-
Journal of Healthcare Engineering 2022A difficult challenge in the realm of biomedical engineering is the detection of physiological changes occurring inside the human body, which is a difficult undertaking....
A difficult challenge in the realm of biomedical engineering is the detection of physiological changes occurring inside the human body, which is a difficult undertaking. At the moment, these irregularities are graded manually, which is very difficult, time-consuming, and tiresome due to the many complexities associated with the methods involved in their identification. In order to identify illnesses at an early stage, the use of computer-assisted diagnostics has acquired increased attention as a result of the requirement of a disease detection system. The major goal of this proposed work is to build a computer-aided design (CAD) system to help in the early identification of glaucoma as well as the screening and treatment of the disease. The fundus camera is the most affordable image analysis modality available, and it meets the financial needs of the general public. The extraction of structural characteristics from the segmented optic disc and the segmented optic cup may be used to characterize glaucoma and determine its severity. For this study, the primary goal is to estimate the potential of the image analysis model for the early identification and diagnosis of glaucoma, as well as for the evaluation of ocular disorders. The suggested CAD system would aid the ophthalmologist in the diagnosis of ocular illnesses by providing a second opinion as a judgment made by human specialists in a controlled environment. An ensemble-based deep learning model for the identification and diagnosis of glaucoma is in its early stages now. This method's initial module is an ensemble-based deep learning model for glaucoma diagnosis, which is the first of its kind ever developed. It was decided to use three pretrained convolutional neural networks for the categorization of glaucoma. These networks included the residual network (ResNet), the visual geometry group network (VGGNet), and the GoogLeNet. It was necessary to use five different data sets in order to determine how well the proposed algorithm performed. These data sets included the DRISHTI-GS, the Optic Nerve Segmentation Database (DRIONS-DB), and the High-Resolution Fundus (HRF). Accuracy of 91.11% for the PSGIMSR data set and the sensitivity of 85.55% and specificity of 95.20% for the suggested ensemble architecture on the PSGIMSR data set were achieved. Similarly, accuracy rates of 95.63%, 98.67%, 95.64%, and 88.96% were achieved using the DRIONS-DB, HRF, DRISHTI-GS, and combined data sets, respectively.
Topics: Diagnosis, Computer-Assisted; Fundus Oculi; Glaucoma; Humans; Optic Disk; Supervised Machine Learning
PubMed: 35273784
DOI: 10.1155/2022/2988262 -
Indian Journal of Ophthalmology Jun 2022Traditional methods for neuroretinal rim width measurement in spectral domain optical coherence tomography (SD-OCT) employs the Bruch's membrane opening (BMO) as the...
BACKGROUND
Traditional methods for neuroretinal rim width measurement in spectral domain optical coherence tomography (SD-OCT) employs the Bruch's membrane opening (BMO) as the anatomical border of the rim, referenced to a BMO horizontal reference plane, termed as "Bruch's Membrane Opening-Horizontal Rim Width" (BMO-HRW). BMO-HRW is defined as the distance between BMO and internal limiting membrane (ILM) on the horizontal plane. In contrast, the Spectralis OCT (Heidelberg Engineering, Germany) employs a new parameter called "Bruch's Membrane Opening-Minimum Rim Width" (BMO-MRW) with Glaucoma Module Premium Edition (GMPE). GMPE provides a novel objective method of optic nerve head (ONH) analysis using BMO, but the neuroretinal rim assessment is performed from the BMO to the nearest point on the ILM, rather than on the horizontal reference plane. It is the BMO-MRW and is defined as the minimum distance between the BMO and ILM in the ONH.
PURPOSE
In this video, anatomy of the ONH and GMPE is decoded from a neophyte user's point of view, as to why BMO-MRW is more important than the traditional BMO-HRW for glaucoma evaluation.
SYNOPSIS
The GMPE concepts are depicted in a novel dynamic (Clinical vs OCT Vs Histology) screenplay, detailing the below focal points with 2D & 3D animations: True Margin of ONH, Bruch's Membrane (BM), Histology Vs OCT, BMO, Bruch's Membrane Opening-Minimum Rim Width, Bruch's Membrane Opening-Minimum Rim Width Versus Bruch's Membrane Opening-Horizontal Rim Width, Alpha, Beta, Gamma Zone of ONH in OCT, Anatomic Positioning System, Impact of Fovea Bruch's Membrane Opening Centre Axis.
HIGHLIGHTS
This video also highlights, how with the advent of Anatomic Positioning System, scans were able to align relative to the individual's Fovea-to-BMO-center (FoBMOC) axis at every follow-up, for accurately detecting changes, as small as 1 micron in BMO-MRW, thus creating a new world in diagnosing glaucoma and detecting glaucomatous progression with precision.
