Standard Camera Research Articles

TESTING OF CLOSE-RANGE PHOTOGRAMMETRY AND LASER SCANNING FOR EASY DOCUMENTATION OF HISTORICAL OBJECTS AND BUILDINGS PARTS

In contemporary times, technology has dramatically simplified the process of documenting cultural heritage objects. This paper aims to illustrate modern methodologies for documenting historical monuments by applying advanced technologies, as these case studies show. Two technologies were used: laser scanning and digital photogrammetry. The first subject of our study was a historically significant 16th-century building located in Jachymov, situated in the North-West region of the Czech Republic. The object was documented by laser scanning (BLK360) and using a standard digital camera Canon Powershot SX230 HS. The laser scanning data were post-processed using Cyclone Register 360 Plus, while photogrammetric point clouds were generated with Agisoft Metashape. The second objects were a building and a sculpture in the historical UNESCO city of Telč; here laser scanning instruments, digital photogrammetry and a new one low-cost ViDoc documentation system were use. Our study is focused on analyzing the sets of point clouds obtained from the data processing, conducted using CloudCompare to draw insights and conclusions. It turns out that the rapid development of techniques and technology makes it possible to use low-cost devices for sufficiently accurate and fast documentation; however, the mentioned ViDOC device can only be used for very close and small objects up to about 5 meters. For the documentation of sculptures, classic close-range photogrammetry is especially appropriate; for the documentation of buildings, laser scanners are suitable; the problem here is with hidden spaces and it is often necessary to use either a platform or a combination with photogrammetry using a drone, especially for the upper parts of the object.

The expression of Cox-2 and Cyclin D1 with clinicopathological characteristics in oral epithelial dysplasia: A retrospective study

Background: Oral mucosal cancers are often preceded by potentially malignant diseases that may exhibit diverse morphological and histological alterations. The expression of COX 2 has been related to tumor vascularization, dissemination, and survival in oral cancer studies. High levels of cyclin D1 expression to precancerous lesions and oral malignancies. This study was designed to evaluate and compare the immunohistochemical expression of the above-mentioned markers. Materials and Methods: 50 retrospective formalin fixed-paraffin embedded histopathologically confirmed blocks of oral epithelial dysplasia. Immunohistochemical staining was performed using monoclonal antibodies against COX-2 and Cyclin D1. Using a standard camera, photomicrographs of the slides were taken. Results: No significant difference in the age distribution of the patients studied, as well as age pivoting at the fifth and sixth decades with mean and standard deviation (53.36 2.63). There was no significant difference in gender among the studied patients. Furthermore, there was a highly significant difference in grade distribution. Conclusions: The COX-2 and Cyclin D1 markers may assist in diagnosing oral epithelial dysplasia based on severity. It's easy to gather image-analytic data on the stained tissue specimens.

Diagnostic Accuracy of Hand-Held Fundus Camera and Artificial Intelligence in Diabetic Retinopathy Screening.

Our study aimed to assess the role of a hand-held fundus camera and artificial intelligence (AI)-based grading system in diabetic retinopathy (DR) screening and determine its diagnostic accuracy in detecting DR compared with clinical examination and a standard fundus camera. This cross-sectional instrument validation study, as a part of the International Diabetes Federation (IDF) Diabetic Retinopathy Screening Project, included 160 patients (320 eyes) with type 2 diabetes (T2DM). After the standard indirect slit-lamp fundoscopy, each patient first underwent fundus photography with a standard 45° camera VISUCAM Zeiss and then with a hand-held camera TANG (Shanghai Zhi Tang Health Technology Co., Ltd.). Two retina specialists independently graded the images taken with the standard camera, while the images taken with the hand-held camera were graded using the DeepDR system and an independent IDF ophthalmologist. The three screening methods did not differ in detecting moderate/severe nonproliferative and proliferative DR. The area under the curve, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, kappa (ĸ) agreement, diagnostic odds ratio, and diagnostic effectiveness for a hand-held camera compared to clinical examination were 0.921, 89.1%, 100%, 100%, 91.4%, infinity, 0.11, 0.86, 936.48, and 94.9%, while compared to the standard fundus camera were 0.883, 83.2%, 100%, 100%, 87.3%, infinity, 0.17, 0.78, 574.6, and 92.2%. The results of our study suggest that fundus photography with a hand-held camera and AI-based grading system is a short, simple, and accurate method for the screening and early detection of DR, comparable to clinical examination and fundus photography with a standard camera.

