Field Imaging Research Articles

Imagine Denoising Methods Based on GANs

Abstract. With the continuous development of technology, peoples demand for image clarity is also constantly increasing. Whether in the fields of medical imaging, aerospace, or people's daily lives, noise in images seriously affects their clarity. Therefore, how to remove noise on the premise of preserving image details has now become a hot research topic. In the domain of image denoising, both early spatial domain filtering methods and recently proposed convolutional neural network models have certain limitations. Compared to other denoising methods, GANs can better remove noise from images and improve image quality. Therefore, this article summarizes and organizes image denoising methods based on GAN models. This article explains four GAN based image denoising methods, namely GAN, WGAN, DNGAN, and GCBD, from the perspectives of framework structure, advantages and disadvantages, and application fields. At the same time, this article also analyzes the development trend of GAN application in image denoising and makes prospects.

Depth-resolved imaging through scattering media using time-gated light field tomography.

We present a novel, to the best of our knowledge, approach to overcome the limitations imposed by scattering media using time-gated light field tomography. By integrating the time-gating technique with light field imaging, we demonstrate the ability to capture and reconstruct images with different depths through highly scattering environments. Our method exploits the temporal characteristics of light propagation to selectively isolate ballistic photons, enabling enhanced depth resolution and improved imaging quality. Through comprehensive experimental validation and analysis, we showcase the effectiveness of our technique in resolving depth information with high fidelity, even in the presence of significant scattering. The resultant system can simultaneously acquire multi-angled projections of the object without requiring prior knowledge of the media or the target. This advancement holds promise for a wide range of applications, including non-invasive medical imaging, environmental monitoring, and industrial inspection, where imaging through scattering media is critical for an accurate and reliable analysis.

Coverage Estimation of Droplets Sprayed on Water-Sensitive Papers Based on Domain-Adaptive Segmentation

Unmanned aerial spraying systems (UASSs) are widely used today for the effective control of pests affecting crops, and more advanced UASS techniques are now being developed. To evaluate such systems, artificial targets are typically used to assess droplet coverage through image processing. To evaluate performance accurately, high-quality binary image processing is necessary; however, this involves labor for sample collection, transportation, and storage, as well as the risk of potential contamination during the process. Therefore, rapid assessment in the field is essential. In the present study, we evaluated droplet coverage on water-sensitive papers (WSPs) under field conditions. A dataset was constructed consisting of paired training examples, each comprising source and target data. The source data were high-quality labeled images obtained from WSP samples through image processing, while the target data were aligned RoIs within field images captured in situ. Droplet coverage estimation was performed using an encoder–decoder model, trained on the labeled images, with features adapted to field images via self-supervised learning. The results indicate that the proposed method detected droplet coverage in field images with an error of less than 5%, demonstrating a strong correlation between measured and estimated values (R2 = 0.99). The method proposed in this paper enables immediate and accurate evaluation of the performance of UASSs in situ.

Digital Dark Field-Higher Contrast and Greater Specificity Dark Field Imaging Using a 4DSTEM Approach.

A new method for dark field imaging is introduced, which uses scanned electron diffraction (or 4DSTEM-4-dimensional scanning transmission electron microscopy) datasets as its input. Instead of working on simple summation of intensity, it works on a sparse representation of the diffraction patterns in terms of a list of their diffraction peaks. This is tested on a thin perovskite film containing structural ordering resulting in additional superlattice spots that reveal details of domain structures, and is shown to give much better selectivity and contrast than conventional virtual dark field imaging. It is also shown to work well in polycrystalline aggregates of CuO nanoparticles. In view of the higher contrast and selectivity, and the complete exclusion of diffuse scattering from the image formation, it is expected to be of significant benefit for characterization of a wide variety of crystalline materials.

Vanadium dioxide and a graphene-based ultra-wideband absorption and polarization conversion switchable terahertz device

A multifunctional terahertz functional device based on vanadium dioxide (VO2) and graphene is proposed, which can realize ultra-wideband absorption and polarization conversion. When the VO2 is in the insulating state, the device can achieve the polarization conversion function to convert the incident wave into the corresponding cross-polarized wave. Polarization conversion ratios (PCRs) can exceed 90% in the 2.1–8 THz frequency range; in the 3–7.5 THz range, the PCR can be more than 95%. When VO2 is in the metallic state and the graphene Fermi energy is 0.9 eV, the device can realize a broadband absorption function. An absorption rate of more than 90% can be achieved over a wide frequency range of 3.3–7.7 THz. In addition, the polarization conversion device can maintain high performance in broadband polarization conversion at incident angles no greater than 40°. The absorber device also exhibits insensitivity to both incident and polarization angles. These advantages which make the proposed multifunctional terahertz functional device have a wide range of applications in the fields of terahertz imaging, sensing, communication, and so on.

