Cataract Detection Research Articles

Towards a Connected Mobile Cataract Screening System: A Future Approach.

Advances in computing and AI technology have promoted the development of connected health systems, indirectly influencing approaches to cataract treatment. In addition, thanks to the development of methods for cataract detection and grading using different imaging modalities, ophthalmologists can make diagnoses with significant objectivity. This paper aims to review the development and limitations of published methods for cataract detection and grading using different imaging modalities. Over the years, the proposed methods have shown significant improvement and reasonable effort towards automated cataract detection and grading systems that utilise various imaging modalities, such as optical coherence tomography (OCT), fundus, and slit-lamp images. However, more robust and fully automated cataract detection and grading systems are still needed. In addition, imaging modalities such as fundus, slit-lamps, and OCT images require medical equipment that is expensive and not portable. Therefore, the use of digital images from a smartphone as the future of cataract screening tools could be a practical and helpful solution for ophthalmologists, especially in rural areas with limited healthcare facilities.

Eye-Vision Net: Cataract Detection and Classification in Retinal and Slit Lamp Images using Deep Network

In the modern world, cataracts are the predominant cause of blindness. Early treatment and detection can reduce the number of cataract patients and prevent surgery. However, cataract grade classification is necessary to control risk and avoid blindness. Previously, various studies focused on developing a system to detect cataract type and grade. However, the existing works on cataract detection does not provide optimal results because of high detection error, lack of learning ability, computational complexity issues, etc. Therefore, the proposed work aims to develop an effective deep learning techniques for detecting and classifying cataracts from the given input samples. Here, the cataract detection and classification are performed using two phases. In order to provide an accurate cataract detection, the proposed study introduced Deep Optimized Convolutional Recurrent Network_Improved Aquila Optimization (Deep OCRN_IAO) model in phase I. Here, both retinal and slit lamp images are utilized for cataract detection. Then, the performance of these two image datasets are analysed, and the best one is chosen for cataract type and grade classification. By analysing the performance, the slit lamp images attain higher results. Therefore, phase II uses slit lamp images and detects the type and grade of cataracts through the proposed Batch Equivalence ResNet-101 (BE_ResNet101) model. The proposed classification model is highly efficient to classify the type and grades of cataracts. The experimental setup is done using MATLAB software, and the datasets used for simulation purposes are DRIMDB (Diabetic Retinopathy Images Database) and real-time slit lamp images. The proposed type and grade detection model has an accuracy of 98.87%, specificity of 99.66%, the sensitivity of 98.28%, Youden index of 95.04%, Kappa of 97.83%, and F1-score is 95.68%. The obtained results and comparative analysis proves that the proposed model is highly suitable for cataract detection and classification.

A Novel Method for Cataract Detection and Segmentation Using Nakagami Distribution

Early detection of cataract is considered as an important solution to prevent vision loss. An automatic detection of cataract is proposed in this work with the help of histogram approach. In the beginning, noises occur in an image which is also referred to as impulse noise. To eliminate this noise a non-linear type of median filter is matched especially for the morphological filter. These filtering methods help to extract the content of the image by edge detection and segmentation. The quality of the image is evaluated the image enhancing can be obtained by a histogram approach. A normalization method can be used to enhance the image which is also called Contrast stretching. To make morphological functions effective a top-hat filter is used to segment the cataract part in the given image. Nakagami distributions are usually used for extracting required important information of ultrasound details by matching histograms from the radio frequency signals. The extracted information from the Nakagami distribution is obtained by parameter values. The recent techniques used to improve the given image quality in histogram modification method are done by Intentional Camera Movement (ICM) and Unintentional Camera Movement (UCM) to recognize the real image more precisely. In the proposed method the result shows the noise reduction and a better contrast in the output image through parameters values such as Mean Squared Error (MSE) obtained as 17.23 and Peak-Signal-to-Noise Ratio (PSNR) obtained as 35.8.

Cataract Disease Detection by Using Transfer Learning-Based Intelligent Methods.

