Mammalian lures monitored with time-lapse cameras increase detection of pythons and other snakes.

Background: Enhancing detection of cryptic snakes is critical for the development of conservation and management strategies; yet, finding methods that provide adequate detection remains challenging. Issues with detecting snakes can be particularly problematic for some species, like the invasive Burmese python (Python bivittatus) in the Florida Everglades. Methods: Using multiple survey methods, we predicted that our ability to detect pythons, larger snakes and all other snakes would be enhanced with the use of live mammalian lures (domesticated rabbits; Oryctolagus cuniculus). Specifically, we used visual surveys, python detection dogs, and time-lapse game cameras to determine if domesticated rabbits were an effective lure. Results: Time-lapse game cameras detected almost 40 times more snakes (n = 375, treatment = 245, control = 130) than visual surveys (n = 10). We recorded 21 independent detections of pythons at treatment pens (with lures) and one detection at a control pen (without lures). In addition, we found larger snakes, and all other snakes were 165% and 74% more likely to be detected at treatment pens compared to control pens, respectively. Time-lapse cameras detected almost 40 times more snakes than visual surveys; we did not detect any pythons with python detection dogs. Conclusions: Our study presents compelling evidence that the detection of snakes is improved by coupling live mammalian lures with time-lapse game cameras. Although the identification of smaller snake species was limited, this was due to pixel resolution, which could be improved by changing the camera focal length. For larger snakes with individually distinctive patterns, this method could potentially be used to identify unique individuals and thus allow researchers to estimate population dynamics.

Comparison of 21 artificial intelligence algorithms in automated diabetic retinopathy screening using handheld fundus camera.

BACKGROUND: Diabetic retinopathy (DR) is a common complication of diabetes and may lead to irreversible visual loss. Efficient screening and improved treatment of both diabetes and DR have amended visual prognosis for DR. The number of patients with diabetes is increasing and telemedicine, mobile handheld devices and automated solutions may alleviate the burden for healthcare. We compared the performance of 21 artificial intelligence (AI) algorithms for referable DR screening in datasets taken by handheld Optomed Aurora fundus camera in a real-world setting. PATIENTS AND METHODS: Prospective study of 156 patients (312 eyes) attending DR screening and follow-up. Both papilla- and macula-centred 50° fundus images were taken from each eye. DR was graded by experienced ophthalmologists and 21 AI algorithms. RESULTS: Most eyes, 183 out of 312 (58.7%), had no DR and mild NPDR was noted in 21 (6.7%) of the eyes. Moderate NPDR was detected in 66 (21.2%) of the eyes, severe NPDR in 1 (0.3%), and PDR in 41 (13.1%) composing a group of 34.6% of eyes with referable DR. The AI algorithms achieved a mean agreement of 79.4% for referable DR, but the results varied from 49.4% to 92.3%. The mean sensitivity for referable DR was 77.5% (95% CI 69.1-85.8) and specificity 80.6% (95% CI 72.1-89.2). The rate for images ungradable by AI varied from 0% to 28.2% (mean 1.9%). Nineteen out of 21 (90.5%) AI algorithms resulted in grading for DR at least in 98% of the images. CONCLUSIONS: Fundus images captured with Optomed Aurora were suitable for DR screening. The performance of the AI algorithms varied considerably emphasizing the need for external validation of screening algorithms in real-world settings before their clinical application.

What is already known on this topic? Diabetic retinopathy (DR) is a common complication of diabetes. Efficient screening and timely treatment are important to avoid the development of sight-threatening DR. The increasing number of patients with diabetes and DR poses a challenge for healthcare.What this study adds? Telemedicine, mobile handheld devices and artificial intelligence (AI)-based automated algorithms are likely to alleviate the burden by improving efficacy of DR screening programs. Reliable algorithms of high quality exist despite the variability between the solutions.How this study might affect research, practice or policy? AI algorithms improve the efficacy of screening and might be implemented to clinical use after thorough validation in a real-life setting.

Optical Camera Communications in Healthcare: A Wearable LED Transmitter Evaluation during Indoor Physical Exercise.

