Traditional liver assessments rely heavily on biopsies, where small tissue samples are extracted and examined for signs of disease or damage. While considered the gold standard, biopsies are inherently limited, providing only a microscopic snapshot of a vast organ that may vary in condition. This narrow sampling can cause uncertainty, sometimes leading to the discard of viable organs. The OU team sought to overcome these limitations by employing PS-OCT, which not only captures detailed cross-sectional images of liver tissue but also measures its polarization properties, revealing subtle structural and biochemical features linked to liver health.

The engineers designed a portable PS-OCT system optimized for clinical and operating room environments, allowing transplant teams to scan liver surfaces quickly and noninvasively. The imaging data are then analyzed using advanced artificial intelligence algorithms, co-developed with Dr. Chongle Pan’s computer science lab. These AI tools quantify critical parameters indicative of hepatic steatosis (fat accumulation) and inflammation, two primary factors influencing liver viability. By training their models on datasets from over thirty clinical cases—comparing PS-OCT results with biopsy outcomes and post-transplant organ function—the researchers demonstrated strong correlations between imaging biomarkers and actual clinical metrics.

Beyond providing a more holistic view of donor liver condition, PS-OCT could enhance decision-making speed, potentially expanding the pool of organs deemed suitable for transplantation by identifying healthy tissue regions missed by biopsy. Such improvements may reduce organ discard rates and improve transplant success. Encouraged by initial findings, the OU team plans a large multisite clinical trial involving institutions like UT Southwestern and the Cleveland Clinic to validate and refine this approach across diverse patient populations.

This work exemplifies OU’s interdisciplinary innovation platform that integrates optical bioimaging, machine learning, pathology, and organ perfusion technology to translate engineering advances directly into clinical practice. The researchers also intend to extend PS-OCT evaluation methods to other organs, including kidneys and lungs, widening the impact of this technology.

Supported by National Science Foundation grants and institutional partnerships, the team’s efforts mark a significant step toward redefining how transplanted organs are assessed. Dr. Tang emphasizes that this is just the beginning, envisioning a future where noninvasive, AI-enhanced imaging facilitates safer, faster, and more effective organ transplantation worldwide.

Subject of Research: Donor liver evaluation using advanced imaging techniques
Article Title: Human donor liver viability evaluation with polarization-sensitive optical coherence tomography
News Publication Date: 24-Jun-2026
Web References: http://dx.doi.org/10.1126/scitranslmed.adv7124
Image Credits: University of Oklahoma / Ellie Pinter

Keywords

Organ transplantation, Optical coherence tomography, Polarization-sensitive imaging, Liver viability, Artificial intelligence, Biomedical imaging, Noninvasive diagnostics, Medical innovation

Most traditional screening technologies—including Pentacam and MS-39—depend on assessing macroscopic features such as corneal curvature, thickness, and surface irregularities. These metrics, while efficacious for diagnosing established keratoconus, often fall short when the disease is nascent and changes remain microscopic. During these early phases, the cornea may outwardly appear normal under routine clinical examination, obscuring internal disruptions within its collagen matrix. Recognizing this limitation, the study authors shifted focus toward detecting subtle collagen fiber disorganization—an early hallmark of keratoconus that precedes conspicuous shape distortion.

PS-OCT, an advanced imaging modality, exploits the interaction between polarized light and biological tissues to reveal microstructural organization inaccessible to conventional tomography. When polarized light traverses the cornea, its retardation—alterations in polarization state—encodes information about the alignment and integrity of collagen fibrils, which are vital for maintaining corneal biomechanical strength and transparency. This method offers ultrahigh resolution and the capacity to generate phase retardation maps that reflect collagen orientation with remarkable sensitivity.

Leveraging a bespoke PS-OCT instrument, the researchers collected comprehensive corneal datasets from 359 eyes, encompassing healthy individuals, clinically diagnosed keratoconus patients, and a substantial cohort exhibiting subclinical keratoconus. The device’s imaging capabilities allowed detailed layer-specific measurements, including epithelium, Bowman’s layer, and stroma thickness, alongside multifaceted polarization data crucial for discerning microarchitectural differences.

