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OBJECTIVE: The diagnosis of urinary tract infection (UTI) currently requires precise specimen collection, handling infectious human waste, controlled urine storage, and timely transportation to modern laboratory equipment for analysis. Here we investigate holographic lens free imaging (LFI) to show its promise for enabling automatic urine analysis at the patient bedside. METHODS: We introduce an LFI system capable of resolving important urine clinical biomarkers such as red blood cells, white blood cells, crystals, and casts in 2 mm thick urine phantoms. RESULTS: This approach is sensitive to the particulate concentrations relevant for detecting several clinical urine abnormalities such as hematuria and pyuria, linearly correlating to ground truth hemacytometer measurements with R(2) = 0.9941 and R(2) = 0.9973, respectively. We show that LFI can estimate E. coli concentrations of 10(3) to 10(5) cells/mL by counting individual cells, and is sensitive to concentrations of 10(5) cells/mL to 10(8) cells/mL by analyzing hologram texture. Further, LFI measurements of blood cell concentrations are relatively insensitive to changes in bacteria concentrations of over seven orders of magnitude. Lastly, LFI reveals clear differences between UTI-positive and UTI-negative urine from human patients. CONCLUSION: LFI is sensitive to clinically-relevant concentrations of bacteria, blood cells, and other sediment in large urine volumes. SIGNIFICANCE: Together, these results show promise for LFI as a tool for urine screening, potentially offering early, point-of-care detection of UTI and other pathological processes.

In-line lensless digital holography has great potential in multiple applications; however, reconstructing high-quality images from a single recorded hologram is challenging due to the loss of phase information. Typical reconstruction methods are based on solving a regularized inverse problem and work well under suitable image priors, but they are extremely sensitive to mismatches between the forward model and the actual imaging system. This paper aims to improve the robustness of such algorithms by introducing the adaptive sparse reconstruction method, ASR, which learns a properly constrained point spread function (PSF) directly from data, as opposed to solely relying on physics-based approximations of it. ASR jointly performs holographic reconstruction, PSF estimation, and phase retrieval in an unsupervised way by maximizing the sparsity of the reconstructed images. Like traditional methods, ASR uses the image formation model along with a sparsity prior, which, unlike recent deep learning approaches, allows for unsupervised reconstruction with as little as one sample. Experimental results in synthetic and real data show the advantages of ASR over traditional reconstruction methods, especially in cases where the theoretical PSF does not match that of the actual system.

While data-driven approaches excel at many image analysis tasks, the performance of these approaches is often limited by a shortage of annotated data available for training. Recent work in semi-supervised learning has shown that meaningful representations of images can be obtained from training with large quantities of unlabeled data, and that these representations can improve the performance of supervised tasks. Here, we demonstrate that an unsupervised jigsaw learning task, in combination with supervised training, results in up to a 9.8% improvement in correctly classifying lesions in colonoscopy images when compared to a fully-supervised baseline. We additionally benchmark improvements in domain adaptation and out-of-distribution detection, and demonstrate that semi-supervised learning outperforms supervised learning in both cases. In colonoscopy applications, these metrics are important given the skill required for endoscopic assessment of lesions, the wide variety of endoscopy systems in use, and the homogeneity that is typical of labeled datasets., 10 pages, 5 figures

Purpose To compare corneal endothelial damage associated with 2 techniques for preloaded Descemet membrane endothelial keratoplasty (DMEK): a tri-folded graft stored in a plastic cartridge designed for DMEK and a scrolled graft stored in a modified Jones Tube, at the time of preparation and after shipping. Methods DMEK grafts were prepared at the Rocky Mountain Lions Eye Bank. The grafts were either tri-folded and loaded in a plastic cartridge or scrolled and loaded into a modified Jones Tube. In each group, the grafts were then either immediately removed from the cartridges or shipped for 48 hours. The grafts were then stained with Calcein AM and imaged using a fluorescent microscope. Endothelial cell loss (ECL) was determined using trainable segmentation in Fiji by 2 graders. At each time point, rates of ECL loss were compared across the 2 groups. To explore the role of donor characteristics, a multivariable regression model was produced to account for method (tri-folding vs. scroll), donor age, donor gender, death-to-preservation time, death-to-preparation time, and shipping. Results A total of 40 grafts were prepared, processed, imaged, and analyzed. No significant difference in cell loss was seen between groups at either time point alone. In the multivariate model, no significant increase in cell loss was associated with either tri-folding (3.7% less ECL; P = 0.051) or shipping (4.3% less ECL; P = 0.049). Conclusions All techniques used resulted in clinically acceptable levels of ECL. Tri-folded tissue in a plastic cartridge did not result in ECL inferior to a scroll when prepared either immediately or preloaded for 48 hours.