One of the most common visual disorders is cataracts, which people suffer from as they get older. The creation of a cloud on the lens of our eyes is known as a cataract. Blurred vision, faded colors, and difficulty seeing in strong light are the main symptoms of this condition. These symptoms frequently result in difficulty doing a variety of tasks. As a result, preliminary cataract detection and prevention may help to minimize the rate of blindness. This paper is aimed at classifying cataract disease using convolutional neural networks based on a publicly available image dataset. In this observation, four different convolutional neural network (CNN) meta-architectures, including InceptionV3, InceptionResnetV2, Xception, and DenseNet121, were applied by using the TensorFlow object detection framework. By using InceptionResnetV2, we were able to attain the avant-garde in cataract disease detection. This model predicted cataract disease with a training loss of 1.09%, a training accuracy of 99.54%, a validation loss of 6.22%, and a validation accuracy of 98.17% on the dataset. This model also has a sensitivity of 96.55% and a specificity of 100%. In addition, the model greatly minimizes training loss while boosting accuracy.

Examination of the Newborn: The Key Skills 1. The Eye

Performing the newborn and infant physical examination (NIPE) is now recognised as part of the midwife’s sphere of practice. The majority of higher education institutions currently provide, or plan to include, NIPE training into their pre-registration curricula1 and many qualified midwives are embracing the NIPE role through undertaking continuous professional development modules.2 In the first of a five-part series, this article considers the importance of the eye examination in the screening process. The significance of history taking, knowledge of risk factors and the detection of the red reflex will be explored. The necessity for early detection of congenital cataracts, retinoblastoma and glaucoma, and the prerequisite referral pathways that the examination requires will also be highlighted.

MTCD: Cataract detection via near infrared eye images

Globally, cataract is a common eye disease and one of the leading causes of blindness and vision impairment. The traditional process of detecting cataracts involves eye examination using a slit-lamp microscope or ophthalmoscope by an ophthalmologist, who checks for clouding of the normally clear lens of the eye. The lack of resources and unavailability of a sufficient number of experts pose a burden to the healthcare system throughout the world, and researchers are exploring the use of AI solutions for assisting the experts. Inspired by the progress in iris recognition, in this research, we present a novel algorithm for cataract detection using near-infrared eye images. The NIR cameras, which are popularly used in iris recognition, are of relatively low cost and easy to operate compared to ophthalmoscope setup for data capture. However, such NIR images have not been explored for cataract detection. We present deep learning-based eye segmentation and multitask network classification networks for cataract detection using NIR images as input. The proposed segmentation algorithm efficiently and effectively detects non-ideal eye boundaries and is cost-effective, and the classification network yields very high classification performance on the cataract dataset.

Cataract detection based on ocular B-ultrasound images by collaborative monitoring deep learning

In this paper, we collect an ocular B-ultrasound image dataset and propose a Collaborative Monitoring Deep Learning (CMDL) method to detect cataract. In the ocular B-ultrasound images, there are often strong echoes near the posterior capsule and lens, and the fine-grained ocular B-ultrasound images are often accompanied by the characteristics of weak penetrating power, low contrast, narrow imaging range, and high noise. Thus, in the proposed CMDL method, we introduce an object detection network based on YOLO-v3 to detect the focus areas we need, so as to reduce the interference of noise and improve the cataract detection accuracy of our method. Considering that the B-ultrasound image dataset we collected is small-scale, we also design three feature extraction modules to avoid over-fitting of the deep neural networks. Among them, there are a depth features extraction module based on DenseNet-161, a shape features extractor based on Fourier descriptor, and a texture features extraction module based on gray-level co-occurrence matrix. Moreover, we also introduce the collaborative learning module to improve the generalization of the proposed model. Specifically, we first fuse the depth, shape, and texture features of the eyeball and lens, respectively. Then, the fused features of the eyeball and lens are concatenated as the input of collaborative network. Finally, the introduced classification loss with the aid of collaborative loss, which distinguishes whether the eyeball and lens belong to the same category, improves the classification accuracy in cataract detection. Experimental results on our collected dataset demonstrate the effectiveness of the proposed CMDL method.