This paper presents an experimental evaluation of a wearable light-emitting diode (LED) transmitter in an optical camera communications (OCC) system. The evaluation is conducted under conditions of controlled user movement during indoor physical exercise, encompassing both mild and intense exercise scenarios. We introduce an image processing algorithm designed to identify a template signal transmitted by the LED and detected within the image. To enhance this process, we utilize the dynamics of controlled exercise-induced motion to limit the tracking process to a smaller region within the image. We demonstrate the feasibility of detecting the transmitting source within the frames, and thus limit the tracking process to a smaller region within the image, achieving an reduction of 87.3% for mild exercise and 79.0% for intense exercise.

Using a smartphone-based digital fundus camera for screening of retinal and optic nerve diseases in veterinary medicine: A preliminary investigation.

BACKGROUND: Ophthalmoscopy is a valuable tool in clinical practice. We report the use of a novel smartphone-based handheld device for visualisation and photo-documentation of the ocular fundus in veterinary medicine. METHODS: Selected veterinary patients of a referral ophthalmology service were included if one or both eyes had clear ocular media, allowing for examination of the fundus. Following pharmacological mydriasis, fundic images were obtained with a handheld fundus camera (Volk VistaView). For comparison, the fundus of a subset of animals was also imaged with a veterinary-specific fundus camera (Optomed Smartscope VET2). RESULTS: The large field of view achieved by the Volk VistaView allowed for rapid and thorough observation of the ocular fundus in animals, providing a tool to visualise and record common pathologies of the posterior segment. Captured fundic images were sometimes overexposed, with the tapetal fundus artificially appearing hyperreflective when using the Volk VistaView camera, a finding that was less frequent when activating a 'veterinary mode' that reduced the sensitivity of the camera's sensor. The Volk VistaView compared well with the Optomed Smartscope VET2. LIMITATION: The main study limitation was the small sample size. CONCLUSIONS: The Volk VistaView camera was easy to use and provided good-quality fundic images in veterinary patients with healthy or diseased eyes, offering a wide field of view that was ideal for screening purposes.

A Preliminary Evaluation of the Diagnostic Performance of a Smartphone-Based Machine Learning-Assisted System for Evaluation of Clinical Activity Score in Digital Images of Thyroid-Associated Orbitopathy.

Background: We previously developed a machine learning (ML)-assisted system for predicting the clinical activity score (CAS) in thyroid-associated orbitopathy (TAO) using digital facial images taken by a digital single-lens reflex camera in a studio setting. In this study, we aimed to apply this system to smartphones and detect active TAO (CAS ≥3) using facial images captured by smartphone cameras. We evaluated the performance of our system on various smartphone models and compared it with the performance of ophthalmologists with varying clinical experience. Methods: We applied the preexisting ML architecture to classify photos taken with smartphones (Galaxy S21 Ultra, iPhone 12 pro, iPhone 11, iPhone SE 2020, Galaxy M20, and Galaxy A21S). The performance was evaluated with smartphone-captured images from 100 patients with TAO. Three ophthalmology residents, three general ophthalmologists with <5 years of clinical experience, and three oculoplastic specialists independently interpreted the same set of images taken under a studio environment and compared their results with those generated by the smartphone-based ML-assisted system. Reference CAS was determined by a consensus of three oculoplastic specialists. Results: Active TAO (CAS ≥3) was identified in 28 patients. Smartphone model used in capturing facial images influenced active TAO detection performance (F1 score 0.59-0.72). The smartphone-based system showed 74.5% sensitivity, 84.8% specificity, and F1 score 0.70 on top three smartphones. On images from all six smartphones, average sensitivity, specificity, and F1 score were 71.4%, 81.6%, and 0.66, respectively. Ophthalmology residents' values were 69.1%, 55.1%, and 0.46. General ophthalmologists' values were 61.9%, 79.6%, and 0.55. Oculoplastic specialists' values were 73.8%, 90.7%, and 0.75. This smartphone-based ML-assisted system predicted CAS within 1 point of reference CAS in 90.7% using facial images from smartphones. Conclusions: Our smartphone-based ML-assisted system shows reasonable accuracy in detecting active TAO, comparable with oculoplastic specialists and outperforming residents and general ophthalmologists. It may enable reliable self-monitoring for disease activity, but confirmatory research is needed for clinical application.