This article reviews deep learning applications in biomedical optics with a particular emphasis on image formation. The review is organized by imaging domains within biomedical optics and includes microscopy, fluorescence lifetime imaging, in vivo microscopy, widefield endoscopy, optical coherence tomography, photoacoustic imaging, diffuse tomography, and functional optical brain imaging. For each of these domains, we summarize how deep learning has been applied and highlight methods by which deep learning can enable new capabilities for optics in medicine. Challenges and opportunities to improve translation and adoption of deep learning in biomedical optics are also summarized. Lasers Surg. Med. © 2021 Wiley Periodicals LLC.

Spatial Frequency Domain Imaging (SFDI) is a powerful technique for non-contact tissue optical property and chromophore mapping over a large field of view. However, a major challenge that limits the clinical adoption of SFDI is that it requires carefully-controlled imaging geometry and the projection of known spatial frequencies. We present speckle-illumination SFDI (si-SFDI), a projector-free technique that measures tissue optical properties from structured illumination formed by randomized speckle patterns. We compute the local power spectral density of images under speckle illumination, from which a high-frequency and a low-frequency tissueresponse parameter can be characterized for each pixel. A lookup table generated by Monte-Carlo simulations is subsequently used to accurately determine optical absorption and reduced scattering coefficients. Compared to conventional SFDI, si-SFDI may be particularly useful for endoscopic applications due to its utilization of simple coherent illumination, which makes it more easily incorporated into existing endoscopic systems. Moreover, speckle illumination offers a large depth of focus compared to projector-based illumination. In this study, we explore wide-field optical property mapping with an endoscope camera and fiber-coupled laser speckle illumination. We apply this technique to tissue-mimicking silicone phantoms and biological tissues. The accuracy of si-SFDI is evaluated by comparing to optical properties measured by conventional SFDI. Future work could accelerate si-SFDI reconstruction by using parallel computing or machine learning algorithms.

Machine learning offers a powerful set of tools to make widefield endoscopic imaging more quantitative. This presentation covers our work in estimating pixel size, topography, optical properties, and molecular chromophores using structured illumination and generative adversarial networks. We aim to create a computational endoscope that will improve computer-aided detection and diagnosis.

Oblique plane microscopy (OPM) enables high speed, volumetric fluorescence imaging through a single-objective geometry. While these advantages have positioned OPM as a valuable tool to probe biological questions in animal models, its potential for

Oblique back-illumination capillaroscopy has recently been introduced as an efficient method for non-invasive blood cell imaging in human capillaries. To apply this technique to clinical blood cell counting, solutions for automatic processing of acquired videos are needed. Here, we take the first step towards this goal, by introducing a novel deep learning multi-cell tracking model, named CycleTrack, which achieves accurate blood cell counting from capillaroscopic videos. CycleTrack combines two simple online tracking models, SORT and CenterTrack, and is tailored to features of capillary blood cell flow. Blood cells are tracked by displacement vectors in two opposing temporal directions between consecutive frames. This approach yields accurate tracking despite rapidly moving and deforming blood cells. The proposed model outperforms other baseline trackers, achieving \(66.3\%\) MOTA and \(75.1\%\) ID F1 score on test videos. CycleTrack achieves an average cell counting error of \(3.42\%\) among 8 1000-frame test videos, compared to \(6.55\%\) and \(22.98\%\) from original CenterTrack and SORT, with negligible time expense. It takes 800s to track and count approximately 8000 blood cells from 9,600 frames captured in a typical one-minute video. Moreover, the blood cell velocity measured by CycleTrack demonstrates a consistent, pulsatile pattern within the physiological range of heart rate. The project code is accessible online at: https://github.com/DurrLab/CycleTrack.

We demonstrate a low-cost diffuser-based computational funduscope that can recover pathological features of the model eye fundus. Our device achieves over 33° FOV and is robust to 4D refractive error using a single-shot point-spread-function.