PRACTICAL EXPLORATION OF LOW-COST YET HIGHLY ACCURATE 3D MAPPING TECHNIQUES FOR DOCUMENTATION AND CONSERVATION OF AN EGYPTIAN TOMB (THEBAN TOMB 45)

Abstract. This research presents the initial endeavour to perform geo-referenced 3D reconstruction of an ancient Egyptian tomb located on the West Bank of the Nile in Luxor, evaluating low-cost techniques. This study uses low-cost equipment in conjunction with simple geodetical principles to accurately map a tomb in 3D to empower archaeological teams with limited budgets to efficiently and effectively map their projects. Emphasizing the project goals of both high detail and high accuracy, one of our chosen techniques is photogrammetry with a standard system camera. Additionally, 3D mapping with a modern smartphone was explored in the tomb and was compared with results from the photogrammetry process. Geo-referencing the underground map in a global grid was also part of the project goals. This research shows that, within certain reasonable constraints, the chosen low-cost techniques successfully achieved all goals of producing a highly detailed, highly accurate, and georeferenced 3D map of the selected tomb (Theban Tomb 45). In this first season of mapping the tomb in 3D, various mapping methods were used and tried and where possible compared. Digitally mapping an ancient, underground Egyptian tomb requires a planned approach. In order to better plan when using low-cost scanning equipment, preliminary research was done for mapping a mostly underground, relatively small but complex to survey ancient Egyptian tomb. Data have been collected with the use of both devices and also with a total station, a laser distance meter and a GNSS (Global Navigation Satellite System) datalogger.

Characteristics of orthopaedic implants damage and mechanisms of its initiation

Implant fractures complicate orthopaedic procedures requiring secondary surgeries. However, it is not exactly known when the damage initiation starts and which factors predispose them to the highest degree. Thus, the aim of the study was to characterize changes observed in orthopaedic implants after their removal from the bone. Particular efforts were made to determine, which of them occur during the process of its production, installation, usage and removal. Such identification enabled to specify the role of lesions emerging during each period and their role in the implant’s deformation or fracture. The paper was focused on implants dedicated to the stabilization of the femur fractures since the bone transfers the highest loads. External surface and geometric features of eight representative implants were observed under standard and stereoscopic cameras, as well as under light and scanning electron microscopes. Macroscopic analysis of the investigated implants exhibited a number of defects in the form of scratches, abrasions, deformations and chipping. The wear degree of each implant was different and strictly depended on how it was implanted in the patient's body, as well as on how the patient exploited it through his mobility. Four different sources of implant integrity changes were identified: manufacturing-related changes, implant application-related changes, daily life-related changes and implant removal-related changes.

Convolutional Neural Network-Based Vehicle Classification in Low-Quality Imaging Conditions for Internet of Things Devices

Vehicle classification has an important role in the efficient implementation of Internet of Things (IoT)-based intelligent transportation system (ITS) applications. Nowadays, because of their higher performance, convolutional neural networks (CNNs) are mostly used for vehicle classification. However, the computational complexity of CNNs and high-resolution data provided by high-quality monitoring cameras can pose significant challenges due to limited IoT device resources. In order to address this issue, this study aims to propose a simple CNN-based model for vehicle classification in low-quality images collected by a standard security camera positioned far from a traffic scene under low lighting and different weather conditions. For this purpose, firstly, a new dataset that contains 4800 low-quality vehicle images with 100 × 100 pixels and a 96 dpi resolution was created. Then, the proposed model and several well-known CNN-based models were tested on the created dataset. The results demonstrate that the proposed model achieved 95.8% accuracy, outperforming Inception v3, Inception-ResNet v2, Xception, and VGG19. While DenseNet121 and ResNet50 achieved better accuracy, their complexity in terms of higher trainable parameters, layers, and training times might be a significant concern in practice. In this context, the results suggest that the proposed model could be a feasible option for IoT devices used in ITS applications due to its simple architecture.