Semantic Segmentation Based on Geometric Calibration Using AI and AR in Health Care

In the medical field, medical imaging is essential for precise diagnosis, treatment planning, and condition monitoring. The goal of this work is to improve the field of healthcare imaging by investigating the complementary applications of augmented reality (AR) and artificial intelligence (AI) in semantic segmentation. More precise identification and delineation of anatomical structures and abnormalities in medical images is made possible by AI-driven semantic segmentation, opening the door to more precise diagnosis and treatment plans. Modern AI algorithms are employed in the suggested methodology to perform semantic segmentation in a variety of medical imaging modalities, including ultrasound, CT, and MRI scans. The detection of anomalies such as tumours, irregularities in organs, and neurological disorders is made easier by these algorithms. The foundation for integrating augmented reality technologies into the healthcare ecosystem is provided by the segmented medical images. AR enhances the interaction and visualization of segmented medical data in the context of healthcare. Real-time augmented reality overlays help surgeons by improving surgical navigation and precision. Additionally, AR apps help with medical education by offering professionals and students alike an immersive learning environment. AR provides interactive visualizations to help patients understand their medical conditions when they are going for therapy and rehabilitation. The difficulties in integrating these technologies in the healthcare industry are discussed in this paper, with a focus on the significance of data privacy, regulatory compliance, and easy integration with current healthcare systems. The successful deployment of AI technologies necessitates interdisciplinary collaboration among AI developers, healthcare professionals, and AR specialists to ensure compliance with ethical, legal, and clinical standards mandated in the healthcare domain. The proposed Segmentation mask is redefined with geometric calibration and used with U-Net for image segmentation. The segmentation is experimented on Cityscapes and PASCAL VOC 2012 datasets. The experimental results show that proposed semantic segmentation based on geometric calibration yields more accuracy than its counterparts.

SERS-Encoded Nanoprobes Based on Silver-Coated Gold Nanorods for Cell Sorting.

Optically-encoded probes have great potential for applications in the fields of biosensing and imaging. By employing specific encoding methods, these probes enable the detection of multiple target molecules and high-resolution imaging within the same sample. Among the various encoding methods, surface-enhanced Raman scattering (SERS) spectral encoding stands out due to its extremely narrow linewidth. Compared to fluorescence spectral encoding, SERS encoding significantly reduces crosstalk between adjacent peaks, thereby achieving a larger encoding capacity and enabling multi-channel parallel analysis. This article presents the design and construction of two novel sets of SERS-encoded probes based on noble metal core-shell nanostructures. Two different encoding strategies are successfully applied to encode the SERS spectra of the probes: 1D encoding based on the wavenumber of characteristic peaks in the SERS spectrum, and 2D encoding combining both wavenumber and intensity of characteristic peaks in the SERS spectrum. In addition, this study also demonstrates the potential application of 1D encoded probes in cell sorting. These studies verify the feasibility of applying these two encoding methods to SERS core-shell probes and provide new insights into the construction of optically encoded probes.

A frequency reconfigurable antireflection metasurface for GPR

Abstract The antireflection metasurface (AM) is employed in ground penetrating radar (GPR) to mitigate the strong reflection of electromagnetic waves at the air-ground interface due to impedance mismatch. However, due to constraints imposed by the relative bandwidth (RBW) and manufacturing processes, these layers tend to exhibit excessive thickness and bulky shape, narrow RBW, and fixed functionality in a passive configuration. This paper presents a novel, dual-band, independent wideband tuning, frequency reconfigurable AM based on varactor diodes with center frequencies of 1.35 GHz and 2.60 GHz. This metasurface possesses positive properties such as a single layer, the ultrathin thickness (0.03 & 0.06λ), the wide RBW (43.3% & 27.4%) and remarkable antireflection. The aforementioned metasurface achieves the described mechanisms and features through the destructive interference theory and the combine element technique. Numerical simulation results of surface currents and electric field energy power demonstrate the antireflection property. The equivalent electromagnetic parameter retrieval results also provide equivalent impedance conditions for non-perfect antireflection. The proposed AM samples demonstrate notable stepwise frequency reconfigurable properties in free-space experiments. The imaging effect after loading this AM is significantly improved in real-world GPR ballast roadbed anomaly detection experiments. This approach provides significant research value and promising prospects across various disciplines, including the stepped-frequency GPR, microwave imaging, and interdisciplinary fields.