A systematic review of camera monitor system display layout designs: Integration of existing knowledge.

Despite the growing interest in mirrorless vehicles equipped with a camera monitor system (CMS), the human factors research findings on CMS display layout design have not been synthesized yet, hindering the application of the knowledge and the identification of future research directions. In an effort to address the 'lack of integration of the existing knowledge', this literature review addresses the following research questions: 1) what CMS display layout designs have been considered/developed by academic researchers and by automakers, respectively?; 2) among possible CMS display layout design alternatives, which ones have not yet been examined through human factors evaluation studies?; and 3) how do the existing human factors studies on the evaluation of different CMS display layout designs vary in the specifics of research? This review provides significant implications for the ergonomic design of CMS display layouts, including some potential design opportunities and future research directions.

Wide-field imaging with smartphone based fundus camera: grading of severity of diabetic retinopathy and locating peripheral lesions in diabetic retinopathy.

AIM: To assess the performance of smartphone based wide-field retinal imaging (WFI) versus ultra-wide-field imaging (UWFI) for assessment of sight-threatening diabetic retinopathy (STDR) as well as locating predominantly peripheral lesions (PPL) of DR. METHODS: Individuals with type 2 diabetes with varying grades of DR underwent nonmydriatic UWFI with Daytona Plus camera followed by mydriatic WFI with smartphone-based Vistaro camera at a tertiary care diabetes centre in South India in 2021-22. Grading of DR as well as identification of PPL (DR lesions beyond the posterior pole) in the retinal images of both cameras was performed by senior retina specialists. STDR was defined by the presence of severe non-proliferative DR, proliferative DR or diabetic macular oedema (DME). The sensitivity and specificity of smartphone based WFI for detection of PPL and STDR was assessed. Agreement between the graders for both cameras was compared. RESULTS: Retinal imaging was carried out in 318 eyes of 160 individuals (mean age 54.7 ± 9 years; mean duration of diabetes 16.6 ± 7.9 years). The sensitivity and specificity for detection of STDR by Vistaro camera was 92.7% (95% CI 80.1-98.5) and 96.6% (95% CI 91.5-99.1) respectively and 95.1% (95% CI 83.5-99.4) and 95.7% (95% CI 90.3-98.6) by Daytona Plus respectively. PPL were detected in 89 (27.9%) eyes by WFI by Vistaro camera and in 160 (50.3%) eyes by UWFI. However, this did not translate to any significant difference in the grading of STDR between the two imaging systems. In both devices, PPL were most common in supero-temporal quadrant (34%). The prevalence of PPL increased with increasing severity of DR with both cameras (p < 0.001). The kappa comparison between the 2 graders for varying grades of severity of DR was 0.802 (p < 0.001) for Vistaro and 0.753 (p < 0.001) for Daytona Plus camera. CONCLUSION: Mydriatic smartphone-based widefield imaging has high sensitivity and specificity for detecting STDR and can be used to screen for peripheral retinal lesions beyond the posterior pole in individuals with diabetes.

Smartphone fundus photography by a physician in medical emergencies: An analytical cross-sectional study of 182 patients.

Background Fundus examination is an integral part of the clinical evaluation of patients with medical emergencies. It is done at the bedside using a portable direct ophthalmoscope. Smartphone fundus photography (SFP) is a novel technique of retinal imaging. We evaluated the use of SFP by a physician in medical emergencies and compared it with direct ophthalmoscopy (DO) findings of the ophthalmologist. Methods We did a prospective study on patients admitted with medical emergencies with an indication for fundus examination. The SFP was done by the physician, and its findings were noted. These were compared with the DO findings of the ophthalmologist. Results Of the 182 patients studied, 111 (61%) had fundus findings by SFP and 95 (52.5%) by DO. Papilloedema (21.4%), haemorrhages (20%) and Roth spots (12.5%) were most common. DO missed early papilloedema and findings in the peripheral retina. Conclusions SFP is as effective as DO, in detecting retinal findings in patients with medical emergencies and can be performed at the bedside by the physician.

