Automated Analysis Method Research Articles

A study on the automation of borehole image auto-recognition based on instance segmentation

To mitigate the time consumption and inefficiency of manual borehole image data recognition, this paper introduces a instance segmetation-based algorithm model called borehole image auto-recognition (BIAR). The algorithm initially employs the instance segmentation module of the you only look once version 8(YOLOv8)deep learning model to automatically detect and classify structures like dike filling and fractures in borehole wall images. Polynomial fitting formulas are then applied to the segmented mask coordinates to generate structural curves of the detected features. The dip and dip direction of these structures are calculated from the extrema of the fitted curves. Finally, the algorithm outputs the processed borehole images. A comparison between automatic and manual recognition using a dataset of actual borehole images demonstrates the algorithm's ability to quickly and accurately identify fractures and discontinuities, even in the presence of complex borehole wall information. The model's accuracy and robustness are also validated. Validation indicates that the BIAR algorithm can identify and map structural surface features in borehole images at a depth of 100 meters within 30 seconds, achieving over 90% accuracy in identifying structural surfaces.This algorithm provides a highly efficient and accurate method for automatic borehole image analysis in engineering applications.

Performance Restoration of Chemically Recycled Carbon Fibres Through Surface Modification with Sizing.

The recycling of Carbon Fibre-Reinforced Polymers (CFRPs) is becoming increasingly crucial due to the growing demand for sustainability in high-performance industries such as automotive and aerospace. This study investigates the impact of two chemical recycling techniques, chemically assisted solvolysis and plasma-enhanced solvolysis, on the morphology and properties of carbon fibres (CFs) recovered from end-of-life automotive parts. In addition, the effects of fibre sizing are explored to enhance the performance of the recycled carbon fibres (rCFs). The surface morphology of the fibres was characterised using Scanning Electron Microscopy (SEM), and their structural integrity was assessed through Thermogravimetric Analysis (TGA) and Raman spectroscopy. An automatic analysis method based on optical microscopy images was also developed to quantify filament loss during the recycling process. Mechanical testing of single fibres and yarns showed that although rCFs from both recycling methods exhibited a ~20% reduction in tensile strength compared to reference fibres, the application of sizing significantly mitigated these effects (~10% reduction). X-ray Photoelectron Spectroscopy (XPS) further confirmed the introduction of functional oxygen-containing groups on the fibre surface, which improved fibre-matrix adhesion. Overall, the results demonstrate that plasma-enhanced solvolysis was more effective at fully decomposing the resin, while the subsequent application of sizing enhanced the mechanical performance of rCFs, restoring their properties closer to those of virgin fibres.

Automatic deep learning method for analysis and prediction of neonatal hyperbilirubinemia in magnetic resonance imaging.

This study conducted a comparative analysis among newborns with varying levels of hyperbilirubinemia, explored the relationships between magnetic resonance imaging (MRI) image features and serum bilirubin levels in hyperbilirubinemia, and proposed an automatic classification system based on deep learning (DL) for prediction of neonatal hyperbilirubinemia (NHB). This retrospective study enrolled 606 consecutive neonates who had their serum bilirubin detected at the Xi'an Fourth Hospital, including 273 cases of patients and 333 cases of normal controls. After data preprocessing, MRI images were fed into the Inception-v3 network, graph convolutional network (GCN), and 3-dimensional (3D) patch-based GCN that introduced the graph attention mechanism (our GCN) for NHB analysis and classification, respectively. Multi-threshold grouping was conducted based on various serum bilirubin levels. Performance evaluation involved the area under the curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). As the bilirubin levels gradually increased, the overall performance metrics of DL system for detecting the T1-weighted imaging signal in the pallidum region showed a significant upward trend. Our GCN for the prediction and classification of MRI image features of NHB achieved satisfactory results. When the bilirubin value exceeded 400 µmol/L, it achieved an AUC of 0.86 and ACC of 0.81, which is significantly higher than other advanced models (ACC: 72-78.3%) with the same proposed input form. The DL system has the potential to automatically analyze and predict NHB on MRI.