An Automated Detection and Classification of Cataract and Glaucoma Using RCNN

one of the human’s deterioration is visual impairment. Cataract and Glaucoma are the most prevailing cause blindness in the world. Early detection and treatment is the best way to prevent the blindness. Currently grading is done by human graders, it is found to be time taking and grading is usually subjective. Computer aided analysis can help human graders. An automated cataract and glaucoma detection and classification approach is proposed in this paper, to grade more objectively. Region based convolution neural network (RCNN) is used to classification process. The percentage of accuracy of classification obtained for cataract and glaucoma is 98.9% and 97.8% respectively. The method is especially suitable for cataract and glaucoma screening in the underdeveloped areas or areas which are in shortage of ophthalmic resources. It can also improve the accessibility of ophthalmic medical treatment.

The Role of Universal Newborn and Infant Physical Eye Examination Screening in the Detection of Congenital Cataract: Evidence from a Population Based Cohort Study

Background: In the United Kingdom, children should undergo eye examinations by maternity/neonatal teams or family practitioners within the first 72 post-natal hours, and again by eight weeks of age, to detect cataract. We report the frequency, associated factors and outcome of ‘late’ detection of congenital cataract in the UK. Methods: A population based prospective cohort study examining outcomes for children undergoing cataract surgery in the first two years of life, in 2009 and 2010. Detection of cataract after the age of eight weeks was defined as ‘late’. We used multivariable regression models to investigate factors associated with late detection. Findings: Of 186 babies, 64 (34%) with truly congenital cataract were diagnosed after 8 weeks of age. This late detection was independently associated with premature birth, specifically moderate and late preterm birth (adjusted odds ratio 3.0, 95%CI 1·1-8·5). The presence of co-existent globe anomaly was protective against late detection (adjOR 0·4, 95% CI 0·2-0·9). Outcomes at five years after surgery were 3- to 4-fold worse in those detected late. Interpretation: Delayed intervention in congenital cataract leads to life-long visual disability and consequent cumulative individual and societal burden. Our findings suggest that, despite a universal screening programme, one in three infants requiring surgery are not detected within the optimal period. Furthermore, there appears to be a gap in ascertainment of children born moderate to late preterm. It is necessary to improve the effectiveness of the screening programme, in particular in relation to pre-term children. Funding: Ulverscroft, Academy of Medical Sciences, NIHR. Declaration of Interest: None to declare. Ethical Approval: The study was approved by the ethics committees through the UK National Health Service Health Research Authority (Research ethics reference 08/H0714/65).

Management of cataracts secondary to intravitreal chemotherapy injections for retinoblastoma seeding.

To report our experience in the management of cataracts presumably due to intravitreal chemotherapy administration in eyes with vitreous disease associated with retinoblastoma. This retrospective study consisted of a cohort of five eyes of five retinoblastoma patients who developed cataracts secondary to intravitreal chemotherapy administration and who then underwent cataract surgery. All patients underwent lensectomy and anterior vitrectomy with/without intraocular lens implantation via clear corneal approach. All cases were administered intraoperative intravitreal melphalan (35-40 mcg) and topotecan (10-20 mcg) at the end of cataract surgery as a preventive measure against retinoblastoma spread. Injections were repeated as needed in monthly follow-ups. Main outcome measures were enucleation rate and disease-free survival time. The age at surgery ranged between 5 and 10 years. Follow-up time varied from 12 to 16 months. Treatment-free period before surgery ranged between 3 and 20 months. Time from last injection to cataract detection was: 2, 2, 10, 6, and 7 months; and time from last injection to cataract surgery was: 8, 3, 20, 7, and 15 months in cases 1-5, respectively. None of the eyes required enucleation. Tumor control was achieved in all patients at the end of follow-up. Injection of melphalan and topotecan into anterior parts of the vitreous may lead to cataract formation. This can be safely managed with lensectomy and anterior vitrectomy and the use of intravitreal administration of melphalan and topotecan at the conclusion of the surgery as a precautionary measure against the potential risk of extraocular spread.

