A New Strategy for the Detection of Diabetic Retinopathy using a Smartphone App and Machine Learning Methods Embedded on Cloud Computer

Alves, Shara S. A.; Matos, Alexis Galeno; Almeida, Jefferson S.; Benevides, Cilis Aragao; Cunha, Caio Cesar Henrique; Santiago, Rhuan Victor Crescencio; Pereira, Renato F.; Filho, Pedro Pedrosa Rebouças

doi:10.1109/cbms49503.2020.00108

Cited by 10 publications

(7 citation statements)

References 10 publications

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“…These methods combine several base models for obtaining optimal predictive model. in this review have used these algorithms for classification of diabetic retinopathy [10]. ARIMA models are used in one study for analysing the time series data [20].…”

Section: ) Machine Learning Algorithms Used For Analysismentioning

confidence: 99%

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Systematic Review on Machine-Learning Algorithms Used in Wearable-Based eHealth Data Analysis

2021

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In this digitized world, data has become an integral part in any domain, including healthcare. The healthcare industry produces a huge amount of digital data, by utilizing information from all sources of healthcare, including the patients' demographics, medications, vital signs, physician's observations, laboratory data, billing data, data from various wearable sensors, etc. With the rapid growth of the wireless technology applications, there has also been a significant increase in the digital health data. New medical discoveries and new eHealth-related technologies, such as mobile apps, novel sensors, and wearable technology have contributed as important data sources for healthcare data. Nowadays, there is a huge potential to improve the healthcare quality and customer satisfaction with the help of machine learning (ML) algorithms applied on time-domain and frequency-domain healthcare data obtained from wearables and sensors. This systematic literature review examines in depth how health data from sensors can be processed and analyzed using ML techniques. The review focuses on the following diseases for obtaining the eHealth data: diabetes mellitus type 1 and type 2, hypertension and hypotension, atrial fibrillation, bradykinesia, dyskinesia, and fever related diseases. The data for the systematic literature review was collected from four databases, Medline, Proquest, Scopus, and Web of Science. We selected 67 studies for the final in-depth review out of the initial pre-selected papers. Our study identified that the major part of eHealth data is obtained from the sensors such as accelerometer, gyroscopes, ECG (Electrocardiogram), EEG (Electroencephalogram ) monitors, and blood glucose sensors. This study also examines the different feature types, feature extraction methods, and ML algorithms used for eHealth data analysis. Our review also shows that neural network (NN) algorithms and support vector machines (SVM) have shown so far the best performance for analyzing the healthcare data among other ML algorithms studied in the literature.

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Section: ) Machine Learning Algorithms Used For Analysismentioning

confidence: 99%

“…CNN is used for image classification applications. Studies have used CNN for ECG image data, ECG signals with spectogram as input [5], and diabetic retinopathy identification [10]. Deep neural network and artificial or shallow neural network is used 6 and 4 studies, respectively.…”

Section: ) Machine Learning Algorithms Used For Analysismentioning

confidence: 99%

Systematic Review on Machine-Learning Algorithms Used in Wearable-Based eHealth Data Analysis

2021

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“…The availability and improvements of smart gadgets such as smartphones have made diabetes-related functions more accessible. Many studies have been conducted to investigate this much-appreciated technology [ 4 , 5 ]. These often still necessitate the use of an externally attachable sensor, and monitoring is then given via an app or a separate continuous glucose monitoring device, which is often semi-invasive and requires connectivity range via Bluetooth or Wi-Fi signal.…”

Section: Introductionmentioning

confidence: 99%

The Effectiveness of Wearable Devices Using Artificial Intelligence for Blood Glucose Level Forecasting or Prediction: Systematic Review