VIDEO LINK
https://youtu.be/6RqF5guAziw.
Topics: Bruch Membrane; Glaucoma; Humans; Intraocular Pressure; Optic Disk; Retinal Ganglion Cells
PubMed: 35648032
DOI: 10.4103/ijo.IJO_1261_21 -
Journal of Neuro-ophthalmology : the... Sep 2021To date, deep learning-based detection of optic disc abnormalities in color fundus photographs has mostly been limited to the field of glaucoma. However, many...
BACKGROUND
To date, deep learning-based detection of optic disc abnormalities in color fundus photographs has mostly been limited to the field of glaucoma. However, many life-threatening systemic and neurological conditions can manifest as optic disc abnormalities. In this study, we aimed to extend the application of deep learning (DL) in optic disc analyses to detect a spectrum of nonglaucomatous optic neuropathies.
METHODS
Using transfer learning, we trained a ResNet-152 deep convolutional neural network (DCNN) to distinguish between normal and abnormal optic discs in color fundus photographs (CFPs). Our training data set included 944 deidentified CFPs (abnormal 364; normal 580). Our testing data set included 151 deidentified CFPs (abnormal 71; normal 80). Both the training and testing data sets contained a wide range of optic disc abnormalities, including but not limited to ischemic optic neuropathy, atrophy, compressive optic neuropathy, hereditary optic neuropathy, hypoplasia, papilledema, and toxic optic neuropathy. The standard measures of performance (sensitivity, specificity, and area under the curve of the receiver operating characteristic curve (AUC-ROC)) were used for evaluation.
RESULTS
During the 10-fold cross-validation test, our DCNN for distinguishing between normal and abnormal optic discs achieved the following mean performance: AUC-ROC 0.99 (95 CI: 0.98-0.99), sensitivity 94% (95 CI: 91%-97%), and specificity 96% (95 CI: 93%-99%). When evaluated against the external testing data set, our model achieved the following mean performance: AUC-ROC 0.87, sensitivity 90%, and specificity 69%.
CONCLUSION
In summary, we have developed a deep learning algorithm that is capable of detecting a spectrum of optic disc abnormalities in color fundus photographs, with a focus on neuro-ophthalmological etiologies. As the next step, we plan to validate our algorithm prospectively as a focused screening tool in the emergency department, which if successful could be beneficial because current practice pattern and training predict a shortage of neuro-ophthalmologists and ophthalmologists in general in the near future.
Topics: Algorithms; Deep Learning; Diagnostic Techniques, Ophthalmological; Humans; Optic Disk; Optic Nerve Diseases; ROC Curve
PubMed: 34415271
DOI: 10.1097/WNO.0000000000001358 -
BMC Ophthalmology Sep 2022Glaucoma is multifactorial, but the interrelationship between risk factors and structural changes remains unclear. Here, we adjusted for confounding factors in glaucoma...
BACKGROUND
Glaucoma is multifactorial, but the interrelationship between risk factors and structural changes remains unclear. Here, we adjusted for confounding factors in glaucoma patients with differing risk factors, and compared differences in structure and susceptible areas in the optic disc and macula.
METHODS
In 458 eyes with glaucoma, we determined confounding factors for intraocular pressure (IOP), central corneal thickness (CCT), axial length (AL), LSFG-measured ocular blood flow (OBF), which was assessed with laser speckle flowgraphy-measured mean blur rate in the tissue area (MT) of the optic nerve head, biological antioxidant potential (BAP), and systemic abnormalities in diastolic blood pressure (dBP). To compensate for measurement bias, we also analyzed corrected IOP (cIOP; corrected for CCT) and corrected MT (cMT; corrected for age, weighted retinal ganglion cell count, and AL). Then, we determined the distribution of these parameters in low-, middle-, and high-value subgroups and compared them with the Kruskal-Wallis test. Pairwise comparisons used the Steel-Dwass test.
RESULTS
The high-cIOP subgroup had significantly worse mean deviation (MD), temporal, superior, and inferior loss of circumpapillary retinal nerve fiber layer thickness (cpRNFLT), and large cupping. The low-CCT subgroup had temporal cpRNFLT loss; the high-CCT subgroup had low cup volume. The high-AL subgroup had macular ganglion cell complex thickness (GCCT) loss; the low-AL subgroup had temporal cpRNFLT loss. The high-systemic-dBP subgroup had worse MD, total, superior, and inferior cpRNFLT loss and macular GCCT loss. The low-BAP subgroup had more male patients, higher dBP, and cpRNFLT loss in the 10 o'clock area. The high-OBF subgroup had higher total, superior and temporal cpRNFLT and macular GCCT.