Building Footprint Extraction from Fixed-Wing UAV Imagery using Mask R-CNN and Object-based Image Analysis Methods (Case Study: Banturejo Village, Malang Regency)

Abstract As a developing area in Malang Regency, Banturejo Village has many potencies since its location near the tourism area of Selorejo Dam. To maximally the harness of potencies while maintaining efficient land use in Banturejo village, mapping the built area in large scale should be carried out. The photogrammetric techniques using fixed-wing UAV could be a good alternative for large-scale mapping in this village area because of its capability to quickly acquire high resolution image with highly customizable mission specifications. But the problem arises in interpreting these imagery into meaningful cartographic representation which often requires cautious manual digitization in much slower rate that its acquisition. In this research the automatic image analysis method for building footprint extraction using Mask R-CNN algorithm and Object-Based Image Analysis was performed. The fixed wing UAV imagery was captured in 2023 and the structure from motion algorithm was employed for photogrammetric processing which produced 10-cm resolution orthophoto. Manually digitized building polygons from the same imagery serve as the gold standard for accuracy analysis, and small proportion of the data was used as training samples for the algorithm. The results shows that 1447 buildings with total area of 180,595 m2 was generated with Mask R-CNN algorithm, while OBIA-Mask R-CNN produced 572 buildings and total area of 201,932 m2. The confusion matrices reveal precision value of 77.94%, recall 51.54%, F1 Score 62.02% by Mask R-CNN method, and precision value of 35.95%, recall 9.21%, F1 Score 14.66% by OBIA-Mask RCNN method. Mask R-CNN method generated slightly lower accuracy of total building area, but in terms of precision the OBIA-Mask RCNN method produces lower number of building polygons.

Antibody selection and automated quantification of TRPV1 immunofluorescence on human skin

Assessing localization of the transient receptor potential vanilloid-1 (TRPV1) in skin nerve fibers is crucial for understanding its role in peripheral neuropathy and pain. However, information on the specificity and sensitivity of TRPV1 antibodies used for immunofluorescence (IF) on human skin is currently lacking. To find a reliable TRPV1 antibody and IF protocol, we explored antibody candidates from different manufacturers, used rat DRG sections and human skin samples for screening and human TRPV1-expressing HEK293 cells for further validation. Final specificity assessment was done on human skin samples. Additionally, we developed two automated image analysis methods: a Python-based deep-learning approach and a Fiji-based machine-learning approach. These methods involve training a model or classifier for nerve fibers based on pre-annotations and utilize a nerve fiber mask to filter and count TRPV1 immunoreactive puncta and TRPV1 fluorescence intensity on nerve fibers. Both automated analysis methods effectively distinguished TRPV1 signals on nerve fibers from those in keratinocytes, demonstrating high reliability as evidenced by excellent intraclass correlation coefficient (ICC) values exceeding 0.75. This method holds the potential to uncover alterations in TRPV1 associated with neuropathic pain conditions, using a minimally invasive approach.

Improving CNN Fish Detection and Classification with Tracking

The regular and consistent monitoring of marine ecosystems and fish communities is becoming more and more crucial due to increasing human pressures. To this end, underwater camera technology has become a major tool to collect an important amount of marine data. As the size of the data collected outgrew the ability to process it, new means of automatic processing have been explored. Convolutional neural networks (CNNs) have been the most popular method for automatic underwater video analysis for the last few years. However, such algorithms are rather image-based and do not exploit the potential of video data. In this paper, we propose a method of coupling video tracking and CNN image analysis to perform a robust and accurate fish classification on deep sea videos and improve automatic classification accuracy. Our method fused CNNs and tracking methods, allowing us to detect 12% more individuals compared to CNN alone.

Gambaran Jumlah Leukosit pada Penderita Demam Berdarah Dengue di Klinik DR.Trisna Garut

Dengue hemorrhagic fever (DHF) is a public health problem in Indonesia whose number of cases continues to increase and its spread is increasingly widespread.Tests used to diagnose dengue fever include a white blood cell count test.White blood cells are cells that play a role in the body's defense system. The reason is, white blood cells help fight infections in the body, such as viruses, bacteria, parasites and fungi. In patients infected with dengue fever, the white blood cell count is usually normal but then decreases (leukopenia) and neutropenia becomes dominant. Thisresearch was conducted from March to April 2023.The aim of this study was to measure the number of white blood cells in dengue fever patients.This type of research is descriptive. The research was carried out using an automatic analysis method (blood analysis) in the laboratory ofKlinik Dr.Trisna Garut, using samples from 30 dengue fever patients who were tested. In this study, the number of white blood cells in dengue fever patients at Klinik Dr.Trisna Garut was 14 (46.7%).Wich is Normal And the white blood cell count results of dengue fever patients at Klinik Dr.Tisna Garut General Hospital decreased by 16 (54.3%).