Development of Scale Invariant lens Opacity Estimation System using Hough Circle Detection Transform Normalization and Entropy

Clear eye lens is responsible for correct vision. Ageing effect acquires opacity at lens structure causing foggy or blurred vision. It is termed as cataract. This may become cause of permanent blindness if remain unidentified and untreated. Due to hazards change in environment and adoption of sluggish lifestyle many diseases like cataract are becoming universal challenge for health organization over the world. Lack of medication and diagnosis facility in developing countries makes cataract as savior vision problem. Proposed methodology suggests image processing based, low cost solution for lens opacity or cataract detection. In this system eye lens image from input image is acquired using Iterative Hough circle detection transform. It is normalized using Daugman’s rubber sheet normalization algorithm which makes system scale invariant. Structural variation in normalized lens image is estimated in terms of entropy or mean value. Comparison of right and left half entropies of normalized image is basis for estimation of lens opacity. It is used to detect and categorize lens opacity or cataract. This system easily categorize lens opacity based on structural features of opacity in one of three grades such as “No cataract”, “Cortical cataract” or “Nuclear cataract”.

Detection of Cataract Based on Image Features Using Convolutional Neural Networks

Cataract are the highest cause of blindness that there are 32.4 million people experiencing blindness and as many as 191 million people experiencing visual disabilities in 2010 in the world. On the other hand, the longer a patient suffers from cataracts or late treatment. The development of cataract identification using a traditional algorithm based on feature representation is highly dependent on the classification process carried out by an eye specialist so that the method is prone to misclassification of a person detected or not. However, at this time there is a deep learning, convolutional neural network (CNN) which is used for pattern recognition which can help automate image classification. This research was conducted to increase the accuracy value and minimize data loss in the process of cataract identification by performing an experience namely the manipulation process was carried out by changing epochs. The results of this study indicate that the addition of epochs affects accuracy and loss data from CNN. By comparing variety of epoch values it can be ignored that the higher the age values used, the higher the value of the model. In this study, using the epoch 50 value reached the highest value with a value of 95%. Based on the model that has been made it has also been successful to receive images according to the specified class. After testing accurately, 10 images achieved an average accuracy of 88%.

Manhattan Vision Screening and Follow-up Study in Vulnerable Populations (NYC-SIGHT): Design and Methodology.

The Manhattan Vision Screening and Follow-up Study in Vulnerable Populations is a 5-year prospective, cluster-randomized study to improve detection and management of glaucoma and other eye diseases in vulnerable populations living in affordable housing developments. To describe the study design and methodology of the Manhattan Vision Screening and Follow-up Study in Vulnerable Populations, which aims to investigate whether community-based vision screenings can improve detection and management of glaucoma, vision impairment, cataract, and other eye diseases among vulnerable populations living in affordable housing developments in upper Manhattan. This 5-year prospective, cluster-randomized, controlled trial consists of vision screening and referral for follow-up eye care among eligible residents aged 40 and older. Visual acuity, intraocular pressure (IOP), and fundus photography are measured. Participants with visual worse than 20/40, or IOP 23 to 29 mm Hg, or unreadable fundus images fail the screening and are scheduled with the on-site optometrist. Those with an abnormal image and/or IOP ≥30 mm Hg, are assigned as "fast-track" and referred to ophthalmology. Participants living in 7 developments randomized to the Enhanced Intervention Group who fail the screening and need vision correction receive complimentary eyeglasses. Those referred to ophthalmology receive enhanced support with patient navigators to assist with follow-up eye care. Participants living in 3 developments randomized to the Usual Care Group who fail the screening and need vision correction are given an eyeglasses prescription only and a list of optical shops. No enhanced support is given to the Usual Care Group. All participants referred to ophthalmology are assisted in making their initial eye exam appointment. This study targets vulnerable populations where they live to ensure improved access to and utilization of eye care services in those who are least likely to seek eye care.