Utility of Spectral Filtering to Improve the Reliability of Marine Fauna Detections from Drone-Based Monitoring.

Monitoring marine fauna is essential for mitigating the effects of disturbances in the marine environment, as well as reducing the risk of negative interactions between humans and marine life. Drone-based aerial surveys have become popular for detecting and estimating the abundance of large marine fauna. However, sightability errors, which affect detection reliability, are still apparent. This study tested the utility of spectral filtering for improving the reliability of marine fauna detections from drone-based monitoring. A series of drone-based survey flights were conducted using three identical RGB (red-green-blue channel) cameras with treatments: (i) control (RGB), (ii) spectrally filtered with a narrow 'green' bandpass filter (transmission between 525 and 550 nm), and, (iii) spectrally filtered with a polarising filter. Video data from nine flights comprising dolphin groups were analysed using a machine learning approach, whereby ground-truth detections were manually created and compared to AI-generated detections. The results showed that spectral filtering decreased the reliability of detecting submerged fauna compared to standard unfiltered RGB cameras. Although the majority of visible contrast between a submerged marine animal and surrounding seawater (in our study, sites along coastal beaches in eastern Australia) is known to occur between 515-554 nm, isolating the colour input to an RGB sensor does not improve detection reliability due to a decrease in the signal to noise ratio, which affects the reliability of detections.

Abstract 18846: A Noninvasive Smartphone Assessment of Aortic Arch Pulse Wave Velocity and Total Arterial Compliance

Introduction: Several clinical studies including a large multiethnic population (Dallas Heart Study) have shown that total arterial compliance (TAC) and aortic arch pulse wave velocity (PWV aa ) are predictors of cardiovascular disease (CVD) events. Dallas Heart Study also showed that PWV aa predicts brain white matter hyperintensity volume independent of cardiovascular risk factors. Recently, Intrinsic Frequency (IF) analysis of carotid pressure waveform from the Framingham Heart Study was used to predict heart failure (HF) events and CVD (Hypertension, 2021 PMID: 33390053). Aim: Our goal was to show the IF method can determine PWV aa and TAC when applied on carotid pulse waveforms obtained from a standard iPhone camera. Methods: The clinical cohort consisted of 120 individuals (40% women) with ages from 20-92 yrs (mean 53 ± 18), including 54 healthy and 66 patients with CVD (22 HF). Phase-contrast magnetic resonance imaging (PC-MRI) and carotid pressure tonometry data were used to compute TAC. PWV aa values were obtained using PC-MRI flow in ascending and descending aorta (not achieved in 1 patients). A custom Apple iPhone 5S application was used to obtain carotid pulse waves by holding the camera against the neck. IF parameters of iPhone waveforms (calibrated by cuff brachial pressures) were used in machine learning algorithms (IF-iPhone). The models of TAC and PWV aa were developed using 100 participants (99 for PWV aa ) and blindly tested on an additional 20 individuals. Results: In blind tests, iPhone-IF models showed Pearson correlations of r=0.89 and r=0.84 with the measured values of TAC and PWV aa , respectively (Fig.1). Conclusions: Assessment of PWV aa and TAC can be accurately achieved using an unmodified smartphone (iPhone). PWV aa evaluation with an iPhone can be used to identify, predict, and quantify risk of vascular brain damage. A Noninvasive iPhone-based method can be used in a clinical or home setting to routinely assess TAC in the general population or in HF.

