Background: Dry eye disease (DED) is a multifactorial disease. Numerous risk factors might cause DED, including indoor air pollution, such as incense. Incense (Bakhoor) is widely used in many cultures, including Saudi Arabia, although its smoke contains toxic chemicals that pose serious health hazards. This research investigates the link between the Schirmer II test and tear fluid proteins in DED patients. The study focuses on identifying the ocular examinations, hypothesizing that incense smoke, particularly from synthetic types, exacerbates DED. Methods: This pilot cross-sectional study was conducted at King Abdulaziz Medical City (KAMC) in Jeddah, Saudi Arabia. Participants were recruited from the Cornea and Ophthalmology Clinics. Eye assessments analyzed tear protein concentrations, including tear collection using Schirmer II test strips and tear break-up time (TBUT). The study included DED patients who used incense. Tear fluid from the Schirmer test of 20 randomly selected patients was used for protein analysis of total protein, lactoferrin, and Immunoglobulin E. Inclusion criteria were male and female subjects aged 18 years or older, diagnosed with DED, and using incense. The sample size was 55 participants, selected via convenience sampling. Subjective data were collected through questionnaires, as well as objective data from the tear test and the sample and analyzed with SPSS. Descriptive and inferential statistics were used, with statistical significance set at p-value < 0.05. Results: The Ocular Comfort Index (OCI) categories showed that 21.8% had no symptoms, 40.0% had low symptoms, 30.9% had moderate symptoms, and 7.3% reported high symptoms. TBUT values and Schirmer test scores decreased with increasing OCI severity, with no statistical significance. The mean (SD) of total protein in the right and left eyes for high OCI was 7.19 (1.39) and 7.42 (0.91), respectively, with no statistical significance. The immunoglobulin E levels in the right and left eyes for high OCI were 301.71 (55.97) and 301.71 (47.14), respectively, with no statistical significance. The mean (SD) of lactoferrin in the right and left eyes for high OCI was 163.77 (10.42) and 159.43 (1.68), respectively, with no statistical significance. Conclusions: The study findings demonstrate alignment in incense-using patients between subjective OCI symptom scores and objective clinical diagnostic measures. Specifically, higher OCI scores are associated with lower TBUT and Schirmer II test values, as well as changes in tear biomarkers such as IgE and lactoferrin. These findings emphasize the potential of using simple screening methods combined with bioanalytical markers for early detection of ocular surface disease. This highlights the potential health risks associated with incense exposure, particularly for individuals predisposed to DED. The urgency for further research to explore the long-term effects of incense on ocular health and to raise awareness about its potential impact on populations with high incense usage cannot be overstated.

Background/Objectives: Glaucoma is a leading cause of irreversible blindness globally and must be detected early to preserve vision. Standard methods, such as automated perimetry and optical coherence tomography (OCT), are limited in their ability to detect early changes. Therefore, this systematic review evaluates the diagnostic and predictive performance of artificial intelligence (AI)-driven OCT-based models, including those based on deep learning, machine learning, and transformer architectures, and contextualizes their performance against the recognized limitations of standard automated perimetry and conventional OCT trend analysis. Methods: The PubMed, Ovid, and Google Scholar databases were comprehensively searched to identify relevant published research in English. Screening of observational, cohort, prospective, retrospective and diagnostic accuracy studies, as well as clinical trials, identified 13 studies. Their key outcomes, including sensitivity, specificity, and area under the curve (AUC), were extracted for synthesis. Results: AI-driven OCT-based models generally reported high diagnostic performance, with mean sensitivities and specificities exceeding 80%, as well as AUC values generally above 0.85. AI-driven OCT-based models were reported to detect glaucoma progression as early as 9 months before standard methods. Conclusions: The findings suggest that AI-driven OCT-based models may improve glaucoma detection by identifying subtle structural and functional changes earlier, which may help inform earlier intervention decisions. However, the majority of included studies relied on internal validation only, and prospective multicenter studies with external validation are required before AI-driven OCT-based models can be reliably implemented in routine glaucoma care.