Transarterial embolization (TAE) is a standard-of-care treatment for tumors in which embolic particles are locally injected via a catheter to occlude blood flow and induce ischemia in the target tissue. Physicians currently rely on subjective visual cues from fluoroscopy in order to determine the procedural endpoint relative to the injection site. This contributes to highly variable treatment outcomes, including the accumulation of embolic particles in healthy tissue, called off-target embolization. To address this concern, we describe a novel, multilumen catheter that 1) measures real-time pressure upstream of the tumor site during TAE injection; and 2) associates that measurement with the volume of embolic particles injected. Using an in vitro silicon vascular model, we characterize the relationship between blood flow, intravascular pressure, and injection pressure. Furthermore, we identify a predictive pressure curve based on the volume of embolic particles injected. This approach has the potential to standardize and optimize TAE, reducing the likelihood of incomplete or off-target embolization, and improving patient outcomes.

Over 220,000 rhinoplasties are performed in the U.S. annually, with approximately 90% of all procedures requiring the use of cartilage autografts or allografts to augment nasal structure or contour. Prior to implantation, graft cartilage is shaped into the desired form by facial plastic surgeons through carving, crushing, or dicing, and molding of the tissue. The use of diced cartilage has been shown in literature to be the optimal technique for creating contour grafts, permitting increased graft moldability, and resulting in the fewest postoperative complications. Despite superior clinical outcomes, adoption of diced techniques is challenged by the laborious, time-intensive process of dicing tissue, which requires up to 2 h in the operating room and is currently performed manually with a blade. Manual dicing can result in inconsistently sized pieces, which can increase risk of graft breakage. Existing cartilage processing tools are primarily intended for crushing tissue and are unsuitable for dicing. This work describes the design and validation of an easy-to-use, effective device for rapid, consistent cartilage dicing. Cartilage sizing analysis demonstrated that the device produced approximately five times more diced cartilage within 5 min compared to manual dicing. The rapid dicer (RD) device consistently dices cartilage to a size suitable for grafting while significantly decreasing processing time and cartilage loss compared to current methods. Future development will focus on performing further user testing of the device to design a more ergonomic instrument casing.

Deep learning techniques hold promise to develop dense topography reconstruction and pose estimation methods for endoscopic videos. However, currently available datasets do not support effective quanti- tative benchmarking. In this paper, we introduce a comprehensive endoscopic SLAM dataset consisting of 3D point cloud data for six porcine organs, capsule and standard endoscopy recordings, synthetically generated data as well as clinically in use conventional endoscope recording of the phantom colon with computed tomography(CT) scan ground truth. A Panda robotic arm, two commercially available capsule endoscopes, three conventional endoscopes with different camera properties, two high precision 3D scan- ners, and a CT scanner were employed to collect data from eight ex-vivo porcine gastrointestinal (GI) tract organs and a silicone colon phantom model. In total, 35 sub-datasets are provided with 6D pose ground truth for the ex-vivo part: 18 sub-datasets for colon, 12 sub-datasets for stomach, and 5 sub- datasets for small intestine, while four of these contain polyp-mimicking elevations carried out by an expert gastroenterologist. To verify the applicability of this data for use with real clinical systems, we recorded a video sequence with a state-of-the-art colonoscope from a full representation silicon colon phantom. Synthetic capsule endoscopy frames from stomach, colon, and small intestine with both depth and pose annotations are included to facilitate the study of simulation-to-real transfer learning algo- rithms. Additionally, we propound Endo-SfMLearner, an unsupervised monocular depth and pose estima- tion method that combines residual networks with a spatial attention module in order to dictate the network to focus on distinguishable and highly textured tissue regions. The proposed approach makes use of a brightness-aware photometric loss to improve the robustness under fast frame-to-frame illu- mination changes that are commonly seen in endoscopic videos. To exemplify the use-case of the En-doSLAM dataset, the performance of Endo-SfMLearner is extensively compared with the state-of-the-art: SC-SfMLearner, Monodepth2, and SfMLearner. The codes and the link for the dataset are publicly available at https://github.com/CapsuleEndoscope/EndoSLAM . A video demonstrating the experimental setup and procedure is accessible as Supplementary Video 1.& nbsp; (c) 2021 Elsevier B.V. All rights reserved.