Simulating images of radio galaxies with diffusion models

Context. With increasing amounts of data produced by astronomical surveys, automated analysis methods have become crucial. Synthetic data are required for developing and testing such methods. Current classical approaches to simulations often suffer from insufficient detail or inaccurate representation of source type occurrences. Deep generative modeling has emerged as a novel way of synthesizing realistic image data to overcome those deficiencies. Aims. We implemented a deep generative model trained on observations to generate realistic radio galaxy images with full control over the flux and source morphology. Methods. We used a diffusion model, trained with continuous time steps to reduce sampling time without quality impairments. The two models were trained on two different datasets, respectively. One set was a selection of images obtained from the second data release of the LOFAR Two-Metre Sky Survey (LoTSS). The model was conditioned on peak flux values to preserve signal intensity information after re-scaling image pixel values. The other, smaller set was obtained from the Very Large Array (VLA) survey of Faint Images of the Radio Sky at Twenty-Centimeters (FIRST). In that set, every image was provided with a morphological class label the corresponding model was conditioned on. Conditioned sampling is realized with classifier-free diffusion guidance. We evaluated the quality of generated images by comparing the distributions of different quantities over the real and generated data, including results from the standard source-finding algorithms. The class conditioning was evaluated by training a classifier and comparing its performance on both real and generated data. Results. We have been able to generate realistic images of high quality using 25 sampling steps, which is unprecedented in the field of radio astronomy. The generated images are visually indistinguishable from the training data and the distributions of different image metrics were successfully replicated. The classifier is shown to perform equally well for real and generated images, indicating strong sampling control over morphological source properties.

The effect of complex extra-furnace treatment of metal melts on the formation of non-metallic inclusions in large-sized ingots

Abstract The article presents the results of studying non-metallic inclusions (NI) after extra-furnace treatment of the 13Cr9Mo2Co1NiVNbNB steel melt to produce large-sized ingots. The objects of the study were laboratory samples (the first batch), and samples obtained after melting in industrial furnace, followed by modification and double vacuuming (the second batch). Previously, using the Fact Sage software, modeling of the NI formation processes was carried out. The data were obtained on the possible NI composition and amount in this steel. The composition, the contamination index, parameters of NI in samples have been studied. It was established that in the samples of the first batch, the main part of the NI consisted of chromium- and manganese-containing oxides, which occupy more than 94% of the total area of the NI. In the samples of the second batch, the main part of the NI was represented by aluminum containing systems. It was shown that the average size of NI in the samples after extra-furnace treatment differs slightly (5%–12%), however, the contamination index in industrial samples after vacuum treatment and especially after modification and repeated vacuum treatment was reduced by more than 10 times. The results obtained allow to recommend vacuum treatment and complex modification with calcium and boron as measures to prevent the formation of NI and to improve metallurgical quality when smelting large ingots using complex alloy steels.

LEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable Stars

With the increasing amount of astronomical observation data, it is an inevitable trend to use artificial intelligence methods for automatic analysis and identification of light curves for full samples. However, data sets covering all known classes of variable stars that meet all research needs are not yet available. There is still a lack of standard training data sets specifically designed for any type of light-curve classification, but existing light-curve training sets or data sets cannot be directly merged into a large collection. Based on the open data sets of the All-Sky Automated Survey for SuperNovae, Gaia, and Zwicky Transient Facility, we construct a compatible light-curve data set named LEAVES for automated recognition of variable stars, which can be used for training and testing new classification algorithms. The data set contains a total of 977,953 variable and 134,592 nonvariable light curves, in which the supported variables are divided into six superclasses and nine subclasses. We validate the compatibility of the data set through experiments and employ it to train a hierarchical random forest classifier, which achieves a weighted average F1-score of 0.95 for seven-class classification and 0.93 for 10-class classification. Experimental results prove that the classifier is more compatible than the classifier established based on a single band and a single survey, and has wider applicability while ensuring classification accuracy, which means it can be directly applied to different data types with only a relatively small loss in performance compared to a dedicated model.

