 . The system of claim 1 , wherein the ocular tissue includes a retina.
12 . The system of claim 1 , wherein the AI-Gating engine is also configured to generate a second gating signal that regulates photonic output.
13 . A system for AI-Gating control of photonic delivery, comprising:
at least one optical modality that acquires real-time input signals from ocular tissue;
one or more processors that receive the real-time input signals and store the real-time input signals as historical input signals;
a predictive model executed by the one or more processors to forecast a future interval of physiologic stability of the ocular tissue based on temporal patterns in the real-time input signals and the historical input signals, where the physiologic stability exhibits transient plateau behavior; and
an AI-Gating engine executed by the one or more processors that generates a gating signal that regulates photonic output only during the future interval, wherein the gating signal optimizes biologic receptivity or safety of the photonic delivery.
14 . The system of claim 13 , wherein the real-time input signals include fixation stability.
15 . The system of claim 13 , further comprising two or more optical modalities, wherein the AI-Gating engine synchronizes activation of the two or more optical modalities within the future interval.
16 . The system of claim 15 , wherein the two or more optical modalities include at least two of optical coherence tomography, optical coherence tomography angiography, fundus autofluorescence, hyperspectral imaging, near-infrared reflectance, or Raman spectroscopy.
17 . The system of claim 13 , wherein the photonic delivery includes pulsed light output.
18 . The system of claim 13 , wherein the predictive model analyzes temporal patterns to identify a stability interval defined by metabolic quieting.
19 . The system of claim 13 , wherein the AI-Gating engine is also configured to generate a second gating signal that regulates acquisition.
20 . A method for operating an ophthalmic imaging system, comprising:
receiving real-time optical signals from ocular tissue;
analyzing the real-time optical signals together with historical data to predict a future temporal interval of physiologic stability wherein forecasting is based on physiologic signals exhibiting transient plateau behavior;
generating a gating signal based on the future temporal interval; and
regulating delivery only during the future temporal interval to improve effective diagnostic performance.
21 . The method of claim 20 , where the regulating includes delaying imaging acquisition.
22 . The method of claim 20 , wherein the regulating includes modulating imaging acquisition.
23 . The method of claim 20 , wherein the gating signal coordinates multimodal acquisition within a shared stability window.
24 . The method of claim 20 , wherein closed-loop refinement occurs across multiple clinical sessions.
25 . The method of claim 20 , wherein a proportion of diagnostically usable frames is increased by restricting acquisition to the future temporal interval.
26 . The method of claim 20 , wherein passive synchronization aligns ungated modalities to a timing of a gated modality.
27 . The method of claim 20 , wherein the ophthalmic imaging system is applied for diagnosis and monitoring of a disease and physiologic condition characterized by time-dependent variability in ophthalmic biomarkers, including optical, metabolic, vascular, inflammatory, neurodegenerative, and biomechanical biomarkers, the ophthalmic imaging system configured to detect inflammatory, metabolic, vascular, neurodegenerative, toxic, degenerative, and neoplastic conditions, including diabetes, glaucoma, keratoconus, macular degeneration, geographic atrophy, central serous chorioretinopathy, uveitis, optic neuropathy, retinal vascular disease, inherited retinal disease, retinal dystrophy, ocular tumors, intraocular neoplasia, toxic maculopathy, mitochondrial disorders, Alzheimer's disease, and Parkinson's disease.
28 . A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the instructions to:
receive at least one of real-time ocular optical inputs and real-time ocular physiologic inputs;
forecast a future interval of stability using a predictive model, where the stability exhibits transient plateau behavior; and
issue, through an AI-Gating engine, a gating signal that controls photonic activation only during the future interval.
29 . The instructions of claim 28 , wherein the photonic activation is permitted only during predicted metabolic receptivity.