Current capsule endoscopes and next-generation robotic capsules for diagnosis and treatment of gastrointestinal diseases are complex cyber-physical platforms that must orchestrate complex software and hardware functions. The desired tasks for these systems include visual localization, depth estimation, 3D mapping, disease detection and segmentation, automated navigation, active control, path realization and optional therapeutic modules such as targeted drug delivery and biopsy sampling. Data-driven algorithms promise to enable many advanced functionalities for capsule endoscopes, but real-world data is challenging to obtain. Physically-realistic simulations providing synthetic data have emerged as a solution to the development of data-driven algorithms. In this work, we present a comprehensive simulation platform for capsule endoscopy operations and introduce VR-Caps, a virtual active capsule environment that simulates a range of normal and abnormal tissue conditions (e.g., inflated, dry, wet etc.) and varied organ types, capsule endoscope designs (e.g., mono, stereo, dual and 360{\deg}camera), and the type, number, strength, and placement of internal and external magnetic sources that enable active locomotion. VR-Caps makes it possible to both independently or jointly develop, optimize, and test medical imaging and analysis software for the current and next-generation endoscopic capsule systems. To validate this approach, we train state-of-the-art deep neural networks to accomplish various medical image analysis tasks using simulated data from VR-Caps and evaluate the performance of these models on real medical data. Results demonstrate the usefulness and effectiveness of the proposed virtual platform in developing algorithms that quantify fractional coverage, camera trajectory, 3D map reconstruction, and disease classification., Comment: 18 pages, 14 figures

Quantification of optical absorption gaps in nailfold capillaries has recently shown promise as a non-invasive technique for neutropenia screening. Here we demonstrate a low-cost, portable attachment to a mobile phone that can resolve optical absorption gaps in nailfold capillaries using a reverse lens technique and oblique 520nm illumination. Resolution μm within a 1mm2 on-axis region is demonstrated, and wide field of view (3.5mm × 4.8mm) imaging is achieved with resolution μm in the periphery. Optical absorption gaps (OAGs) are visible in superficial capillary loops of a healthy human participant by an ∼8-fold difference in contrast-to-noise ratio with respect to red blood cell absorption contrast. High speed video capillaroscopy up to 240 frames per second (fps) is possible, though 60fps is sufficient to resolve an average frequency of 37 OAGs/minute passing through nailfold capillaries. The simplicity and portability of this technique may enable the development of an effective non-invasive tool for white blood cell screening in point-of-care and global health settings.

One barrier to breast cancer diagnosis in low-resource settings is that devices for core needle biopsy (CNB) are either disposable and expensive, or reusable and susceptible to internal contamination. Through interviews with field workers and verification experiments, we identified that a common, commercially available, reusable CNB device allows contaminants to enter the driver chamber during firing, necessitating laborious cleaning of the entire device after every use. We introduce a novel CNB device attachment that eliminates this contamination mode and interfaces with existing commercial reusable drivers and low-cost disposable needles. This attachment repositions the driver–needle connection to the exterior of the driver, preventing retrograde flow of blood. Using an unmodified commercial CNB, we replicate chamber contamination by firing into a body fluid-mimicking glycerol solution. Prototypes were tested for their performance in eliminating this contamination. We tested the effectiveness of a cleaning procedure to reduce trace contamination by using a fluorescent dye and measuring the intensity of fluorescence after cleaning. The device's ability to reliably and consistently biopsy tissue with the novel attachment was evaluated using breast tissue models. In these tests, a reusable CNB with our attachment exhibited no measurable internal contamination, and maintained full biopsy functionality as measured by tissue sample weight and length. Minimizing internal device contamination would simplify the cleaning process for reusable biopsy devices. This would improve the accessibility of breast cancer biopsies in low- and middle-income countries (LMICs).

We present a deep learning framework for wide-field, content-aware estimation of absorption and scattering coefficients of tissues, called Generative Adversarial Network Prediction of Optical Properties (GANPOP). Spatial frequency domain imaging is used to obtain ground-truth optical properties from in vivo human hands, freshly resected human esophagectomy samples and homogeneous tissue phantoms. Images of objects with either flat-field or structured illumination are paired with registered optical property maps and are used to train conditional generative adversarial networks that estimate optical properties from a single input image. We benchmark this approach by comparing GANPOP to a single-snapshot optical property (SSOP) technique, using a normalized mean absolute error (NMAE) metric. In human gastrointestinal specimens, GANPOP estimates both reduced scattering and absorption coefficients at 660 nm from a single 0.2/mm spatial frequency illumination image with 58% higher accuracy than SSOP. When applied to both in vivo and ex vivo swine tissues, a GANPOP model trained solely on human specimens and phantoms estimates optical properties with approximately 43% improvement over SSOP, indicating adaptability to sample variety. Moreover, we demonstrate that GANPOP estimates optical properties from flat-field illumination images with similar error to SSOP, which requires structured-illumination. Given a training set that appropriately spans the target domain, GANPOP has the potential to enable rapid and accurate wide-field measurements of optical properties, even from conventional imaging systems with flat-field illumination.