Multimodal Data-Driven Segmentation of Bone Metastasis Lesions in SPECT Bone Scans Using Deep Learning.

Patients with malignant tumors often develop bone metastases. SPECT bone scintigraphy is an effective tool for surveying bone metastases due to its high sensitivity, low-cost equipment, and radiopharmaceutical. However, the low spatial resolution of SPECT scans significantly hinders manual analysis by nuclear medicine physicians. Deep learning, a promising technique for automated image analysis, can extract hierarchal patterns from images without human intervention. To enhance the performance of deep learning-based segmentation models, we integrate textual data from diagnostic reports with SPECT bone scans, aiming to develop an automated analysis method that outperforms purely unimodal data-driven segmentation models. We propose a dual-path segmentation framework to extract features from bone scan images and diagnostic reports separately. In the first path, an encoder-decoder network is employed to learn hierarchical representations of features from SPECT bone scan images. In the second path, the Chinese version of the MacBERT model is utilized to develop a text encoder for extracting features from diagnostic reports. The extracted textual features are then fused with image features during the decoding stage in the first path, enhancing the overall segmentation performance. Experimental evaluation conducted on real-world clinical data demonstrated the superior performance of the proposed segmentation model. Our model achieved a 0.0209 increase in the DSC (Dice Similarity Coefficient) score compared to the well-known U-Net model. The proposed multimodal data-driven method effectively identifies and isolates metastasis lesions in SPECT bone scans, outperforming existing classical deep learning models. This study demonstrates the value of incorporating textual data in the deep learning-based segmentation of lowresolution SPECT bone scans.

Single Vesicle Surface Protein Profiling and Machine Learning-Based Dual Image Analysis for Breast Cancer Detection.

Single-vesicle molecular profiling of cancer-associated extracellular vesicles (EVs) is increasingly being recognized as a powerful tool for cancer detection and monitoring. Mask and target dual imaging is a facile method to quantify the fraction of the molecularly targeted population of EVs in biofluids at the single-vesicle level. However, accurate and efficient dual imaging vesicle analysis has been challenging due to the interference of false signals on the mask images and the need to analyze a large number of images in clinical samples. In this work, we report a fully automatic dual imaging analysis method based on machine learning and use it with dual imaging single-vesicle technology (DISVT) to detect breast cancer at different stages. The convolutional neural network Resnet34 was used along with transfer learning to produce a suitable machine learning model that could accurately identify areas of interest in experimental data. A combination of experimental and synthetic data were used to train the model. Using DISVT and our machine learning-assisted image analysis platform, we determined the fractions of EpCAM-positive EVs and CD24-positive EVs over captured plasma EVs with CD81 marker in the blood plasma of pilot HER2-positive breast cancer patients and compared to those from healthy donors. The amount of both EpCAM-positive and CD24-positive EVs was found negligible for both healthy donors and Stage I patients. The amount of EpCAM-positive EVs (also CD81-positive) increased from 18% to 29% as the cancer progressed from Stage II to III. No significant increase was found with further progression to Stage IV. A similar trend was found for the CD24-positive EVs. Statistical analysis showed that both EpCAM and CD24 markers can detect HER2-positive breast cancer at Stages II, III, or IV. They can also differentiate individual cancer stages except those between Stage III and Stage IV. Due to the simplicity, high sensitivity, and high efficiency, the DISVT with the AI-assisted dual imaging analysis can be widely used for both basic research and clinical applications to quantitatively characterize molecularly targeted EV subtypes in biofluids.

Automatic stress analysis method for semiconductor materials based on dual-wavelength infrared photoelasticity

In this paper, an automatic method of stress analysis was proposed, based on dual-wavelength infrared photoelasticity, which was prospectively applicable to quality inspection in semiconductor manufacturing engineering. This method employed a strategy and corresponding algorithm to locate reference points by analyzing the rate of intensity change between two photoelastic images captured at dual wavelengths, relative to the material fringe constant, guiding the unwrapping process of the isoclinic value and isochromatic value maps. It enabled the full-field stress analysis without any manual intervention or any priori knowledge or information about the stress to analyze. An optic instrument was developed to realize the measurement of dual-wavelength infrared photoelasticity. Using this device, the feasibility of the proposed method was verified by quantifying the internal stress fields of different samples made in monocrystalline silicon wafers. The experimental results illustrated the full automation of the proposed method, and its high accuracy as well. Additionally, an optimized scanning scheme was further discussed to balance the efficiency and the accuracy based on the above method and device of dual-wavelength infrared photoelasticity.