Multifunctional Near-Infrared Luminescence Performance of Nd3+ Doped SrSnO3 Phosphor

The phosphors with persistent luminescence in the NIR (near-infrared) region and the NIR-to-NIR Stokes luminescence properties have received considerable attention owing to their inclusive application prospects in the in vivo imaging field. In this paper, Nd3+ doped SrSnO3 phosphors with remarkable NIR emission performance were prepared using a high temperature solid state reaction method; the phase structure, morphology, and luminescence properties were discussed systematically. The SrSnO3 host exhibits broadband NIR emission (800–1300 nm) with absorptions in the near ultraviolet region. Nd3+ ions emerge excellent NIR-to-NIR Stokes luminescence under 808 nm laser excitation, with maximum emission at around ~1068 nm. The concentration-dependent luminescence properties, temperature dependent emission, and the luminescence decay curves of Nd3+ in the SrSnO3 host were also studied. The Nd3+ doped SrSnO3 phosphors exhibit exceptional thermal stability; the integrated emission intensity can retain approximately 66% at 423 K compared to room temperature. Most importantly, NIR persistent luminescence also can be observed for the SrSnO3:Nd3+ samples, which is in the first and second biological windows. A possible mechanism was proposed for the persistent NIR luminescence of Nd3+ based on the thermo-luminescence spectra. Consequently, the exciting results indicate that multifunctional NIR luminescence has been successfully realized in the SrSnO3:Nd3+ phosphors.

Antithermal-Quenching Near-Infrared Emitting Lu2SrAl4SiO12:Cr3+ Garnet-Type Phosphor for High-Resolution Nonvisual Imaging.

Near-infrared (NIR) light allows fast and nondestructive detection with deep penetration into biological tissues and is widely used in food inspection, biomedical imaging, night vision security, and other fields. Cr3+-doped NIR first region (NIR-I) phosphors have many interesting features that have attracted a lot of attention recently. However, practical issues, such as low photoluminescence quantum efficiency and poor thermal stability, need to be addressed. We synthesized Cr3+-doped Lu2SrAl4SiO12 garnet-type phosphors using a high-temperature solid-state reaction method. Under blue-light (@ 430 nm) excitation, the phosphors exhibited broadband NIR-I emission in the range of 600-1000 nm, with an emission peak at 710 nm. This system is unique, as the emission had multipeak sharp-line superposition and the Cr3+ ions were situated in a relatively strong crystal field environment, as opposed to a weak crystal field environment for other matrix. The optimal doping content of Cr3+ ions was 2 mol %, and its internal quantum efficiency was ∼76.8%. Surprisingly, this NIR phosphor showed an antithermal quenching effect, and the integrated luminescence intensity of NIR-I emission measured at 573 K was 206.3% of that measured at 303 K. We found that the antithermal quenching of NIR-I luminescence was caused by the extremely low thermal expansion coefficient and rigid structure of the Lu2SrAl4SiO12 matrix in the temperature range, as well as the weakening of electron-phonon coupling with increasing temperature. The optimized phosphor was packaged with a blue chip into a NIR phosphor-converted light-emitting diode device. The light source device showed an output power of 119.02 mW and an electro-optical conversion efficiency of 11.02% under a driving current of 300 mA. The application potential of this NIR phosphor was demonstrated in the field of high-resolution nondestructive imaging.

