Researchers at Rice University and The University of Texas MD Anderson Cancer Center have developed a compact, artificial intelligence-powered imaging device that could transform how clinicians detect cancer. The technology, which aims to bring high-resolution, real-time diagnostics directly to the point of care, was recently described in a paper published in the Proceedings of the National Academy of Sciences.
The device, called PrecisionView, is a handheld endomicroscope that overcomes limitations in medical imaging by combining advanced optics with deep learning. The system enables clinicians to visualize both subcellular structures and underlying blood vessels across large tissue areas without the need for invasive biopsies.
“Early detection is one of the most critical factors in improving cancer outcomes, but today’s tools often force clinicians to choose between detail and coverage,” said corresponding author Rebecca Richards-Kortum, the Malcolm Gillis University Professor at Rice and co-director of the Rice360 Institute for Global Health Technologies. “With PrecisionView, we no longer have to make that trade-off - we can see both clearly and in real time.”
Epithelial cancers, which include cancers of the cervix and oral cavity, account for a majority of cancer cases, yet many are diagnosed at late stages, in part because current diagnostic methods rely heavily on biopsies, which are invasive and limited in scope.
Traditional in vivo microscopy offers a noninvasive alternative, but it has some key constraints, including a smaller field of view, shallow depth of field and some difficulty imaging uneven tissue surfaces. These limitations can make it challenging to assess large or complex lesions and identify where a biopsy may truly be needed. PrecisionView aims to address these challenges through a novel design that integrates a deep learning-optimized optical system with real-time image reconstruction.
About the size of a pen, PrecisionView uses a custom-designed phase mask and AI reconstruction algorithm to dramatically expand imaging capabilities. The system achieves a field of view roughly five times larger and a depth of field about eight times greater than conventional systems while maintaining cellular-level resolution.
“Traditionally, machine learning and artificial intelligence tools are used to enhance images in terms of resolution or contrast, after the images have been acquired by conventional imaging systems,” explained Ashok Veeraraghavan, chair of electrical and computer engineering at Rice and co-author of the study. “In stark contrast, this work utilizes AI approaches to redesign the optics of a microscope. The AI-designed optics not just improves resolution/contrast but more importantly breaks the conventional trade-off between depth of field and resolution - creating a handheld microscope platform that still achieves cellular resolution, while providing for an 8-times increase in depth of field. In practice, this DOF improvement is critical for usability of the device in the field since it makes it practical for clinicians and technicians to hold the device on their hand and obtain high resolution images without the image quality being compromised due to focal blur.”
This advancement allows clinicians to simultaneously visualize two critical hallmarks of cancer: cellular changes in epithelial tissue and microvascular patterns beneath the surface.
“Being able to capture both nuclear and vascular features in a single, continuous image is a major step forward, because these are the signals clinicians rely on to distinguish healthy tissue from precancerous or cancerous lesions,” said Huayu Hou, a graduate student in Richards-Kortum’s Optical Spectroscopy and Imaging Laboratory and one of the authors of the paper.
The device can generate detailed maps of tissue areas spanning several square centimeters and display results in real time at up to 15 frames per second.
The researchers validated PrecisionView through a series of experiments, including imaging of healthy volunteers and human tissue samples with precancerous lesions. In one study, the device was used to scan the oral cavity of volunteers, producing high-resolution maps of tissue structure and blood vessels across areas larger than 1 square centimeter. In another, it successfully identified precancerous changes in cervical tissue, clearly distinguishing abnormal regions from surrounding healthy tissue.
“Instead of sampling a small piece of tissue and sending it to a lab, this technology allows us to assess a much larger area instantly,” said Jimin Wu, a postdoctoral associate in electrical and computer engineering and one of the authors of the study. “That could significantly reduce missed diagnoses and unnecessary procedures.”
Beyond its imaging performance, PrecisionView is designed with accessibility in mind. Built using relatively simple components and costing roughly $3,000, the system could be deployed in clinics and low-resource settings where traditional pathology infrastructure is limited. Developing highly effective, low-cost health care solutions like this is one of Rice360’s hallmark initiatives.
“PrecisionView has the potential to bring high-quality diagnostic capability directly to the point of care - helping clinicians make more timely decisions which will improve access to life-saving early detection,” said Kathleen Schmeler, one of the authors of the study and associate vice president of global oncology in the Department of Cancer Network, Division of Surgery at MD Anderson. “The impact will be particularly significant in medically underserved areas where access to pathology services may be limited or delayed, leading to missed or late diagnoses.”
The researchers say the technology could support a range of clinical applications, from guiding biopsies and surgical decisions to enabling earlier cancer detection during routine screenings. They emphasize, however, that larger clinical studies are still needed to fully validate the device’s diagnostic accuracy.
“PrecisionView represents a future direction for medical imaging, one where artificial intelligence and optical design work together to improve outcomes,” Richards-Kortum said. “By designing hardware and algorithms together, we can unlock capabilities that simply weren’t possible before.”
Reference: Hou H, Wu J, Liu J, et al. Deep-learning endomicroscope with large field-of-view and depth-of-field for real-time in vivo imaging of epithelial cancer hallmarks. Proc Natl Acad Sci U S A. 2026;123(20):e2602705123. doi: 10.1073/pnas.2602705123
This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source. Our press release publishing policy can be accessed here.