CONCLUSIONS
Structural changes and local susceptibility to glaucomatous damage show unique variations in patients with different risk factors, which might suggest that specific risk factors induce specific types of pathogenesis and corresponding glaucoma phenotypes. Our study may open new avenues for the development of precision medicine for glaucoma.
Topics: Antioxidants; Glaucoma; Humans; Male; Optic Disk; Risk Factors; Tomography, Optical Coherence
PubMed: 36123604
DOI: 10.1186/s12886-022-02587-5 -
The British Journal of Ophthalmology Jun 2020To assess changes in the position of lamina cribrosa pores (LCPs) induced by acute intraocular pressure (IOP) elevation. (Observational Study)
Observational Study
PURPOSE
To assess changes in the position of lamina cribrosa pores (LCPs) induced by acute intraocular pressure (IOP) elevation.
METHODS
A prospective observational study. Acute angle-closure suspects who underwent the 2-hour dark room prone provocative test (DRPPT) were included. At baseline and within 5 min after the DRPPT end, tonometry, fundus photography and optical coherence tomography were performed. Optic disc photos taken before and after the DRPPT were aligned and moving distance of each visible LCP was measured (LCPMD).
RESULTS
38 eyes from 27 participants (age: 52.5±10.8 years) were included. The IOP rose from 16.7±3.2 mm Hg at baseline to 23.9±4.3 mm Hg at the DRPPT end. The mean lateral LCPMD was 28.1±14.6 µm (range: 5.0-77.2 µm), which increased with higher IOP rise (p=0.01) and deeper optic cup (p=0.02) in multivariate analysis. The intralamina range and SD of the LCPMD increased with younger age (p=0.01 and p=0.02, respectively) and with wider optic cup (p=0.01 and p=0.02, respectively). The LCP movements were headed to the superior direction in 12 (33%) eyes, inferior direction in 10 (28%) eyes, temporal direction in 9 (25%) eyes, and nasal direction in 5 (14%) eyes.
CONCLUSIONS
IOP rise is associated with LCP movements in the frontal plane, which are more pronounced with higher IOP rise and deeper optic cup. The intralamina variability in the IOP rise-associated LCPMD increased with younger age and wider optic cup. IOP variation-associated lateral LCP movements may be of interest to elucidate glaucomatous optic nerve damage.
Topics: Dark Adaptation; Female; Follow-Up Studies; Glaucoma, Angle-Closure; Humans; Intraocular Pressure; Male; Middle Aged; Optic Disk; Prospective Studies; Tomography, Optical Coherence
PubMed: 31488430
DOI: 10.1136/bjophthalmol-2019-314016 -
BMC Bioinformatics Dec 2022Glaucoma can cause irreversible blindness to people's eyesight. Since there are no symptoms in its early stage, it is particularly important to accurately segment the...
BACKGROUND
Glaucoma can cause irreversible blindness to people's eyesight. Since there are no symptoms in its early stage, it is particularly important to accurately segment the optic disc (OD) and optic cup (OC) from fundus medical images for the screening and prevention of glaucoma. In recent years, the mainstream method of OD and OC segmentation is convolution neural network (CNN). However, most existing CNN methods segment OD and OC separately and ignore the a priori information that OC is always contained inside the OD region, which makes the segmentation accuracy of most methods not high enough.
METHODS
This paper proposes a new encoder-decoder segmentation structure, called RSAP-Net, for joint segmentation of OD and OC. We first designed an efficient U-shaped segmentation network as the backbone. Considering the spatial overlap relationship between OD and OC, a new Residual spatial attention path is proposed to connect the encoder-decoder to retain more characteristic information. In order to further improve the segmentation performance, a pre-processing method called MSRCR-PT (Multi-Scale Retinex Colour Recovery and Polar Transformation) has been devised. It incorporates a multi-scale Retinex colour recovery algorithm and a polar coordinate transformation, which can help RSAP-Net to produce more refined boundaries of the optic disc and the optic cup.
RESULTS
The experimental results show that our method achieves excellent segmentation performance on the Drishti-GS1 standard dataset. In the OD and OC segmentation effects, the F1 scores are 0.9752 and 0.9012, respectively. The BLE are 6.33 pixels and 11.97 pixels, respectively.
CONCLUSIONS
This paper presents a new framework for the joint segmentation of optic discs and optic cups, called RSAP-Net. The framework mainly consists of a U-shaped segmentation skeleton and a residual space attention path module. The design of a pre-processing method called MSRCR-PT for the OD/OC segmentation task can improve segmentation performance. The method was evaluated on the publicly available Drishti-GS1 standard dataset and proved to be effective.
