Table 1 Summary of study characteristics

¹¹

Anjum, 2024

Optimizing type 2 diabetes management: AI-enhanced time series analysis of continuous glucose monitoring data for personalized dietary intervention

To forecast interstitial glucose values using AI models and provide personalized dietary interventions to manage T2D.

40, 45 (two patients reported)

NR

8 T2D patients, 10,160 CGM readings total

Mean glucose: 8.295 mmol/L; Min: 3.4, Max: 19.9; SD: 2.584

T2D

NR

NR

UK

CGM data collected from FreeStyle Libre system

Private

Mar 2021 - Dec 2021 ( ~ 9 months)

Real-world

¹⁶

Vettoretti, 2020

Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors

To review AI methods integrated with CGM for DSS in T1D: bolus calculation, BC parameter tuning, and glucose prediction

NR

NR

10 clinical participants; 20–100 simulated subjects

NR

T1D

NR

NR

Italy

ABC4D; UVa/Padova T1D Simulator

Private

NR

Mixed

²¹

Ribeiro 2024

A Novel AI Approach for Assessing Stress Levels in Patients with Type 2 Diabetes Mellitus Based on the Acquisition of Physiological Parameters Acquired during Daily Life

Develop an AI-based wearable system for stress classification in T2D patients using physiological indicators.

Mean (SD) Min-Max: Years 42 (17) 12-75

47% F

128

HbA1c: 7.9 (5-12)

BMI (kg/m2): 35 (10-67),

BP: 62% High, 30% Normal, 8% Other

FBS: 163 (80-280)

FH T2D: 40% Yes

Smoke: 62% Yes

T2D

NR

Hypertension

N/A

Public dataset of 128 diabetic patients [Kaggle]

Public; in silico

N/A

N/A

⁵⁷

Zhu, 2024

Population-Specific Glucose Prediction in Diabetes Care With Transformer-Based Deep Learning on the Edge

To develop a low-power, population-specific, multi-horizon glucose prediction model deployable on wearable devices

NR (varies by dataset; age used as input)

NR

124 total (24 T1D, 100 T2D)

RMSE, MAE, MAPE, gRMSE reported; example RMSE: 14.7-23.5 mg/dL

Mixed (T1D + T2D)

NR

NR

UK, China

OhioT1DM, ShanghaiDM

Public

8 weeks (OhioT1DM), 14 days (ShanghaiDM)

Real-world

⁵⁸

Zhu 2024

Multi-Horizon Glucose Prediction Across Populations with Deep Domain Generalization

Develop a generalizable, multi-horizon glucose prediction model for diverse populations using CGM data

Mean (SD): Years

REPLACE-BG: 44 (14), OhioT1DM: 50 (10),

ABC4D: 47 (17),

GVAS: 74 (12)

REPLACE-BG: 112 (50%) F,

OhioT1DM: 5 (42%) F,

ABC4D: 12 (55%) F,

GVAS: 18 (40%) F

REPLACE-BG: 226; OhioT1DM: 12;

ABC4D: 22;

GVAS: 45

Mean (SD): mg/dL

REPLACE-BG: 160 (26),

OhioT1DM: 162 (20),

ABC4D: 178 (18),

GVAS: 140 (48)

T1D; T2D

REPLACE-BG: ~90% White,

OhioT1DM: NR,

ABC4D: NR,

GVAS: NR

GVAS: Stroke

REPLACE-BG: US,

OhioT1DM: US,

ABC4D: UK,

GVAS: UK

REPLACE-BG, OhioT1DM (public); ABC4D, GVAS (proprietary)

Public: OhioT1DM; Private: REPLACE-BG, ABC4D, GVAS; all in vivo

3-day CGM segments per participant used for modeling; dataset durations vary (72 h to 26 wks)

REPLACE-BG: Controlled,

OhioT1DM: Controlled,

ABC4D: Controlled,

GVAS: Real-world

⁵⁹

Warren, 2024

A Scalable Application of Artificial Intelligence-Driven Insulin Titration Program…

To assess the scalability and effectiveness of AI-driven insulin titration (d-Nav) in real-world T2D management

67.1 ± 11.5 years

52.8% male

600 patients

Baseline HbA1c: 8.6% ± 2.1%; reduced to 7.3% ± 1.2%; TIR improved from 47.7% to 65.4% in CGM subgroup; hypoglycemia <54 mg/dL: 0.4–0.6/month

T2D

46.2% non-Caucasian; 37.2% African American; 53.8% Caucasian

66.3% hypertension, 64.8% dyslipidemia, 36% CVD, 20.5% nephropathy

USA

Physicians East, North Carolina

Private

Oct 2022-Sep 2023

Clinical

⁶⁰

Wang 2023

A novel hypoglycemia alarm framework for type 2 diabetes with high glycemic variability