A novel benign and malignant classification model for lung nodules based on multi-scale interleaved fusion integrated network

AbstractOne of the precursors of lung cancer is the presence of lung nodules, and accurate identification of their benign or malignant nature is important for the long-term survival of patients. With the development of artificial intelligence, deep learning has become the main method for lung nodule classification. However, successful deep learning models usually require large number of parameters and carefully annotated data. In the field of medical images, the availability of such data is usually limited, which makes deep networks often perform poorly on new test data. In addition, the model based on the linear stacked single branch structure hinders the extraction of multi-scale features and reduces the classification performance. In this paper, to address this problem, we propose a lightweight interleaved fusion integration network with multi-scale feature learning modules, called MIFNet. The MIFNet consists of a series of MIF blocks that efficiently combine multiple convolutional layers containing 1 × 1 and 3 × 3 convolutional kernels with shortcut links to extract multiscale features at different levels and preserving them throughout the block. The model has only 0.7 M parameters and requires low computational cost and memory space compared to many ImageNet pretrained CNN architectures. The proposed MIFNet conducted exhaustive experiments on the reconstructed LUNA16 dataset, achieving impressive results with 94.82% accuracy, 97.34% F1 value, 96.74% precision, 97.10% sensitivity, and 84.75% specificity. The results show that our proposed deep integrated network achieves higher performance than pre-trained deep networks and state-of-the-art methods. This provides an objective and efficient auxiliary method for accurately classifying the type of lung nodule in medical images.

The Seismic Surface Rupture Zone in the Western Segment of the Northern Margin Fault of the Hami Basin and Its Causal Interpretation, Eastern Tianshan

The Eastern Tianshan region, influenced by the far-field effect of northward compression and expansion of the Qinghai-Xizang block, features highly developed Late Quaternary active faults that exhibit significant neotectonic activity. Historically, the Barkol-Yiwu Basin, located to the north of the Eastern Tianshan, experienced two major earthquakes in 1842 and 1914, each with a magnitude of M71/2. In contrast, the Hami Basin on the southern margin of the Eastern Tianshan has no historical records of any major earthquakes, and its seismic potential, mechanisms, and future earthquake hazards remain unclear. Based on satellite image interpretation and field surveys, this study identified a relatively recent and well-preserved seismic surface rupture zone with good continuity in the Liushugou area of the western segment of the Northern Margin Fault of the Hami Basin (HMNF), which is the seismogenic structure responsible for the rupture. The surface rupture zone originates at Kekejin in the east, extends intermittently westward through Daipuseke Bulake and Liushugou, and terminates at Wuzun Bulake, with a total length of approximately 21 km. The rupture zone traverses the youngest geomorphic surface units, such as river beds or floodplains and first-order terraces (platforms), and is characterized by a series of single or multiple reverse fault scarps. The morphology of fault scarps is clear, presenting a light soil color with heights ranging from 0.15 m to 2.13 m and an average displacement of 0.56 m, suggesting that this surface rupture zone likely represents the most recent seismic event. Comparison with historical earthquake records in the Eastern Tianshan region suggests that the rupture zone may have been formed simultaneously with the Xiongkuer rupture zone by the 1842 M71/2 earthquake along the boundary faults on both sides of the Barkol Mountains, exhibiting a flower-like structural pattern. Alternatively, it might represent a separate, unrecorded seismic event occurring shortly after the 1842 earthquake. The estimated magnitude of the associated earthquake is about 6.6~6.9. Given that surface-rupturing earthquakes have already occurred in the western segment, the study indicates that the Erdaogou–Nanshankou section of the HMNF has surpassed the average recurrence interval for major earthquakes, indicating a potential future earthquake hazard.

The Methodology of Adaptive Levels of Interval for Laser Speckle Imaging

A methodology is proposed for use in the laser speckle imaging field. This methodology modified the graphical and numerical speckle pattern imaging methods to improve their extraction and discrimination capabilities when processing the embedded temporal activity in the images of laser speckle patterns. This is through enabling these methods to adapt the levels of speckle images’ interval during processing to speed up the process and overcome the lack of discrimination when they deal with a complex scattering medium having regions of various scales of activity. The impact of using the new methodology on the imaging methods’ performance was evaluated using graphical and numerical evaluation tests, in addition, an exceptional laser speckle imaging system was designed and implemented to undertake a series of experimental validation tests on this methodology. The evaluation and experimental validation tests show the effectiveness of this methodology on the extraction and discrimination capabilities for the standard imaging speckle pattern methods and prove its ability to provide high performance with the real images of speckle patterns. The results also show an improvement in the processing speed for both graphical and numerical methods when the adaptive levels methodology is applied to them, which reaches 78% for the graphical and 87% for the numerical speckle processing methods.