Detection of Posterior Segment Eye Disease in Rural Eye Camps in South India: A Nonrandomized Cluster Trial

Rural screening camps in India historically have focused on detection of cataract and uncorrected refractive error. This study aimed to increase detection, referral, and follow-up for posterior segment diseases (PSDs) in rural eye camps using a novel technology-driven eye camp model. A clustered nonrandomized trial in the catchment area of Aravind Eye Care System (AECS) Pondicherry, to compare 2 eye camp models: the traditional AECS eye camp model and the novel, technology-driven, eye camp model. Patients 40 to 75 years of age who attended free camps conducted by AECS Pondicherry. Those with corneal pathologic features were excluded because this precluded an adequate view of the posterior segment to screen for PSD. The clinical protocols in the 2 arms were standardized and the same study team was used in both study arms. The unit of allocation to the 2 study arms was at the level of the eye camp, rather than the level of the individual study participant. The primary study outcome was detection of suspected PSD (glaucoma, diabetic retinopathy, age-related macular degeneration, other PSDs). Secondary outcomes included: (1) the proportion of referred participants who underwent an examination at the base hospital and (2) the proportion with confirmed PSD on examination at the base hospital. The study included 11 traditional and 18 novel eye camps with a total of 3048 participants (50% in each study arm). The mean age of all participants was 58.4 ± 9.1 years and 1434 participants (47%) were men. The proportion receiving a referral for PSD was significantly greater in the novel (8.3%) compared with the traditional (3.6%) eye camp (P < 0.001; risk ratio, 2.31; 95% confidence interval, 2.30-2.34). Among the 183 participants referred from the camps for PSD, 73 (39.9%) followed up for further evaluation at the base hospital. In a resource-constrained setting, use of digital fundus photography in novel eye camps resulted in increased detection of and referral for PSD. Further research is needed to determine whether this intervention is cost effective and may contribute to prevention of avoidable blindness and visual impairment in South India. Further research also is needed to improve follow-up of patients referred from camps for suspicion of PSD.

Efficient network selection for computer-aided cataract diagnosis under noisy environment

Background and objective:Computer-aided cataract diagnosis (CACD) methods play a crucial role in early detection of cataract. The existing CACD methods are suffering from performance diminution due to the presence of noise in digital fundus retinal images. The lack of robustness in CACD methods against noise is a serious concern since even the presence of small noise levels may degrade the performance of cataract detection. However, noise in fundus retinal images is unavoidable due to various processes involved in the acquisition or transmission. Hence, a robust CACD method against noisy conditions is required to diagnose the cataract accurately.Methods:In this paper, an efficient network selection based robust CACD method under additive white Gaussian noise (AWGN) is proposed. The presented method consists a set of locally- and globally-trained independent support vector networks with features extracted at various noise levels. A suitable network is then selected based on the noise level present in the input image. The automatic feature extraction technique using pre-trained convolutional neural network (CNN) is adopted to extract features from input fundus retinal images.Results:A good-quality fundus retinal image dataset is obtained from EyePACS dataset with the use of natural image quality evaluator (NIQE) score. The synthetic noisy fundus retinal images are then generated artificially from good-quality fundus retinal images using AWGN model for effective analysis. The analysis is carried out with existing CNN based CACD methods at different noise levels. From results it is obvious that the proposed CACD method is superior in exhibiting robust performance against AWGN than existing CNN based CACD methods.Conclusions:From the experimental results, it is clear that the proposed method show superior performance against noise when compared with existing methods in literature. The proposed method can be useful as a starting point to continue further research on CNN based robust CACD methods.