Topics: Humans; Optic Disk; Glaucoma
PubMed: 36474136
DOI: 10.1186/s12859-022-05058-2 -
BioMed Research International 2022Glaucoma is one of the leading factors of vision loss, where the people tends to lose their vision quickly. The examination of cup-to-disc ratio is considered essential...
Glaucoma is one of the leading factors of vision loss, where the people tends to lose their vision quickly. The examination of cup-to-disc ratio is considered essential in diagnosing glaucoma. It is hence regarded that the segmentation of optic disc and cup is useful in finding the ratio. In this paper, we develop an extraction and segmentation of optic disc and cup from an input eye image using modified recurrent neural networks (mRNN). The mRNN use the combination of recurrent neural network (RNN) with fully convolutional network (FCN) that exploits the intra- and interslice contexts. The FCN extracts the contents from an input image by constructing a feature map for the intra- and interslice contexts. This is carried out to extract the relevant information, where RNN concentrates more on interslice context. The simulation is conducted to test the efficacy of the model that integrates the contextual information for optimal segmentation of optical cup and disc. The results of simulation show that the proposed method mRNN is efficient in improving the rate of segmentation than the other deep learning models like Drive, STARE, MESSIDOR, ORIGA, and DIARETDB.
Topics: Computer Simulation; Diagnostic Techniques, Ophthalmological; Glaucoma; Humans; Neural Networks, Computer; Optic Disk
PubMed: 35547359
DOI: 10.1155/2022/6799184 -
Eye (London, England) Oct 2021To evaluate the distribution of macula and circumpapillary retina nerve fiber layer (cpRNFL) thickness and other associated factors among grade-1 primary school children...
OBJECTIVE
To evaluate the distribution of macula and circumpapillary retina nerve fiber layer (cpRNFL) thickness and other associated factors among grade-1 primary school children in Lhasa using spectral-domain optical coherence tomography (SD-OCT).
METHODOLOGY
OCT assessment was conducted on 1856 grade-1 students from 7 primary schools in Lhasa, Tibet following a successful random stratified sampling of the students. Each child underwent comprehensive general and ocular examinations as well as an SD-OCT detection (12 × 9 mm, 3D wide scan mode, Topcon 3D OCT-1) to assess the thickness of the macula, ganglion cell-inner plexiform layer (GCIPL), ganglion cell complex (GCC), and cpRNFL. Multivariate and correlation analyses were performed to evaluate the association of the demographic and ocular variables.
RESULTS
The average age of the 1762 (94.43%) students who underwent OCT assessment was 6.83 ± 0.46 years. Among them, 984 (53.02%) were boys. The number of students who had macular, cpRNFL, and optic disc scans completed and with adequate image quality were 1412 (82.2%), 1277 (74.4%), and 1243 (72.4%), respectively. The average macula full retinal thickness (FRT), GCIPL, GCC, and cpRNFL thickness of the students was 279.19 ± 10.61 μm, 76.41 ± 4.70 μm, 108.15 ± 6.15 μm, and 112.33 ± 13.5 μm, respectively. Multivariate regression and correlation analysis further revealed that boys and girls had significant differences in their average cpRNFL thickness. Moreover, GCC and GCIPL thickness was negatively correlated with IOP but positively correlated with the body mass index. The thickness of all the layers of the macula and cpRNFL were positively correlated with spherical equivalent. Further to this, the average macular FRT, GCIPL, and GCC thicknesses were positively correlated with cpRNFL global thickness.
CONCLUSION
This study describes the normal distribution of macular retina, cpRNFL, and optic disc parameters in grade-1 Tibetan children in Lhasa. It contributes to the establishment of a normative ophthalmology database of Tibetan children, and advances the ability of OCT in ophthalmic disorder diagnosis during long-term monitoring in plateau.
Topics: Child; Cross-Sectional Studies; Female; Humans; Macula Lutea; Male; Nerve Fibers; Optic Disk; Retina; Retinal Ganglion Cells; Tomography, Optical Coherence
PubMed: 33239762
DOI: 10.1038/s41433-020-01313-z -
BMC Medical Imaging Jan 2021Glaucoma is an eye disease that causes vision loss and even blindness. The cup to disc ratio (CDR) is an important indicator for glaucoma screening and diagnosis....
BACKGROUND
Glaucoma is an eye disease that causes vision loss and even blindness. The cup to disc ratio (CDR) is an important indicator for glaucoma screening and diagnosis. Accurate segmentation for the optic disc and cup helps obtain CDR. Although many deep learning-based methods have been proposed to segment the disc and cup for fundus image, achieving highly accurate segmentation performance is still a great challenge due to the heavy overlap between the optic disc and cup.