Automatic Image Analysis Method as a Tool to Evaluate the Anisotropy of Autoclaved Aerated Concrete for Moisture and Heat Transport

In this article, the results of studies testing the anisotropy of autoclaved aerated concrete in terms of water and heat transport are presented. Using image analysis techniques, a study was conducted on four different samples of concrete produced in the same process. To ensure the comparability of results, the pictures were taken from a fixed distance with the same lens settings trimmed to a set size. Cross-sectional profiles of the material were examined and were arranged in two directions: perpendicular and parallel to the growth direction occurring in the autoclave. For each block, approximately 4750 objects were obtained, with an average of 2700 objects along the wall and 2050 across it. As a result of the comparative analysis, metrics concerning pores, significantly distinguishing the profile direction, were identified. These included the pore area (area), the maximum and minimum distance between points on the perimeter (Feret, MinFeret), lengths of the major and minor axes of the fitted ellipse (major, minor), and the ratio of the area of selection to its convex hull (solidity). As a reference, standard investigations were conducted for moisture transport using the time domain reflectometry setup and for thermal conductivity values using the steady-state heat flow plate apparatus.

Research on lunar regolith of the Chang'E-4 landing site: An automated analysis method based on deep learning framework

On January 3, 2019, the Chang'E-4 lander successfully landed within the Von Kármán crater, located in the South P ole-Aitken Basin (SPA) on the farside of the Moon (45.5°S, 177.6°E), marking the first soft landing on the lunar farside. The lander, equipped with the Lunar Penetrating Radar (LPR) system, aimed to provide insights into the structure and evolution of the Moon. Previous research often relied on manually identifying hyperbolic features to analyze the lunar shallow subsurface properties. This inefficient approach may lead to subjective biases, resulting in unstable outcomes. This research constructed an automatic analysis framework by integrating the Swin Transformer with a 3D velocity spectrum, which is then applied to analyze the properties of the Chang'E-4 LPR data. The experimental results indicate that the framework achieved a precision of 98.9 % and a recall of 96.7 % in hyperbolic feature identification, with an F1 of 0.9782 and AP of 94.8 %. Additionally, it has been experimentally validated that the framework can accurately invert hyperbolic features' two-way travel time and velocity. Finally, the framework is applied to analyze the lunar shallow subsurface structure and properties within the landing area of the Chang'E-4 mission.

Electroencephalography can Ubiquitously Delineate the Brain Dysfunction of Neurodegenerative Dementia by Both Visual and Automatic Analysis Methods: A Preliminary Study.

Introduction: The aim was to examine the differences in electroencephalography (EEG) findings by visual and automated quantitative analyses between Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) and Parkinson's disease with dementia (PDD). Methods: EEG data of 20 patients with AD and 24 with DLB/PDD (12 DLB and 12 PDD) were retrospectively analyzed. Based on the awake EEG, the posterior dominant rhythm frequency and proportion of patients who showed intermittent focal and diffuse slow waves (IDS) were visually and automatically compared between the AD and DLB/PDD groups. Results: On visual analysis, patients with DLB/PDD showed a lower PDR frequency than patients with AD. In patients with PDR <8 Hz and occipital slow waves or patients with PDR <8 Hz and IDS, DLB/PDD was highly suspected (PPV 100%) and AD was unlikely (PPV 0%). On automatic analysis, the findings of the PDR were similar to those on visual analysis. Comparisons between visual and automatic analysis showed an overlap in the focal slow wave commonly detected by both methods in 10 of 44 patients, and concordant presence or absence of IDS in 29 of 43 patients. With respect to PDR <8 Hz and the combination of PDR <8 Hz and IDS, PPV and NPV in DLB/PDD and AD were not different between visual and automatic analysis. Conclusions: As the noninvasive, widely available clinical tool of low expense, visual analysis of EEG findings provided highly sufficient information to delineate different brain dysfunction in AD and DLB/PDD, and automatic EEG analysis could support visual analysis especially about PD.