TelePi: an affordable telepathology microscope camera system anyone can build and use

Telepathology facilitates histological diagnoses through sharing expertise between pathologists. However, the associated costs are high and frequently prohibitive, especially in low-resource settings, where telepathology would paradoxically be of paramount importance due to a paucity of pathologists.We have constructed a telepathology system (TelePi) with a budget of < €120 using the small, single-board computer Raspberry Pi Zero and its High-Quality Camera Module in conjunction with a standard microscope and open-source software. The system requires no maintenance costs or service contracts, has a small footprint, can be moved and shared across several microscopes, and is independent from other computer operating systems. TelePi uses a responsive and high-resolution web-based live stream which allows remote consultation between two or more locations. TelePi can serve as a telepathology system for remote diagnostics of frozen sections. Additionally, it can be used as a standard microscope camera for teaching of medical students and for basic research. The quality of the TelePi system compared favorable to a commercially available telepathology system that exceed its cost by more than 125-fold. Additionally, still images are of publication quality equal to that of a whole slide scanner that costs 800 times more.In summary, TelePi is an affordable, versatile, and inexpensive camera system that potentially enables telepathology in low-resource settings without sacrificing image quality.

Evaluation of functional tests performance using a camera-based and machine learning approach.

The objective of this study is to evaluate the performance of functional tests using a camera-based system and machine learning techniques. Specifically, we investigate whether OpenPose and any standard camera can be used to assess the quality of the Single Leg Squat Test and Step Down Test functional tests. We recorded these exercises performed by forty-six healthy subjects, extract motion data, and classify them to expert assessments by three independent physiotherapists using 15 binary parameters. We calculated ranges of movement in Keypoint-pair orientations, joint angles, and relative distances of the monitored segments and used machine learning algorithms to predict the physiotherapists' assessments. Our results show that the AdaBoost classifier achieved a specificity of 0.8, a sensitivity of 0.68, and an accuracy of 0.7. Our findings suggest that a camera-based system combined with machine learning algorithms can be a simple and inexpensive tool to assess the performance quality of functional tests.

Practical quantum imaging with undetected photons

Infrared (IR) imaging is invaluable across many scientific disciplines, from material analysis to diagnostic medicine. However, applications are often limited by detector cost, resolution and sensitivity, noise caused by the thermal IR background, and the cost, portability and tunability of infrared sources. Here, we describe a compact, portable, and low-cost system that is able to image objects at IR wavelengths without an IR source or IR detector. This imaging with undetected photons (IUP) approach uses quantum interference and correlations between entangled photon pairs to transfer image information from the IR to a wavelength which can be detected with a standard silicon camera. We also demonstrate a rapid analysis approach to acquire both phase and transmission image information. These developments provide an important step towards making IUP a commercially viable technique.

Needle versus Cannula to Treat Tear Trough: A Prospective Study Comparing both Methods.

We aimed to clinically compare needle and cannula techniques in vivo with ultrasound and magnetic resonance imaging, to better understand the best technique and adverse events. Ten adults without previous fillers in the area were injected with hyaluronic acid (CPM 22.5 HA mg/mL) with a 25G cannula on one side and a Becton Dickinson syringe needle (31G 0.3 mL) on the other. The product was fractionated among two visits. Assessment was made at time 0d, 14d, 30d, 60d, 90d, 180d, and 365d with standard camera, Vectra H2, ultrasound, and magnetic resonance imaging. Level of satisfaction was also evaluated. All the patients showed natural results with both techniques and a high satisfaction index. Cannulas were minimally less traumatic in terms of bruises. However, the product was applied in a more superficial layer and in a less precise manner, despite the fact that a deep technique was used with cannulas, depositing the product on the orbital bone. Patients reported a more noticeable change immediately after the procedure on the side treated with cannulas but less edema and a more comfortable procedure on the Becton Dickinson syringe treated side. None of the patients required hyaluronidase to dissolve overcorrection of the area, and no severe complications were observed. The product remained in most of them at day 365. Cannulas seem to be less traumatic regarding bruises, but less precise in vivo. Thin needles seem to be more precise with minimal trauma. However, this difference disappears during patient's evolution.