High-Performance Vis–NIR Photodetectors Based on Two-Dimensional Bi2Te3 Thin Film and Applications

Two-dimensional materials have excellent optoelectronic properties and have great significance in the field of photodetectors. We have prepared a thin film photodetector based on bismuth telluride (Bi2Te3) topological insulator using dual-temperature-zone vapor deposition technology. Due to the high-quality lattice structure of Bi2Te3 and the uniform and dense surface morphology of the Bi2Te3 thin film, the device exhibits excellent photoelectric response and Vis–NIR spectral range. Under 405 nm illumination, the responsivity is 5.6 mA/W, the specific detectivity is 1.22 × 107 Jones, and the response time is 262/328 ms. We designed a photodetector single-point scanning imaging system and successfully achieved high-resolution imaging at a wavelength of 532 nm. This work provides guidance for the application of two-dimensional materials, especially Bi2Te3, in the fields of photodetectors and imaging.

MIGHTEE: The continuum survey data release 1

Abstract The MeerKAT International GHz Tiered Extragalactic Exploration Survey (MIGHTEE) is one of the large survey projects using the MeerKAT telescope, covering four fields that have a wealth of ancillary data available. We present Data Release 1 of the MIGHTEE continuum survey, releasing total intensity images and catalogues over ∼20 deg2, across three fields at ∼1.2-1.3 GHz. This includes 4.2 deg2 over the Cosmic Evolution Survey (COSMOS) field, 14.4 deg2 over the XMM Large-Scale Structure (XMM-LSS) field and deeper imaging over 1.5 deg2 of the Extended Chandra Deep Field South (CDFS). We release images at both a lower resolution (7–9 arcsec) and higher resolution (∼5 arcsec). These images have central rms sensitivities of ∼1.3 −2.7 μJy beam−1 (∼1.2 −3.6 μJy beam−1) in the lower (higher) resolution images respectively. We also release catalogues comprised of ∼144 000 (∼114 000) sources using the lower (higher) resolution images. We compare the astrometry and flux-density calibration with the Early Science data in the COSMOS and XMM-LSS fields and previous radio observations in the CDFS field, finding broad agreement. Furthermore, we extend the source counts at the ∼10 μJy level to these larger areas (∼20 deg2) and, using the areal coverage of MIGHTEE we measure the sample variance for differing areas of sky. We find a typical sample variance of 10-20percnt for 0.3 and 0.5 sq. deg. sub-regions at S1.4 ≤ 200 μJy, which increases at brighter flux densities, given the lower source density and expected higher galaxy bias for these sources.

Identification of sweetpotato virus disease-infected leaves from field images using deep learning

IntroductionSweetpotato virus disease (SPVD) is widespread and causes significant economic losses. Current diagnostic methods are either costly or labor-intensive, limiting both efficiency and scalability.MethodsThe segmentation algorithm proposed in this study can rapidly and accurately identify SPVD lesions from field-captured photos of sweetpotato leaves. Two custom datasets, DS-1 and DS-2, are utilized, containing meticulously annotated images of sweetpotato leaves affected by SPVD. DS-1 is used for training, validation, and testing the model, while DS-2 is exclusively employed to validate the model’s reliability. This study employs a deep learning-based semantic segmentation network, DeepLabV3+, integrated with an Attention Pyramid Fusion (APF) module. The APF module combines a channel attention mechanism with multi-scale feature fusion to enhance the model’s performance in disease pixel segmentation. Additionally, a novel data augmentation technique is utilized to improve recognition accuracy in the edge background areas of real large images, addressing issues of poor segmentation precision in these regions. Transfer learning is applied to enhance the model’s generalization capabilities.ResultsThe experimental results indicate that the model, with 62.57M parameters and 253.92 Giga Floating Point Operations Per Second (GFLOPs), achieves a mean Intersection over Union (mIoU) of 94.63% and a mean accuracy (mAcc) of 96.99% on the DS-1 test set, and an mIoU of 78.59% and an mAcc of 79.47% on the DS-2 dataset.DiscussionAblation studies confirm the effectiveness of the proposed data augmentation and APF methods, while comparative experiments demonstrate the model’s superiority across various metrics. The proposed method also exhibits excellent detection results in simulated scenarios. In summary, this study successfully deploys a deep learning framework to segment SPVD lesions from field images of sweetpotato foliage, which will contribute to the rapid and intelligent detection of sweetpotato diseases.

Imaging and observation of microcirculation in bowel mucosa using sidestream dark field imaging.