Ahmed¹,

Aziz²,

Abd‐Alrazaq³

et al. 2023

J Med Internet Res

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Background In 2021 alone, diabetes mellitus, a metabolic disorder primarily characterized by abnormally high blood glucose (BG) levels, affected 537 million people globally, and over 6 million deaths were reported. The use of noninvasive technologies, such as wearable devices (WDs), to regulate and monitor BG in people with diabetes is a relatively new concept and yet in its infancy. Noninvasive WDs coupled with machine learning (ML) techniques have the potential to understand and conclude meaningful information from the gathered data and provide clinically meaningful advanced analytics for the purpose of forecasting or prediction. Objective The purpose of this study is to provide a systematic review complete with a quality assessment looking at diabetes effectiveness of using artificial intelligence (AI) in WDs for forecasting or predicting BG levels. Methods We searched 7 of the most popular bibliographic databases. Two reviewers performed study selection and data extraction independently before cross-checking the extracted data. A narrative approach was used to synthesize the data. Quality assessment was performed using an adapted version of the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Results From the initial 3872 studies, the features from 12 studies were reported after filtering according to our predefined inclusion criteria. The reference standard in all studies overall (n=11, 92%) was classified as low, as all ground truths were easily replicable. Since the data input to AI technology was highly standardized and there was no effect of flow or time frame on the final output, both factors were categorized in a low-risk group (n=11, 92%). It was observed that classical ML approaches were deployed by half of the studies, the most popular being ensemble-boosted trees (random forest). The most common evaluation metric used was Clarke grid error (n=7, 58%), followed by root mean square error (n=5, 42%). The wide usage of photoplethysmogram and near-infrared sensors was observed on wrist-worn devices. Conclusions This review has provided the most extensive work to date summarizing WDs that use ML for diabetic-related BG level forecasting or prediction. Although current studies are few, this study suggests that the general quality of the studies was considered high, as revealed by the QUADAS-2 assessment tool. Further validation is needed for commercially available devices, but we envisage that WDs in general have the potential to remove the need for invasive devices completely for glucose monitoring in the not-too-distant future. Trial Registration PROSPERO CRD42022303175; https://tinyurl.com/3n9jaayc

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“…The availability and advancements of smart devices, such as smartphones, have made the monitoring of diabetes-related features more accessible. Many studies have examined this much welcomed technology [ 5 , 6 ]. These normally require the use of an external attachable sensor, and monitoring is then delivered via an app or a separate continuous glucose monitoring (CGM) device, which can still be semi-invasive and require a connection range via Bluetooth or Wi-Fi signals.…”

Section: Introductionmentioning

confidence: 99%

Overview of Artificial Intelligence–Driven Wearable Devices for Diabetes: Scoping Review

Ahmed¹,

Aziz²,

Abd‐Alrazaq³

et al. 2022

J Med Internet Res

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Background Prevalence of diabetes has steadily increased over the last few decades with 1.5 million deaths reported in 2012 alone. Traditionally, analyzing patients with diabetes has remained a largely invasive approach. Wearable devices (WDs) make use of sensors historically reserved for hospital settings. WDs coupled with artificial intelligence (AI) algorithms show promise to help understand and conclude meaningful information from the gathered data and provide advanced and clinically meaningful analytics. Objective This review aimed to provide an overview of AI-driven WD features for diabetes and their use in monitoring diabetes-related parameters. Methods We searched 7 of the most popular bibliographic databases using 3 groups of search terms related to diabetes, WDs, and AI. A 2-stage process was followed for study selection: reading abstracts and titles followed by full-text screening. Two reviewers independently performed study selection and data extraction, and disagreements were resolved by consensus. A narrative approach was used to synthesize the data. Results From an initial 3872 studies, we report the features from 37 studies post filtering according to our predefined inclusion criteria. Most of the studies targeted type 1 diabetes, type 2 diabetes, or both (21/37, 57%). Many studies (15/37, 41%) reported blood glucose as their main measurement. More than half of the studies (21/37, 57%) had the aim of estimation and prediction of glucose or glucose level monitoring. Over half of the reviewed studies looked at wrist-worn devices. Only 41% of the study devices were commercially available. We observed the use of multiple sensors with photoplethysmography sensors being most prevalent in 32% (12/37) of studies. Studies reported and compared >1 machine learning (ML) model with high levels of accuracy. Support vector machine was the most reported (13/37, 35%), followed by random forest (12/37, 32%). Conclusions This review is the most extensive work, to date, summarizing WDs that use ML for people with diabetes, and provides research direction to those wanting to further contribute to this emerging field. Given the advancements in WD technologies replacing the need for invasive hospital setting devices, we see great advancement potential in this domain. Further work is needed to validate the ML approaches on clinical data from WDs and provide meaningful analytics that could serve as data gathering, monitoring, prediction, classification, and recommendation devices in the context of diabetes.

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A New Strategy for the Detection of Diabetic Retinopathy using a Smartphone App and Machine Learning Methods Embedded on Cloud Computer

Cited by 10 publications

References 10 publications

Systematic Review on Machine-Learning Algorithms Used in Wearable-Based eHealth Data Analysis

Systematic Review on Machine-Learning Algorithms Used in Wearable-Based eHealth Data Analysis

The Effectiveness of Wearable Devices Using Artificial Intelligence for Blood Glucose Level Forecasting or Prediction: Systematic Review

Overview of Artificial Intelligence–Driven Wearable Devices for Diabetes: Scoping Review

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