CataractNet: An Automated Cataract Detection System Using Deep Learning for Fundus Images

Cataract is one of the most common eye disorders that causes vision distortion. Accurate and timely detection of cataracts is the best way to control the risk and avoid blindness. Recently, artificial intelligence-based cataract detection systems have been received research attention. In this paper, a novel deep neural network, namely CataractNet , is proposed for automatic cataract detection in fundus images. The loss and activation functions are tuned to train the network with small kernels, fewer training parameters, and layers. Thus, the computational cost and average running time of CataractNet are significantly reduced compared to other pre-trained Convolutional Neural Network (CNN) models. The proposed network is optimized with the Adam optimizer. A total of 1130 cataract and non-cataract fundus images are collected and augmented to 4746 images to train the model. For avoiding the over-fitting problem, the dataset is extended through augmentation before model training. Experimental results prove that the proposed method outperforms the state-of-the-art cataract detection approaches with an average accuracy of 99.13%.

Detecting Cataract Using Smartphones.

Objective: Cataract, which is the clouding of the crystalline lens, is the most prevalent eye disease accounting for 51% of all eye diseases in the U.S. Cataract is a progressive disease, and its early detection is critical for preventing blindness. In this paper, an efficient approach to identify cataract disease by adopting luminance features using a smartphone is proposed. Methods: Initially, eye images captured by a smartphone were cropped to extract the lens, and the images were preprocessed to remove irrelevant background and noise by utilizing median filter and watershed transformation. Then, a novel luminance transformation from pixel brightness algorithm was introduced to extract lens image features. The luminance and texture features of different types of cataract disease images could be obtained accurately in this stage. Finally, by adopting support vector machines (SVM) as the classification method, cataract eyes were identified. Results: From all the images that we fed into our system, our method could diagnose diseased eyes with 96.6% accuracy, 93.4% specificity, and 93.75% sensitivity. Conclusion: The proposed method provides an affordable, rapid, easy-to-use, and versatile method for detecting cataracts by using smartphones without the use of bulky and expensive imaging devices. This methodcan be used for bedside telemedicine applications or in remote areas that have medical shortages. Previous smartphone-based cataract detection methods include texture feature analysis with 95 % accuracy, Gray Level Co-occurrence Matrix (GLCM) method with 89% accuracy, red reflex measurement method, and RGB color feature extraction method using cascade classifier with 90% accuracy. The accuracy of cataract detection in these studies is subject to changes in smartphone models and/or environmental conditions. However, our novel luminance-based method copes with different smartphone camera sensors and chroma variations, while operating independently from sensors’ color characteristics and changes in distances and camera angle. Clinical and Translational Impact—This study is an early/pre-clinical research proposing a novel luminance-based method of detecting cataract using smartphones for remote/at-home monitoring and telemedicine application.

Deep Learning-Based Cataract Detection and Grading from Slit-Lamp and Retro-Illumination Photographs: Model Development and Validation Study

Backgroud: Accurate detection and grading of cataract is pivotal in providing personalized precision treatment strategies for preventing visual loss. We develop and validate an automated deep learning (DL)-based artificial intelligence (AI) platform for diagnosing and grading cataracts, using slit-lamp and retro-illumination lens photographs based on the Lens Opacities Classification System-Ⅲ (LOCS-Ⅲ). Methods: Slit-lamp and retro-illumination lens photographs were graded by two trained graders using LOCS-III. Image datasets were labeled and divided into training, validation, and test datasets. We trained and validated AI platforms with four key strategies in the AI domain: (1) region detection network for redundant information inside data, (2) data augmentation and transfer learning for the small dataset size problem, (3) generalized cross entropy loss for dataset bias, and (4) class balanced loss for class imbalance problems. The performance of the AI platform was reinforced with an ensemble of three AI algorithms (ResNet18, WideResNet50-2, and ResNext50). Finding: The AI platform showed robust diagnostic performance (AUC of 0.9992 [0.9986-0.9998], 0.9994 [0.9989-0.9998]; accuracy of 98.82% [97.7-99.9], 98.51% [97.4-99.6]) and grading prediction performance (AUC of 0.9567 [0.9501-0.9633], 0.9650 [0.9509-0.9792]; accuracy of 91.22% [89.4-93.0], 90.26% [88.6-91.9]) for nuclear opalescence (NO) and nuclear color (NC) using slit-lamp photographs. For cortical opacity (CO) and posterior subcapsular opacity (PSC), the system achieved high diagnostic performance (AUC of 0.9680 [0.9579-0.9781], 0.9465 [0.9348-0.9582]; accuracy of 96.21% [94.4-98.0], 92.17% [88.6-95.8]) and good grading prediction performance (AUC of 0.9044 [0.8958-0.9129], 0.9174 [0.9055-0.9295]; accuracy of 91.33% [89.7-93.0], 87.89% [85.6-90.2]) using retro-illumination images. Interpretation: Our DL-based AI platform successfully yielded accurate and precise detection and grading of NO and NC in seven-level classification and CO and PSC in six-level classification, overcoming the limitations of medical databases such as small training data or biased label distribution. Funding: This work was supported by the National Research Foundation of Korea grant funded by the Korean government's Ministry of Education (NRF-2021R1C1C1007795; Seoul, Korea), which was received by D.H.L. Declaration of Interest: The authors declare no conflict of interest Ethical Approval: The study was approved by the Samsung Medical Center Institutional Review Board (IRB file number 2020-08-035)