Spatial frequency domain imaging can map tissue scattering and absorption properties over a wide field of view, making it useful for clinical applications such as wound assessment and surgical guidance. This technique has previously required the projection of fully characterized illumination patterns. Here, we show that random and unknown speckle illumination can be used to sample the modulation transfer function of tissues at known spatial frequencies, allowing the quantitative mapping of optical properties with simple laser diode illumination. We compute low- and high-spatial frequency response parameters from the local power spectral density for each pixel and use a lookup table to accurately estimate absorption and scattering coefficients in tissue phantoms, in vivo human hand, and ex vivo swine esophagus. Because speckle patterns can be generated over a large depth of field and field of view with simple coherent illumination, this approach may enable optical property mapping in new form-factors and applications, including endoscopy.

Poor access to eye care is a major global challenge that could be ameliorated by low-cost, portable, and easy-to-use diagnostic technologies. Diffuser-based imaging has the potential to enable inexpensive, compact optical systems that can reconstruct a focused image of an object over a range of defocus errors. Here, we present a diffuser-based computational funduscope that reconstructs important clinical features of a model eye. Compared to existing diffuser-imager architectures, our system features an infinite-conjugate design by relaying the ocular lens onto the diffuser. This offers shift-invariance across a wide field-of-view (FOV) and an invariant magnification across an extended depth range. Experimentally, we demonstrate fundus image reconstruction over a 33° FOV and robustness to ±4D refractive error using a constant point-spread-function. Combined with diffuser-based wavefront sensing, this technology could enable combined ocular aberrometry and funduscopic screening through a single diffuser sensor.

Significance: Spatial frequency-domain imaging (SFDI) is a powerful technique for mapping tissue oxygen saturation over a wide field of view. However, current SFDI methods either require a sequence of several images with different illumination patterns or, in the case of single-snapshot optical properties (SSOP), introduce artifacts and sacrifice accuracy. Aim: We introduce OxyGAN, a data-driven, content-aware method to estimate tissue oxygenation directly from single structured-light images. Approach: OxyGAN is an end-to-end approach that uses supervised generative adversarial networks. Conventional SFDI is used to obtain ground truth tissue oxygenation maps for ex vivo human esophagi, in vivo hands and feet, and an in vivo pig colon sample under 659- and 851-nm sinusoidal illumination. We benchmark OxyGAN by comparing it with SSOP and a two-step hybrid technique that uses a previously developed deep learning model to predict optical properties followed by a physical model to calculate tissue oxygenation. Results: When tested on human feet, cross-validated OxyGAN maps tissue oxygenation with an accuracy of 96.5%. When applied to sample types not included in the training set, such as human hands and pig colon, OxyGAN achieves a 93% accuracy, demonstrating robustness to various tissue types. On average, OxyGAN outperforms SSOP and a hybrid model in estimating tissue oxygenation by 24.9% and 24.7%, respectively. Finally, we optimize OxyGAN inference so that oxygenation maps are computed ∼10 times faster than previous work, enabling video-rate, 25-Hz imaging. Conclusions: Due to its rapid acquisition and processing speed, OxyGAN has the potential to enable real-time, high-fidelity tissue oxygenation mapping that may be useful for many clinical applications.

