DRY EYE DISEASE (DED) is a multifaceted condition that is challenging to diagnose due to its variable presentation and complex pathophysiology. Reliance on traditional tools like slit lamp assessment, vital dyes, fluorescein tear break-up time (TBUT), Schirmer’s test, and ocular surface staining is common but often limited by poor reproducibility and inadequate correlation with patient symptoms (Sullivan et al, 2014). Thankfully, technological advancements are significantly enhancing the objectivity, repeatability, and efficiency of DED evaluations.
Tear Film and Surface Analysis
Noninvasive tear breakup time (NIBUT) objectively assesses tear film stability by analyzing distortions in reflected light patterns without the need for dyes. The Tear Film & Ocular Surface Society (TFOS) considers NIBUT superior to fluorescein TBUT for DED diagnosis, recommending a cutoff of < 10 seconds compared to TBUT’s < 5 seconds. This method eliminates variability from dye introduction and subjective observation (Wolffsohn et al, 2025).
Tear osmolarity measures tear salt concentration, a hallmark of DED when elevated. Values exceeding 308 mOsm/L, or an intereye difference exceeding 8 mOsm/L, are indicative of tear film instability and remain part of the TFOS Dry Eye Workshop (DEWS) III diagnostic algorithm (Wolffsohn et al, 2025).
Inflammatory biomarkers like cytokines and matrix metalloproteinases (MMPs) are indicators of ocular surface inflammation. A commercially available point-of-care test measures MMP-9 levels; it displays a positive result (pink/red line) when exceeding 40 µg/mL and a negative result (blue line) otherwise. This test is useful for identifying inflammation, but may be less sensitive to mild to moderate DED, potentially leading to false negatives (Mejía-Salgado et al, 2025).
Interferometry offers a non-invasive way to analyze the lipid layer thickness (LLT) of the tear film, which is crucial for preventing excessive evaporation. A low LLT correlates with meibomian gland dysfunction and, therefore, is a potential indicator of evaporative DED (Li et al, 2020).
Meibography allows clinicians to visualize the meibomian glands and assess their structure. This detailed imaging helps them identify signs of gland atrophy or obstruction, providing crucial information for diagnosing and managing evaporative DED.
High-resolution slit lamp and infrared ocular surface photography documents conjunctival staining, lid margin irregularity, and tear film abnormalities.
Thermography uses a camera to detect infrared radiation emitted from the ocular surface, mapping changes in the ocular surface temperature that are presumed to be caused by tear fluid evaporation (Persiya and Sasithradevi, 2024). Changes in ocular surface temperature, particularly over the inter-blink period, may be indicative of tear film deficiencies (Shah and Galor, 2021).
Emerging Technology
Artificial intelligence (AI) algorithms are being developed to automate interpretation of meibography, NIBUT, and interferometry images, reducing examiner variability and enhancing diagnostic consistency. Additionally, wearable tear sensors and smart contact lenses capable of monitoring tear film biomarkers in real time are under active development (Rajan et al, 2024). While AI in DED is still in its early stages, it portends a compelling future for more precise, efficient, and personalized care.
Technology is transforming DED diagnosis from a subjective process into a data-driven specialty. Combining objective measurements with clinical expertise and patient symptomology allows eyecare providers to better understand each patient’s specific DED mechanisms. This leads to more targeted treatments, improved patient outcomes, and a better quality of life. The ongoing evolution of technology promises even more sophisticated diagnostic tools, further advancing DED management.
References
1. Sullivan BD, Crews LA, Messmer EM, et al. Correlations between commonly used objective signs and symptoms for the diagnosis of dry eye disease: clinical implications. Acta Ophthalmol. 2014;92(2):161-166.
2. Wolffsohn JS, Benítez-Del-Castillo J, Loya-Garcia D, et al; TFOS Collaborator Group. TFOS DEWS III Diagnostic Methodology. Am J Ophthalmol. 2025 May 30:S0002-9394(25)00275-2. [Online ahead of print] doi: 10.1016/j.ajo.2025.05.033
3. Mejía-Salgado G, Rojas-Carabali W, Cifuentes-González C, et al. Real-world performance of the inflammadry test in dry eye diagnosis: an analysis of 1,515 patients. Graefes Arch Clin Exp Ophthalmol. 2025 Mar 5:1-9. doi: 10.1007/s00417-025-06760-6
4. Li J, Ma J, Hu M, Yu J, Zhao Y. Assessment of tear film lipid layer thickness in patients with Meibomian gland dysfunction at different ages. BMC Ophthalmol. 2020 Oct 6;20(1):394. doi: 10.1186/s12886-020-01667-8
5. Persiya J, Sasithradevi A. Thermal mapping the eye: A critical review of advances in infrared imaging for disease detection. J Therm Biol. 2024;121:103867. doi: 10.1016/j.jtherbio.2024.103867
6. Shah AM, Galor A. Impact of ocular surface temperature on tear characteristics: current insights. Clin Optom (Auckl). 2021;13:51-62. doi: 10.2147/OPTO.S281601
7. Rajan A, Vishnu J, Shankar B. Tear-based ocular wearable biosensors for human health monitoring. Biosensors. 2024 Oct 8;14(10):483. doi: 10.3390/bios14100483