Computer Aided Diagnosis Based on Artificial Intelligence in Providing Support for Health Services in Rural Areas

The health service system in Indonesia is still a homework that needs to be addressed by the government. Both in terms of equity, regulation, and integration between offline and online systems. As for what is happening now, there are at least some obstacles that need to be addressed and addressed as soon as possible. On the other hand, health development should beseen as an investment to improve the quality of human resources and support economic development, and have an essential role in poverty reduction efforts. Today’s primary health development problems include the high disparity in health status between socioeconomic levels, regions, and urban and rural areas. In general, the health status of people with high socioeconomic levels in western Indonesia and urban areas tends to be better. On the other hand, the health status of people with low socioeconomic status in eastern Indonesia and rural areas is still lagging. The quality, equity, and affordability of health services are also still low. The quality of service is an obstacle because medical personnel is minimal and equipment is inadequate. In terms of numbers, the ratio of health workers to the population to be served is still low. The affordability of services is closely related to the number and distribution of healthfacilities. In terms of the limitations of medical equipment, this is due to the high price of the medical equipment. Besides, special training is also needed in medical equipment, which training takes quite a long time, and the material is quite complex. With these conditions, it is necessary to optimize technology to develop medical equipment, especially diagnostic support equipment that is cheap, easy to use, and has high accuracy. Our research aims to address optimizing image processing technology and artificial intelligence in developing technology to support health services in rural areas. We tried to implement several cases of health services that often occur in rural areas, namely early detection of cataracts, optimizing the use of ultrasound machines, optimizing the use of lowquality MRI machines in the diagnosis of Alzheimer’s, using microscopic images to identify leukocytes and using X-ray images to support the diagnosis. COVID19. Of the several modulesthat we have developed for health care support technology, the results show promisingprospects with a good level of accuracy, ease to use, and use modalities that already exist in the lowest health service units to reduce prices. It is hoped that this image processing and artificial intelligence-based health service support module can be used in rural areas to overcome disparities in health services in Indonesia.

The capability of early detection of eye diseases in the community by primary health facility worker

Cataract is one of the biggest causes of blindness in the world. As many as 2.4 million people suffered from cataracts in Indonesia in 2013. Therefore, early detection needs to be done to prevent further complications. However, health workers' limited ability in primary health care facilities has resulted in high cataract diagnosis rates. The purpose of this study was to determine the ability of early detection of eye diseases, especially cataracts by primary health care workers. This study was conducted using a quantitative descriptive method analyzing 158 medical records as a result of early detection of primary health care facilities in Sleman Regency, which were sent to the ophthalmologist team of the Department of Eye Health FKKMK UGM to be re-examined. The re-examination results by a team of ophthalmologists received 141 medical records that fulfilled the inclusion criteria, consisting of 61 patients confirmed by cataract diagnosis (43.3%) and 80 patients with a diagnosis of not cataract (56.7%). According to the study, primary health care workers' ability towards early detection of cataracts in Sleman Regency needed to be enhanced (4.3%).