Reflection mode Terahertz (THz) imaging of corneal tissue water content (CTWC) is a proposed method for early, accurate detection and study of corneal diseases. Despite promising results from and cornea studies, interpretation of the reflectivity data is confounded by the contact between corneal tissue and dielectric windows used to flatten the imaging field. Herein, we present an optical design for non-contact THz imaging of cornea. A beam scanning methodology performs angular, normal incidence sweeps of a focused beam over the corneal surface while keeping the source, detector, and patient stationary. A quasioptical analysis method is developed to analyze the theoretical resolution and imaging field intensity profile. These results are compared to the electric field distribution computed with a physical optics analysis code. Imaging experiments validate the optical theories behind the design and suggest that quasioptical methods are sufficient for designing of THz corneal imaging systems. Successful imaging operations support the feasibility of non-contact imaging. We believe that this optical system design will enable the first, clinically relevant, exploration of CTWC using THz technology.

Terahertz (THz) imaging of corneal tissue water content (CTWC) is a proposed method for early, accurate detection and study of corneal diseases. Despite promising results from and cornea studies, interpretation of the reflectivity data is confounded by the contact between corneal tissue and rigid dielectric window used to flatten the imaging field. This work develops a novel imaging system and image reconstruction methods specifically for nearly spherical targets such as human cornea. A prototype system was constructed using a 650 GHz multiplier source and Schottky diode detector. Resolution and imaging field strength measurement from characterization targets correlate well with those predicted by the quasioptical theory and physical optics analysis. Imaging experiments with corneal phantoms and corneas demonstrate the hydration sensitivity of the imaging system and reliable measurement of CTWC. We present successful acquisition of non-contact THz images of human cornea, and discuss strategies for optimizing the imaging system design for clinical use.

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Artificial intelligence (AI) is expected to cause significant medical quality enhancements and cost-saving improvements in ophthalmology. Although there has been a rapid growth of studies on AI in the recent years, real-world adoption of AI is still rare. One reason may be because the data derived from economic evaluations of AI in health care, which policy makers used for adopting new technology, have been fragmented and scarce. Most data on economics of AI in ophthalmology are from diabetic retinopathy (DR) screening. Few studies classified costs of AI software, which has been considered as a medical device, into direct medical costs. These costs of AI are composed of initial and maintenance costs. The initial costs may include investment in research and development, and costs for validation of different datasets. Meanwhile, the maintenance costs include costs for algorithms upgrade and hardware maintenance in the long run. The cost of AI should be balanced between manufacturing price and reimbursements since it may pose significant challenges and barriers to providers. Evidence from cost-effectiveness analyses showed that AI, either standalone or used with humans, was more cost-effective than manual DR screening. Notably, economic evaluation of AI for DR screening can be used as a model for AI to other ophthalmic diseases.