Reflection-Aware Generation and Identification of Square Marker Dictionaries.

Square markers are a widespread tool to find correspondences for camera localization because of their robustness, accuracy, and detection speed. Their identification is usually based on a binary encoding that accounts for the different rotations of the marker; however, most systems do not consider the possibility of observing reflected markers. This case is possible in environments containing mirrors or reflective surfaces, and its lack of consideration is a source of detection errors, which is contrary to the robustness expected from square markers. This is the first work in the literature that focuses on reflection-aware square marker dictionaries. We present the derivation of the inter-marker distance of a reflection-aware dictionary and propose new algorithms for generating and identifying such dictionaries. Additionally, part of the proposed method can be used to optimize preexisting dictionaries to take reflection into account. The experimentation carried out demonstrates how our proposal greatly outperforms the most popular predefined dictionaries in terms of inter-marker distance and how the optimization process significantly improves them.

Dual stereo-digital image correlation system for simultaneous measurement of overlapped wings with a polarization RGB camera and fluorescent speckle patterns.

Simultaneous monitoring of overlapped multi-wing structure by stereo-digital image correlation (stereo-DIC) may be used to quantify insect motion and deformation. We propose a dual stereo-DIC system based on multispectral imaging with a polarization RGB camera. Different fluorescent speckle patterns were fabricated on wings, which emit red and blue spectra under ultraviolet light that were imaged and separated using a polarization RGB camera and auxiliary optical splitting components. The resulting dual stereo-DIC system was validated through translation experiments with transparent sheets and reconstructed overlapped insect wings (cicadas). Dynamic measurements of the Ruban artificial flier indicate the efficacy of this approach to determining real insect flight behavior.

Evaluation of photography using head-mounted display technology (ICAPS) for district Trachoma surveys.

BACKGROUND: As the prevalence of trachoma declines worldwide, it is becoming increasingly expensive and challenging to standardize graders in the field for surveys to document elimination. Photography of the tarsal conjunctiva and remote interpretation may help alleviate these challenges. The purpose of this study was to develop, and field test an Image Capture and Processing System (ICAPS) to acquire hands-free images of the tarsal conjunctiva for upload to a virtual reading center for remote grading. METHODOLOGY/PRINCIPAL FINDINGS: This observational study was conducted during a district-level prevalence survey for trachomatous inflammation-follicular (TF) in Chamwino, Tanzania. The ICAPS was developed using a Samsung Galaxy S8 smartphone, a Samsung Gear VR headset, a foot pedal trigger and customized software allowing for hands-free photography. After a one-day training course, three trachoma graders used the ICAPS to collect images from 1305 children ages 1-9 years, which were expert-graded remotely for comparison with field grades. In our experience, the ICAPS was successful at scanning and assigning barcodes to images, focusing on the everted eyelid with adequate examiner hand visualization, and capturing images with sufficient detail to grade TF. The percentage of children with TF by photos and by field grade was 5%. Agreement between grading of the images compared to the field grades at the child level was kappa = 0.53 (95%CI = 0.40-0.66). There were ungradable images for at least one eye in 199 children (9.1%), with more occurring in children ages 1-3 (18.5%) than older children ages 4-9 (4.2%) (χ2 = 145.3, p<0.001). CONCLUSIONS/SIGNIFICANCE: The prototype ICAPS device was robust, able to image 1305 children in a district level survey and transmit images from rural Tanzania to an online grading platform. More work is needed to improve the percentage of ungradable images and to better understand the causes of disagreement between field and photo grading.

Development of 3D Printed Smartphone-Based Multi-Purpose Fundus Camera (MultiScope) for Retinopathy of Prematurity.