To objectively evaluate diagnostic performance, the research team implemented three independent AI models corresponding to data derived from PS-OCT, Pentacam, and MS-39, respectively. Utilizing identical machine learning frameworks and validation techniques ensured fair comparison across modalities. While accuracy rates were comparable for unequivocally healthy or diseased eyes, distinct disparities emerged when evaluating subclinical cases. The PS-OCT-driven AI demonstrated a unique ability to reclassify certain eyes previously deemed subclinical by tomography as truly healthy based on collagen organization patterns, effectively reducing false-positive diagnoses.

This strategic reclassification hinged not on arbitrary distinctions but on substantial biophysical evidence. Eyes labeled subclinical by PS-OCT exhibited increased phase retardation and detectable thinning of Bowman’s layer, even absent conspicuous corneal shape anomalies. Conversely, eyes reverted to healthy classification revealed collagen and layer thickness consistent with normal corneal integrity, underscoring the technique’s specificity and nuanced diagnostic capability.

Visual inspection of phase retardation maps furnished compelling insights. Healthy corneas displayed homogenous retardation signals indicative of well-aligned collagen fibers and symmetrical Bowman’s layer thickness. In contrast, subclinical keratoconus samples presented moderate but consistent central increases in phase retardation coupled with subtle Bowman’s layer thinning—suggesting early collagen remodeling. Established keratoconus cases revealed chaotic, irregular retardation distributions alongside marked morphological abnormalities, aligning with known disease pathology.

These findings hold profound clinical implications. Vis-à-vis patient management, a frequent conundrum arises when thin or marginally atypical corneas are categorized as “suspect,” prompting conservative intervention or denial of refractive surgeries. PS-OCT’s capacity to distinguish genuinely compromised tissue from naturally thin yet stable corneas heralds a paradigm shift, allowing ophthalmologists to tailor treatment strategies with increased confidence and potentially expanding surgical candidacy safely.

Moreover, PS-OCT’s integration with AI algorithms exemplifies the potency of combining sophisticated imaging with computational intelligence — enabling multidimensional analysis of corneal health beyond mere morphology. Such synergy not only enhances sensitivity but also circumvents subjective interpretation pitfalls, laying groundwork for standardizing early keratoconus detection robustly across diverse clinical settings.

While the current study establishes a promising new frontier, the authors emphasize necessity for prolonged longitudinal studies to validate whether PS-OCT-based reclassifications accurately predict disease stability or progression over extended periods. Nonetheless, immediate utility for screening and diagnostic refinement is evident, particularly in refractive surgery candidate evaluation programs where early differentiation between pathological and physiological corneal features is critical.

Looking ahead, this research epitomizes a broader trend within biomedical optics: harnessing polarization-sensitive imaging to unlock deeper tissue characterization, which traditional intensity-based modalities overlook. Future technological innovations might further amplify resolution and data richness, integrating seamlessly with AI advances to personalize ocular healthcare, monitor corneal pathologies dynamically, and optimize treatment outcomes.

In conclusion, polarization-sensitive optical coherence tomography augmented by artificial intelligence represents a transformative leap in ophthalmic imaging. By illuminating microscopic organizational changes in corneal collagen, this technique bridges existing diagnostic gaps, facilitates earlier disease detection, and ultimately enhances clinical decision-making in keratoconus management. As this methodology gains traction, it promises to elevate corneal diagnostics from structural observation to functional tissue analysis, aligning perfectly with the era of precision medicine.

Subject of Research:
Optical imaging techniques for early keratoconus detection and artificial intelligence applications in ophthalmology.

Article Title:
Advancing subclinical keratoconus detection using polarization-sensitive optical coherence tomography and artificial intelligence.

News Publication Date:
9-Feb-2026

Web References:
https://www.spiedigitallibrary.org/journals/biophotonics-discovery/volume-3/issue-01/015004/Advancing-subclinical-keratoconus-detection-using-polarization-sensitive-optical-coherence-tomography/10.1117/1.BIOS.3.1.015004.full

References:
Patil, R. P., et al. “Advancing subclinical keratoconus detection using polarization-sensitive optical coherence tomography and artificial intelligence,” Biophotonics Discovery 3(1), 015004 (2026), doi:10.1117/1.BIOS.3.1.015004.

Image Credits:
R. P. Patil et al.

Keywords

Tomography, Eye, Optical Coherence Tomography, Polarization-sensitive Imaging, Keratoconus, Corneal Collagen, Artificial Intelligence, Biomedical Imaging, Ophthalmology, Corneal Microstructure, Bowman’s Layer, Refractive Surgery Screening