Intra-abdominal laparoscopic assessment of organs perfusion using imaging photoplethysmography.

An objective evaluation of the functional state and viability of biological tissues during minimally invasive surgery remains unsolved task. Various non-contact methods for evaluating perfusion during laparoscopic surgery are discussed in the literature, but so far there have been no reports of their use in clinical settings. Imaging photoplethysmography (iPPG) is a new method for quantitative assessment of perfusion distribution along the tissue. This is the first study in which we demonstrate successful use of iPPG to assess perfusion of organs during laparoscopic surgery in an operation theater. We used a standard rigid laparoscope connected to a standard digital monochrome camera, and abdominal organs were illuminated by green light. A distinctive feature is the synchronous recording of video frames and electrocardiogram with subsequent correlation data processing. During the laparoscopically assisted surgeries in nine cancer patients, the gradient of perfusion of the affected organs was evaluated. In particular, measurements were carried out before preparing a part of the intestine or stomach for resection, after anastomosis, or during physiological tests. The spatial distribution of perfusion and its changes over time were successfully measured in all surgical cases. In particular, perfusion gradient of an intestine before resection was visualized and quantified by our iPPG laparoscope in all respective cases. It was also demonstrated that systemic administration of norepinephrine leads to a sharper gradient between well and poorly perfused areas of the colon. In four surgical cases, we have shown capability of the laparoscopic iPPG system for intra-abdominal assessment of perfusion in the anastomosed organs. Moreover, good repeatability of continuous long-term measurements of tissue perfusion inside the abdominal cavity was experimentally demonstrated. Our study carried out in real clinical settings has shown that iPPG laparoscope is feasible for intra-abdominal visualization and quantitative assessment of perfusion distribution.

Estimating depth from RGB images using deep-learning for robotic applications in apple orchards

Vision-enabled robotic approaches for apple orchard management have been widely studied in recent years. It is essential for the vision-system to capture the depth information of the canopies for improved understanding of the geometric relations between objects in the orchard environment, which is essential for safe and efficient operations of robots. Unfortunately, depth-enabled sensors are more expensive and less ubiquitous compared to standard RGB cameras, thus limiting the accessibility of depth cues. This study demonstrates that a data-driven approach using a conditional generative adversarial network (cGAN), known as Pix2Pix can estimate depth from RGB images of orchards acquired from a monocular camera. The Pix2Pix network was modified to generate a depth channel when a standard RGB image was given as input. The network was trained and tested for their efficacy using images acquired from two different apple cultivation systems and camera models. The results demonstrated that the model can generate depth estimates comparable to the actual depth channel with a root-mean-squared error (RMSE) of 1.83 cm (corresponding to a relative error of 3.5%). Moreover, a high structural similarity measure index (> 0.55) and commensurate textural features were observed between the actual depth image and the predicted depth image. The results showed that the use of the Pix2Pix model for producing rational depth maps of fruit orchards with monocular cameras is a viable alternative to the use of relatively more expensive RGB-D sensors for obtaining depth information.

Real-time eye blink detection using general cameras: a facial landmarks approach

Eyes are essential in Human-Computer Interaction (HCI) as they provide valuable insights into a person's thoughts and intentions. However, current eye movement analysis methods require specialized equipment or high-quality videos, making them less accessible and usable. This paper proposes a real-time eye blink detection algorithm that uses standard cameras, making it widely applicable and convenient. The approach achieves accurate and efficient eye blink detection by leveraging the Eye Aspect Ratio (EAR) algorithm and facial landmarks technique. This paper developed a Python application and conducted tests using a laptop webcam to validate the performance and practicality of the algorithm across various settings. The algorithm's effectiveness depends on carefully tuning parameters such as threshold and frame rate. The results demonstrate the algorithm's potential in real-time eye blink detection, with potential applications in drowsiness detection during driving, prevention of Computer Vision Syndrome, and assistance for individuals with disabilities. By enabling eye blink detection using commonly available cameras, the algorithm paves the way for integrating eye movement analysis into everyday devices and systems, enhancing user experience and enabling more natural interactions. Further refinement and optimization of this approach hold promise for a wide range of applications in HCI, healthcare, and beyond, opening up new possibilities for research and innovation.

