Artificial Intelligence-Based Teleopthalmology Application for Diagnosis of Diabetics Retinopathy

Ghouali, Samir; Onyema, Edeh Michael; Guellil, Mohammed Seghir; Wajid, Mohd Anas; Clare, O.; Cherifi, Walid; Feham, Mohammed

doi:10.1109/ojemb.2022.3192780

Cited by 30 publications

(9 citation statements)

References 57 publications

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“…The result obtained from unsupervised clustering for intubation recognition is about 87.8 percent [12]. In a similar manner the authors in [21] have proposed AI-based application for the diagnostic of diabetes retinopathy. [25] conducted a detailed review focussing the utility of AI in the domain of imaging for COVID-19 patient care.…”

Section: Iot-based Technologies the Authors Inmentioning

confidence: 79%

“…Technology Target population Covid/Post-Covid Sharma et.al [10] IoT, Mobile Computing, Cloud Computing Elderly people Covid Taiwo et.al [11] IoT General Both Verma et.al [16] ML, Bioinformatics General Postcovid Deepa et.al [17] ML,Random Forest,Naïve Bayes,IoT General Covid Boussen, et.al [20] Unsupervised ML, clustering Generic covid Born et.al [21] AI, Data Analysis General Covid Ieracitano et.al [22] AI General Covid Nabavi, et.al [23] ML, AI General Covid Pahar et.al [24] Transfer learning,ML, Deep learning General Covid-19 Khanday et.al [26] ML, Deep Learning. General Covid Alhomdy et.al [27] Cloud Computing Generic Covid Moorthy.et.al [28] Cloud computing, wearables.…”

Section: Referencesmentioning

confidence: 99%

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Post-Covid Remote Patient Monitoring using Medical Internet of Things and Machine Learning Analytics

Rahman

Parveen

Sofi

et al. 2023

SCPE

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The Covid-19 pandemic disturbed the smooth functioning of healthcare services throughout the world. New practices such as masking, social distancing and so on were followed to prevent the spread. Further, the severity of the problem increases for the elderly people and people having co-morbidities as proper medical care was not possible and as a result many deaths were recorded. Even for those patients who recovered from Covid could not get proper health monitoring in the Post-Covid phase as a result many deaths and severity in health conditions were reported after the Covid recovery i.e., the Post-Covid era. Technical interventions like the Internet of Things (IoT) based remote patient monitoring using Medical Internet of Things (M-IoT) wearables is one of the solutions that could help in the Post-Covid scenarios. The paper discusses a proposed framework where in a variety of IoT sensing devices along with ML algorithms are used for patient monitoring by utilizing aggregated data acquired from the registered Post-Covid patients. Thus, by using M-IoT along with Machine Learning (ML) approaches could help us in monitoring Post-Covid patients with co-morbidities for and immediate medical help.

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Section: Iot-based Technologies the Authors Inmentioning

confidence: 79%

Section: Referencesmentioning

confidence: 99%

Post-Covid Remote Patient Monitoring using Medical Internet of Things and Machine Learning Analytics

Rahman

Parveen

Sofi

et al. 2023

SCPE

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“…Developed countries have a higher rate of diabetes than undeveloped countries. Several people with DR live in developing countries [ 24 ]. This is because of insufficient treatment and improper health care management.…”

Section: Discussionmentioning

confidence: 99%

DR-NASNet: Automated System to Detect and Classify Diabetic Retinopathy Severity Using Improved Pretrained NASNet Model

et al. 2023

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Diabetes is a widely spread disease that significantly affects people’s lives. The leading cause is uncontrolled levels of blood glucose, which develop eye defects over time, including Diabetic Retinopathy (DR), which results in severe visual loss. The primary factor causing blindness is considered to be DR in diabetic patients. DR treatment tries to control the disease’s severity, as it is irreversible. The primary goal of this effort is to create a reliable method for automatically detecting the severity of DR. This paper proposes a new automated system (DR-NASNet) to detect and classify DR severity using an improved pretrained NASNet Model. To develop the DR-NASNet system, we first utilized a preprocessing technique that takes advantage of Ben Graham and CLAHE to lessen noise, emphasize lesions, and ultimately improve DR classification performance. Taking into account the imbalance between classes in the dataset, data augmentation procedures were conducted to control overfitting. Next, we have integrated dense blocks into the NASNet architecture to improve the effectiveness of classification results for five severity levels of DR. In practice, the DR-NASNet model achieves state-of-the-art results with a smaller model size and lower complexity. To test the performance of the DR-NASNet system, a combination of various datasets is used in this paper. To learn effective features from DR images, we used a pretrained model on the dataset. The last step is to put the image into one of five categories: No DR, Mild, Moderate, Proliferate, or Severe. To carry this out, the classifier layer of a linear SVM with a linear activation function must be added. The DR-NASNet system was tested using six different experiments. The system achieves 96.05% accuracy with the challenging DR dataset. The results and comparisons demonstrate that the DR-NASNet system improves a model’s performance and learning ability. As a result, the DR-NASNet system provides assistance to ophthalmologists by describing an effective system for classifying early-stage levels of DR.

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“…The field of diabetes management is seeing a revolution in the use of artificial intelligence (AI). The paradigm of diabetes care has changed as a result of the incorporation of technology like continuous glucose monitoring devices and the creation of artificial pancreas systems [16]. The utilization of artificial intelligence (AI) algorithms for tailored medical aid and sophisticated analysis has been spurred by the exponential growth of electronic data from diabetes patients.…”

Section: Artificial Intelligence Techniques In Diabetes Carementioning

confidence: 99%

How Artificial Intelligence Technology can be Used to Treat Diabetes

Jawaid,

Qureshi

2024

Preprint

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Background: The research is conducted against the backdrop of the rising incidence of diabetes and the demand for novel approaches to improve patient outcomes. With the goal of revolutionizing conventional methods and addressing the changing healthcare landscape, the study concentrated on the application of AI technology in diabetes treatment. Objective: Examining the various ways AI can be used in diabetes care and assessing how it can enhance automated screenings, tailored treatment recommendations, predictive modeling, and patient self-management were the main goals of the study. Result: The findings demonstrated how well AI works to anticipate a patient's unique reaction to a therapy, assist medical personnel in making decisions, and give patients more control over their own care. Artificial intelligence (AI)-powered solutions showed promise in improving real-time monitoring and encouraging patient adherence, supporting a comprehensive strategy for managing diabetes. Conclusion: The research indicates that artificial intelligence (AI) has great potential to change the way diabetes is treated. However, it also highlights the need for more research, ethical issues, and validation of AI. The results advocate for joint efforts to ensure responsible and patient-centered technology innovation in diabetes care and encourage the integration of AI with new technologies.

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Artificial Intelligence-Based Teleopthalmology Application for Diagnosis of Diabetics Retinopathy

Cited by 30 publications

References 57 publications

Post-Covid Remote Patient Monitoring using Medical Internet of Things and Machine Learning Analytics

Post-Covid Remote Patient Monitoring using Medical Internet of Things and Machine Learning Analytics

DR-NASNet: Automated System to Detect and Classify Diabetic Retinopathy Severity Using Improved Pretrained NASNet Model

How Artificial Intelligence Technology can be Used to Treat Diabetes

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