Retinopathy of Prematurity (ROP) is a prominent source of low vision and blindness in preterm babies. Wide-Field Digital Retinal Imaging (WFDRI) systems acquire accurate digital images which are very useful for identification, documentation and transmitting the various retinal diseases. This telemedicine technique has potential for an alternative tool for Binocular Indirect Ophthalmoscopy (BIO) in ROP screening, but it is very expensive and accessibility for poor communities is limited. Capabilities of good illumination, high resolution camera and processing speed of the modern smartphones are being identified as a substitute. Potential applications of 3D printing is that it provides a severe impact in medical field, especially in ophthalmology sector. Competences of 3D printing are very useful for the development of retinal camera from any smartphone with the help of 3D printable devices. The primary aim of this study is to develop a handheld 3D printed smartphone-based multi-purpose fundus camera for ROP screening. The secondary aim is to check the feasibility and compare the digital fundus images obtained from the developed fundus camera against the commercial RetCam imaging. The proposed cost effective and remote reading device is an alternative to WFDRI for ROP screening and can improve the potential of ROP care for low resource communities.

Partial-field illumination ophthalmoscope: improving the contrast of a camera-based retinal imager.

Effective and accurate in vivo diagnosis of retinal pathologies requires high performance imaging devices, combining a large field of view and the ability to discriminate the ballistic signal from the diffuse background in order to provide a highly contrasted image of the retinal structures. Here, we have implemented the partial-field illumination ophthalmoscope, a patterned illumination modality, integrated to a high pixel rate adaptive optics full-field microscope. This non-invasive technique enables us to mitigate the low signal-to-noise ratio, intrinsic of full-field ophthalmoscopes, by partially illuminating the retina with complementary patterns to reconstruct a wide-field image. This new, to the best of our knowledge, modality provides an image contrast spanning from the full-field to the confocal contrast, depending on the pattern size. As a result, it offers various trade-offs in terms of contrast and acquisition speed, guiding the users towards the most efficient system for a particular clinical application.

Calibration and location analysis of a heterogeneous binocular stereo vision system.

In the dairy farming industry, we can obtain the temperature, color, and location information of dairy cows by patrol inspection robot so as to monitor the health status and abnormal behaviors of dairy cows. We build and calibrate a heterogeneous binocular stereo vision (HBSV) system comprising a high-definition color camera and infrared thermal camera and mount it on a patrol inspection robot. First, based on the traditional chessboard, an easy-to-make calibration board for the HBSV system is designed. Second, an accurate locating and sorting algorithm for the calibration points of the calibration board is designed. Then, the cameras are calibrated and the HBSV system is stereo-calibrated. Finally, target locating is achieved based on the above calibration results and Yolo target detection technology. In this paper, several experiments are carried out from many aspects. The target locating average error of HBSV system is 3.11%, which satisfies the needs of the dairy farming environment. The video's FPS captured by using HBSV is 7.3, which is 78% higher than that by using binocular stereo vision system and infrared thermal camera. The results show that the HBSV system has application value to a certain degree.

ESIDE: A computationally intelligent method to identify earthworm species (E. fetida) from digital images: Application in taxonomy.

Earthworms (Crassiclitellata) being ecosystem engineers significantly affect the physical, chemical, and biological properties of the soil by recycling organic material, increasing nutrient availability, and improving soil structure. The efficiency of earthworms in ecology varies along with species. Therefore, the role of taxonomy in earthworm study is significant. The taxonomy of earthworms cannot reliably be established through morphological characteristics because the small and simple body plan of the earthworm does not have anatomical complex and highly specialized structures. Recently, molecular techniques have been adopted to accurately classify the earthworm species but these techniques are time-consuming and costly. To combat this issue, in this study, we propose a machine learning-based earthworm species identification model that uses digital images of earthworms. We performed a stringent performance evaluation not only through 10-fold cross-validation and on an external validation dataset but also in real settings by involving an experienced taxonomist. In all the evaluation settings, our proposed model has given state-of-the-art performance and justified its use to aid earthworm taxonomy studies. We made this model openly accessible through a cloud-based webserver and python code available at https://sites.google.com/view/wajidarshad/software and https://github.com/wajidarshad/ESIDE.