Exploitation of long-range acoustic dependence for early change detection in dynamic machine running status

This paper suggests that the long-range dependence (LRD) in acoustic signal is an important indicator of dynamic behavior in data. Considering this information, a novel framework based on LRD in acoustic is proposed for detecting change-points in machine running status. Initially, the LRD phenomenon in acoustic is demonstrated. Subsequently, a change-point detection framework is developed, leveraging LRD to monitor machine running states. The framework includes two phases: (1) characteristic data prediction: the LRD value is predicted by the fractional autoregressive integrated moving average (FARIMA) model, which is applied to reflect changes in machine operation states. (2) date change detection: based on residual analysis, a null hypothesis testing based automatic analysis method for acoustic signals during machine successive operations is used to make decision. Experiments in real-world applications demonstrated the good potential of the proposed framework in practical engineering scenarios.

Automated analysis of a novel object recognition test in mice using image processing and machine learning

The novel object recognition test (NORT) is one of the most commonly employed behavioral tests in experimental animals designed to evaluate an animal's interest in and recognition of novelty. However, manual procedures, which rely on researchers’ observations, prevent high throughput analysis. In this study, we developed an automated analysis method for NORT utilizing machine learning-assisted exploratory behavior detection. We recorded the exploratory behavior of the mice using a video camera. The coordinates of the mouse nose and tail base in recorded video files were detected using a pre-trained machine learning model, DeepLabCut. Each video was then segmented into frame images, which were categorized into "exploratory,” or "non-exploratory" frames based on manual observation. Mouse feature vectors were calculated as vectors from the nose to the vertices of the object and were utilized for SVM training. The trained SVM effectively detected exploratory behaviors, showing a strong correlation with human observer assessments. Upon application to NORT, the duration of mouse exploratory behavior towards objects predicted by the SVM exhibited a significant correlation with the assessments made by human observers. The novelty discrimination index derived from the SVM predictions also aligned well with that from human observations.

Comparison of semi and fully automated artificial intelligence driven softwares and manual system for cephalometric analysis

BackgroundCephalometric analysis has been used as one of the main tools for orthodontic diagnosis and treatment planning. The analysis can be performed manually on acetate tracing sheets, digitally by manual selection of landmarks or by recently introduced Artificial Intelligence (AI)-driven tools or softwares that automatically detect landmarks and analyze them. The use of AI-driven tools is expected to avoid errors and make it less time consuming with effective evaluation and high reproducibility.ObjectiveTo conduct intra- and inter-group comparisons of the accuracy and reliability of cephalometric tracing and evaluation done manually and with AI-driven tools that is WebCeph and CephX softwares.MethodsDigital and manual tracing of lateral cephalometric radiographs of 54 patients was done. 18 cephalometric parameters were assessed on each radiograph by 3 methods, manual method and by using semi (WebCeph) and fully automatic softwares (Ceph X). Each parameter was assessed by two investigators using these three methods. SPSS was then used to assess the differences in values of cephalometric variables between investigators, between softwares, between human investigator means and software means. ICC and paired T test were used for intra-group comparisons while ANOVA and post-hoc were used for inter-group comparisons.ResultsTwelve out of eighteen variables had high intra-group correlation and significant ICC p-values, 5 variables had relatively lower values and only one variable (SNO) had significantly low ICC value. Fifteen out of eighteen variables had minimal detection error using fully-automatic method of cephalometric analysis. Only three variables had lowest detection error using semi-automatic method of cephalometric analysis. Inter-group comparison revealed significant difference between three methods for eight variables; Witts, NLA, SNGoGn, Y-Axis, Jaraback, SNO, MMA and McNamara to Point A.ConclusionThere is a lack of significant difference among manual, semiautomatic and fully automatic methods of cephalometric tracing and analysis in terms of the variables measured by these methods. The mean detection errors were the highest for manual analysis and lowest for fully automatic method. Hence the fully automatic AI software has the most reproducible and accurate results.

Automatic retinal image analysis methods using colour fundus images for screening glaucomatous optic neuropathy

ObjectivesTrain an automatic retinal image analysis (ARIA) method to screen glaucomatous optic neuropathy (GON) on non-mydriatic retinal images labelled with the additional results of optical coherence tomography (OCT) and assess...