METHODS
In this paper, we propose a two-stage method where the optic disc is firstly located and then the optic disc and cup are segmented jointly according to the interesting areas. Also, we consider the joint optic disc and cup segmentation task as a multi-category semantic segmentation task for which a deep learning-based model named DDSC-Net (densely connected depthwise separable convolution network) is proposed. Specifically, we employ depthwise separable convolutional layer and image pyramid input to form a deeper and wider network to improve segmentation performance. Finally, we evaluate our method on two publicly available datasets, Drishti-GS and REFUGE dataset.
RESULTS
The experiment results show that the proposed method outperforms state-of-the-art methods, such as pOSAL, GL-Net, M-Net and Stack-U-Net in terms of disc coefficients, with the scores of 0.9780 (optic disc) and 0.9123 (optic cup) on the DRISHTI-GS dataset, and the scores of 0.9601 (optic disc) and 0.8903 (optic cup) on the REFUGE dataset. Particularly, in the more challenging optic cup segmentation task, our method outperforms GL-Net by 0.7[Formula: see text] in terms of disc coefficients on the Drishti-GS dataset and outperforms pOSAL by 0.79[Formula: see text] on the REFUGE dataset, respectively.
CONCLUSIONS
The promising segmentation performances reveal that our method has the potential in assisting the screening and diagnosis of glaucoma.
Topics: Deep Learning; Diagnostic Techniques, Ophthalmological; Glaucoma; Humans; Image Processing, Computer-Assisted; Optic Disk
PubMed: 33509106
DOI: 10.1186/s12880-020-00528-6 -
Romanian Journal of Ophthalmology 2023A leading cause of irreversible vision loss, glaucoma needs early detection for effective management. Intraocular Pressure (IOP) is a significant risk factor for... (Review)
Review
A leading cause of irreversible vision loss, glaucoma needs early detection for effective management. Intraocular Pressure (IOP) is a significant risk factor for glaucoma. Convolutional Neural Networks (CNN) demonstrate exceptional capabilities in analyzing retinal fundus images, a non-invasive and cost-effective imaging technique widely used in glaucoma diagnosis. By learning from large datasets of annotated images, CNN can identify subtle changes in the optic nerve head and retinal structures indicative of glaucoma. This enables early and precise glaucoma diagnosis, empowering clinicians to implement timely interventions. CNNs excel in analyzing complex medical images, detecting subtle changes indicative of glaucoma with high precision. Another valuable diagnostic tool for glaucoma evaluation, Optical Coherence Tomography (OCT), provides high-resolution cross-sectional images of the retina. CNN can effectively analyze OCT scans and extract meaningful features, facilitating the identification of structural abnormalities associated with glaucoma. Visual field testing, performed using devices like the Humphrey Field Analyzer, is crucial for assessing functional vision loss in glaucoma. The integration of CNN with retinal fundus images, OCT scans, visual field testing, and IOP measurements represents a transformative approach to glaucoma detection. These advanced technologies have the potential to revolutionize ophthalmology by enabling early detection, personalized management, and improved patient outcomes. CNNs facilitate remote expert opinions and enhance treatment monitoring. Overcoming challenges such as data scarcity and interpretability can optimize CNN utilization in glaucoma diagnosis. Measuring retinal nerve fiber layer thickness as a diagnostic marker proves valuable. CNN implementation reduces healthcare costs and improves access to quality eye care. Future research should focus on optimizing architectures and incorporating novel biomarkers. CNN integration in glaucoma detection revolutionizes ophthalmology, improving patient outcomes and access to care. This review paves the way for innovative CNN-based glaucoma detection methods. CNN = Convolutional Neural Networks, AI = Artificial Intelligence, IOP = Intraocular Pressure, OCT = Optical Coherence Tomography, CLSO = Confocal Scanning Laser Ophthalmoscopy, AUC-ROC = Area Under the Receiver Operating Characteristic Curve, RNFL = Retinal Nerve Fiber Layer, RNN = Recurrent Neural Networks, VF = Visual Field, AP = Average Precision, MD = Mean Defect, sLV = square-root of Loss Variance, NN = Neural Network, WHO = World Health Organization.
Topics: Humans; Artificial Intelligence; Ophthalmology; Glaucoma; Optic Disk; Neural Networks, Computer; Intraocular Pressure; Tomography, Optical Coherence; Vision Disorders
PubMed: 37876506
DOI: 10.22336/rjo.2023.39