Sidestream dark field (SDF) imaging is a tool for assessing microcirculation, commonlyused for early diagnosis and monitoring of sepsis. In this study, we used SDF imaging to observe andassess the microcirculation of the intestinal mucosa during bowel surgery. We also compared different performance between normal mucosa and diseased mucosa using SDF imaging. SDF imaging was conducted in 13 patients to evaluate microcirculationparameters. All patients were assessed at distances of 0, 1, 2, 3 and 4centimeters (cm) from the edge of the mesentery, respectively. Microcirculatory parameters such as microvascular flow index (MFI), proportion of perfused vessels (PPV), vascular density (VD), total vessel density (TVD), perfused vessel density (PVD) and heterogeneity index (HI) were measured in these patients. Compared to normal intestinal mucosa, the diseased intestinal mucosa exhibited higher values for VD (p=0.044), TVD (p=0.006) and PVD (p=0.007). No significant differences in PPV, MFI and HI were observed between the two groups. The microcirculation parameters (MFI, PPV and PVD) of the intestine at the distal distance of 3cm were significantly lower than those at a distance of 2cm (MFI 1.5 (0.75) vs. 3 (0.5), PPV 57.6 (9.1) vs. 97.1 (8.6)% and PVD 11.395 (3.082)vs. 20.726 (4.115)mm/mm2). In conclusion, SDF imaging is an advanced technique that provide real-time visualization of intestinal mucosal microcirculation. It has the potential to assess the blood perfusion of the intestine during surgery.

Application Research of Infrared Technology in Safety Detection in Ship Field

Abstract. This paper studies infrared technology and its application in the field of ship detection, mainly studying infrared thermal imaging of failure equipment refueling oil spill and ship safety problems because infrared technology is a high precision contact thermal measurement method through infrared camera infrared objects, the infrared spectrum wavelength range of infrared radiation heat, on the receiver into electronic signals, through electronic attenuation and electronic scanning form the corresponding heat map or temperature field image, to intuitively reflect the temperature distribution and heat focus of the object. In addition, the infrared detection device can be reasonably installed in the ship without affecting the work of other equipment, and it has been found that infrared technology has significantly improved the solution to the safety problems of ships.

Microlicia ferricola (Lavoisiereae), a new species of Melastomataceae from the Iron Quadrangle, Minas Gerais, Brazil

Here, we describe Microlicia ferricola, a newly discovered species exclusive to the Iron Quadrangle, Minas Gerais state. Additionally, we provide an illustration plate, field images, occurrence map, and comparisons with its morphological relatives such as M. longicalycina, M. woodgyeriana, and M. trichocalycina. This new species is characterized by its green-reddish to reddish branches, leaves, hypanthia, and sepals covered with glandular trichomes, lanceolate to elliptic-lanceolate leaves, setose at the apices, campanulate-oblong hypanthia, long-triangular sepals, dimorphic and bicolorous androecium with tetrasporangiate anthers. Microlicia ferricola is found in the Serra do Rola-Moça State Park and is considered a Least Concern (LC) taxon since it is within a conservation unit, ensuring its preservation.

Accuracy of the Image Interpretation Capability of ChatGPT-4 Vision in Analysis of Hess Screen and Visual Field Abnormalities.

OpenAI, the owner of ChatGPT, publicly released the GPT-4 Vision in September 2023. This multimedia chatbot has the capability to receive and analyze various images presented to it by the user. We assessed the accuracy of its interpretation of 2 of the images commonly used in neuro-ophthalmology, namely Hess screen and automated visual field images. We separately uploaded typical images of 5 abnormal Hess screen charts related to third, fourth, and sixth cranial nerve palsy, Brown syndrome, and inferior orbital wall fracture with entrapment of the inferior rectus muscle. Likewise, 5 classic images of automated visual field grayscale maps related to lesions of the optic nerve, the chiasma, the optic tract, the optic radiations, and the occipital lobe were presented. The chatbot was instructed to select the best option among the 5 choices presented in each question. The GPT-4 Vision was able to select the right choice in 2/5 questions on Hess screens and 3/5 of the visual field questions. Despite selection of the correct option, qualitative evaluation of GPT-4 responses revealed flawed analysis of certain aspects of some image findings, such as the side of involvement or the misinterpretation of the physiologic blind spot as a central scotoma. The performance of GPT-4 Vision in the interpretation of abnormalities of Hess screen and visual field involvement was highly variable, even with simple typical cases of classic disorders. As the chatbot's image recognition is currently evolving, its capacity to accurately interpret ophthalmologic images is still limited at this time.