Although wireless capsule endoscopy is the preferred modality for diagnosis and assessment of small bowel diseases, the poor camera resolution is a substantial limitation for both subjective and automated diagnostics. Enhanced-resolution endoscopy has shown to improve adenoma detection rate for conventional endoscopy and is likely to do the same for capsule endoscopy. In this work, we propose and quantitatively validate a novel framework to learn a mapping from low-to-high resolution endoscopic images. We combine conditional adversarial networks with a spatial attention block to improve the resolution by up to factors of 8x, 10x, 12x, respectively. Quantitative and qualitative studies performed demonstrate the superiority of EndoL2H over state-of-the-art deep super-resolution methods DBPN, RCAN and SRGAN. MOS tests performed by 30 gastroenterologists qualitatively assess and confirm the clinical relevance of the approach. EndoL2H is generally applicable to any endoscopic capsule system and has the potential to improve diagnosis and better harness computational approaches for polyp detection and characterization. Our code and trained models are available at https://github.com/CapsuleEndoscope/EndoL2H., Comment: 23 pages, submitted to IEEE Transactions on Medical Imaging, corresponding Author: Mehmet Turan

This erratum is to add author Nicholas J. Durr as a co-corresponding author.

Purpose Preloaded, trifolded grafts in Descemet membrane endothelial keratoplasty require transfer of the trifolding process from the corneal transplant surgeon to the eye bank technician. We sought to assess whether trifolding may be safely conducted by an eye bank technician with cell loss comparable to standard peeling and lifting. Methods A total of 10 grafts were stained, peeled, and transferred directly onto a bed of Calcein-AM and Amvisc Plus by an eye bank technician. Five grafts were removed and stained as a scroll, and 5 grafts were trifolded with the endothelium in before transfer. Photographs were acquired with an inverted fluorescence microscope, and image segmentation was performed. A t test was conducted to compare differences in endothelial cell loss across groups. Results Mean cell loss in the scroll group was 18.5% [95% confidence interval (CI): 15.2%-21.9%] compared with 7.6% of the trifolded group (95% CI: 1.7%-13.5%). A 2-tailed t test indicated decreased cell loss in the trifolded group (P = 0.013). Conclusions Despite additional manipulation of the graft, trifolding of Descemet membrane and endothelium may be performed by an eye bank technician without significantly increased cell loss relative to graft preparation as a scroll.

Following the footprints of Bill Gates, Steve Jobs and Mark Zuckerberg, there has been a misconception that students are better off quitting their studies to bring to life their ideas, create jobs and monetize their inventions. Having historically transitioned from manpower to mind power, we live in one of the most rapidly changing times in human history. As a result, academic institutions that are supposed to be pioneers and educators of the next generations have started to realize that they need to adapt to a new system, and change their policies to be more flexible towards patent ownership and commercialization. There is an infrastructure being developed towards students starting their own businesses while continuing with their studies. This paper aims to provide an overview of the existing landscape, the exciting rewards as well as risks awaiting a student entrepreneur, the challenges of the present ecosystem, and questions to consider prior to embarking on such a journey. Various entities influencing the start-up environment are considered, specifically for the medical technology sector. These parties include but are not limited to: scientists, clinicians, investors, academic institutions and governments. A special focus will be set on the seemingly unbridgeable gap between founding a company and a scientific career.

Significance There is a critical need for tools that increase the accessibility of eye care to address the most common cause of vision impairment: uncorrected refractive errors. This work assesses the performance of an affordable autorefractor, which could help reduce the burden of this health care problem in low-resource communities. Purpose The purpose of this study was to validate the commercial version of a portable wavefront autorefractor for measuring refractive errors. Methods Refraction was performed without cycloplegia using (1) a standard clinical procedure consisting of an objective measurement with a desktop autorefractor followed by subjective refraction (SR) and (2) with the handheld autorefractor. Agreement between both methods was evaluated using Bland-Altman analysis and by comparing the visual acuity (VA) with trial frames set to the resulting measurements. Results The study was conducted on 54 patients (33.9 ± 14.1 years of age) with a spherical equivalent (M) refraction determined by SR ranging from -7.25 to 4.25 D (mean ± SD, -0.93 ± 1.95 D). Mean differences between the portable autorefractor and SR were 0.09 ± 0.39, -0.06 ± 0.13, and 0.02 ± 0.12 D for M, J0, and J45, respectively. The device agreed within 0.5 D of SR in 87% of the eyes for spherical equivalent power. The average VAs achieved from trial lenses set to the wavefront autorefractor and SR results were 0.02 ± 0.015 and 0.015 ± 0.042 logMAR units, respectively. Visual acuity resulting from correction based on the device was the same as or better than that achieved by SR in 87% of the eyes. Conclusions This study found excellent agreement between the measurements obtained with the portable autorefractor and the prescriptions based on SR and only small differences between the VA achieved by either method.