Feature-enhanced X-ray imaging using fused neural network strategy with designable metasurface

Abstract Scintillation-based X-ray imaging can provide convenient visual observation of absorption contrast by standard digital cameras, which is critical in a variety of science and engineering disciplines. More efficient scintillators and electronic postprocessing derived from neural networks are usually used to improve the quality of obtained images from the perspective of optical imaging and machine vision, respectively. Here, we propose to overcome the intrinsic separation of optical transmission process and electronic calculation process, integrating the imaging and postprocessing into one fused optical–electronic convolutional autoencoder network by affixing a designable optical convolutional metasurface to the scintillator. In this way, the convolutional autoencoder was directly connected to down-conversion process, and the optical information loss and training cost can be decreased simultaneously. We demonstrate that feature-specific enhancement of incoherent images is realized, which can apply to multi-class samples without additional data precollection. Hard X-ray experimental validations reveal the enhancement of textural features and regional features achieved by adjusting the optical metasurface, indicating a signal-to-noise ratio improvement of up to 11.2 dB. We anticipate that our framework will advance the fundamental understanding of X-ray imaging and prove to be useful for number recognition and bioimaging applications.

Authenticity detection of eye images using deep learning technology in online exams

The use of online education technologies is one of the most effective ways to achieve the universal goal of quality education throughout life for the sustainable development of UNESCO. During the writing of the article, the features that have appread in the field of application of online exam technologies will be described. The existing forms, methods and tools for conducting the exam in an online format, their potential and risks are analyzed. Attention is drawn to the fact that the methods and tools used for conducting online control have disadvantages, such as significant costs in organizing proctoring and difficulties in assessing one of the student’s key competencies, namely, readiness to use systematized theoretical and practical knowledge to formulate and solve research problems. This research introduces an innovative proctoring system that harnesses deep learning techniques, specifically the Yolo algorithm. The primary objective of this system is to accurately detect the eyes of the exam-taker by utilizing a standard web camera as the sole input source. The obtained results from the deep learning model are subsequently subjected to processing through the Cascaded Haar Classifier to yield a definitive outcome. The proposed approach demonstrates considerable proficiency in discriminating between authentic exam-takers and static images, achieving a notable level of accuracy. Leveraging the capabilities of deep learning in conjunction with the cascaded classifier enables real-time eye detection and localization within images or video frames, thereby establishing the proctoring system as a reliable and effective solution for ensuring academic integrity during online examinations.

Using the community-based breeding program (CBBP) model as a collaborative platform to develop the African Goat Improvement Network-Image collection protocol (AGIN-ICP) with mobile technology for data collection and management of livestock phenotypes.

Introduction: The African Goat Improvement Network Image Collection Protocol (AGIN-ICP) is an accessible, easy to use, low-cost procedure to collect phenotypic data via digital images. The AGIN-ICP collects images to extract several phenotype measures including health status indicators (anemia status, age, and weight), body measurements, shapes, and coat color and pattern, from digital images taken with standard digital cameras or mobile devices. This strategy is to quickly survey, record, assess, analyze, and store these data for use in a wide variety of production and sampling conditions. Methods: The work was accomplished as part of the multinational African Goat Improvement Network (AGIN) collaborative and is presented here as a case study in the AGIN collaboration model and working directly with community-based breeding programs (CBBP). It was iteratively developed and tested over 3years, in 12 countries with over 12,000 images taken. Results and discussion: The AGIN-ICP development is described, and field implementation and the quality of the resulting images for use in image analysis and phenotypic data extraction are iteratively assessed. Digital body measures were validated using the PreciseEdge Image Segmentation Algorithm (PE-ISA) and software showing strong manual to digital body measure Pearson correlation coefficients of height, length, and girth measures (0.931, 0.943, 0.893) respectively. It is critical to note that while none of the very detailed tasks in the AGIN-ICP described here is difficult, every single one of them is even easier to accidentally omit, and the impact of such a mistake could render a sample image, a sampling day's images, or even an entire sampling trip's images difficult or unusable for extracting digital phenotypes. Coupled with tissue sampling and genomic testing, it may be useful in the effort to identify and conserve important animal genetic resources and in CBBP genetic improvement programs by providing reliably measured phenotypes with modest cost. Potential users include farmers, animal husbandry officials, veterinarians, regional government or other public health officials, researchers, and others. Based on these results, a final AGIN-ICP is presented, optimizing the costs, ease, and speed of field implementation of the collection method without compromising the quality of the image data collection.

Modified Allen Test Assessment via Imaging Photoplethysmography

Abstract The modified Allen test (MAT) is a widely used tool to examine the perfusion capacities of the arteries (ulnar or radial) of the hand. In clinical practice, MAT is utilized to ensure that in the event of occlusion, remaining vessels guarantee sufficient hand perfusion. This work shows that perfusion assessment via imaging photoplethysmography (iPPG) can be used to assess MAT. We captured the hands of seven subjects with a standard RGB camera while performing a MAT. Out of these captured video recordings, the iPPG signal was extracted via plane-orthogonal-to-skin transformation. Based on these extracted signals, we derive the heart rate, signal-to-noise ratio, DC parameter, and correlation with a reference iPPG signal to determine if an Allen test was pathological or inconspicuous. For all seven subjects, the results of our MAT assessment were identical to the medical expert’s assessment. This paper presents a procedure and initial thresholds for objectively evaluating and quantifying MAT.We show how globally and locally calculated parameters can be used to assess the perfusion of the investigated hand. This analysis makes it possible to quantify the exact point in time when the perfusion is again sufficient. If a reperfusion problem occurs, we are able to localize the spatial regions of occurrence. The presented hardware setup is affordable and can be easily installed and used in every doctor’s office.

Tear biomarkers for Alzheimer’s disease screening and diagnosis (the TearAD study): design and rationale of an observational longitudinal multicenter study

BackgroundAlzheimer’s disease (AD) is the most common cause of dementia, and due to increasing life expectancy the number of patients is expected to grow. The diagnosis of AD involves the use of biomarkers determined by an amyloid PET scan or cerebrospinal fluid analyses that are either invasive or expensive, and not available in each hospital, thus limiting their usage as a front-line screener. The TearAD study aims to use tear fluid as a potential source for AD biomarkers. In previous reports, we demonstrated that AD biomarkers amyloid-beta and tau, are measurable in tear fluid and are associated with disease severity and neurodegeration. This study aims to validate previous results in a larger cohort and evaluate the diagnostic accuracy of tear biomarkers to discriminate between individuals with and without neurodegeneration as determined by hippocampal atrophy.MethodsThe TearAD study is an observational longitudinal multi-center study that will enroll 50 cognitively healthy controls, 50 patients with subjective cognitive decline, 50 patients with mild cognitive impairment and 50 patients with AD dementia from the memory clinic. Participants will be examined at baseline, after one year, and after two years follow-up. Study assessments include neuropsychological tests and ophthalmic examination. All participants will receive a MRI scan, and a subset of the study population will undergo cerebral spinal fluid collection and an amyloid PET scan. Tear fluid will be collected with Schirmer strips and levels of Aβ38, Aβ40, Aβ42, t-tau and p-tau in tear fluid will be determined using multiplex immunoassays. Blood samples will be collected from all participants. Images of the retina will be obtained with a standard, hyperspectral and ultra-wide field fundus camera. Additionally, macular pigment optical density will be measured with the macular pigment reflectometer, and cross-sectional images of the retina will be obtained through optical coherence tomography imaging.DiscussionThe TearAD study will provide insight into the potential diagnostic use of tear biomarkers as a minimally invasive and low cost tool for the screening and diagnosis of AD.Trial registrationRetrospectively registered at clinicaltrials.gov (NCT05655793).