Develop a hypoglycemia early alarm framework for T2D patients with high glycemic variability using dynamic multi-scale features

NR

NR

204

Mean (Range): mg/dL

Train: 158 (40-382)

Test: 164 (40-400)

CV of 0.36 or greater

T2D

NR

NR

China

Shanghai Sixth People’s Hospital

Private; in vivo

36 hours; 3–5 consecutive days of CGM data per participant

Hospital-based (real-world)

⁶¹

Tucker, 2024

Neural Networks With Gated Recurrent Units Reduce Glucose Forecasting Error Due to Changes in Sensor Location

To examine whether GRU neural networks can reduce glucose forecast error caused by changes in CGM sensor location

Mean 60.4 years (range 45–79)

6 females, 7 males

13 participants

RMSE, MARD, glycemic standard deviation

T2D

NR

NR

USA

Clinical study, Univ. of Minnesota (NCT03481530)

In vivo

15 weeks

Real-world

⁶²

Tao 2023

A Double Deep Latent Autoencoder for Diabetic Retinopathy Diagnose Based on Continuous Glucose Sensors

To diagnose diabetic retinopathy using CGM data with a deep latent autoencoder framework

NR

NR

26 individuals

NR

T2D

NR

NR

Iran

Sina Hospital, Tehran University

Private

NR

Clinical

⁶³

Shuzan 2024

QU-GM: An IoT Based Glucose Monitoring System From Photoplethysmography, interstitial Pressure, and Demographic Data Using Machine Learning

Develop a non-invasive, real-time wearable system for glucose estimation and severity classification

Median (Min-Max) 43 (13–87) years

42% F

139

Median (Min-Max)

Glucose (mg/dL): 118 (66-600),

BMI (kg/m2): 27 (17-46),

SBP: 135 (90-234)

DBP: 93 (60-169)

Pulse Rate: 79 (53-128)

Diabetes 50%,

Healthy 50%

NR

NR

Qatar

Qatar Diabetic Society; IRB-HMC-2021-011

Private; in vivo

Single visit (spot measurement)

Controlled/laboratory setting

⁶⁴

Seo, 2024

Generative Adversarial Network-Based Data Augmentation for Improving Hypoglycemia Prediction

To improve hypoglycemia prediction performance using GAN-based synthetic data augmentation in training data

NR

NR

10 patients (over 86,000 data points)

Baseline accuracy improved by 4-6% post-GAN augmentation

T1D

NR

NR

China

Jiangsu Province Hospital (NR dataset)

Private

10 days per patient

Clinical

³⁰

Ramazi, 2021

Predicting progression patterns of type 2 diabetes using multi-sensor measurements

To predict future HbA1c, HDL, LDL, and triglyceride levels in T2D patients using wearable and static data

33–78 years

19 female, 31 male

54

CGM-derived RMSE metrics

T2D

NR

NR

USA

Christiana Care Health System (Delaware)

Private

1 year (7-day wear + 1-yr follow-up)

Real-world clinical

⁶⁵

Yin, 2017

A Health Decision Support System for Disease Diagnosis Based on Wearable Medical Sensors and Machine Learning Ensembles

To develop a hierarchical, closed-loop health decision support system (HDSS) combining wearable sensors and CDSSs to enable disease diagnosis and tracking

NR

NR

6 diseases analyzed (datasets ranged from 120–3163 records)

Included for T2D only

Mixed

NR

Multiple (e.g., T2D, arrhythmia, hypothyroid)

USA

Public UCI Machine Learning Repository, literature-based private datasets

Public

Cross-sectional

Real-world and simulated tiers

⁶⁶

Kim 2024

Impartial feature selection using multi-agent reinforcement learning for adverse glycemic event prediction

Develop a deep learning model for predicting adverse glycemic events in hospitalized T2D patients using CGM, EMR, and MARL-based feature selection

Range: 20–90 Years

39% F

102

CGM: 40-400 mg/dL

T2D

NR

NR

South Korea

Soonchunhyang University Cheonan Hospital, IEEE Dataport

Private; in vivo

CGM: 7-10 days per participant

EMR: April 2019 to January 2022

Real-world (hospital-based)

⁶⁷

Kashif 2024

GLSTM: On Using LSTM for Glucose Level Prediction

To predict glucose levels in individuals with prediabetes using a personalized Long Short-Term Memory (LSTM) model trained on multimodal wearable sensor data

35–65 years

100% F

11

Prediabetes

NR

NR

US

BIG IDEAs Lab dataset (PhysioNet)

Public; in vivo

8–10 days per participant

Real-world

⁶⁸

Kannenberg, 2024

Personalized Lifestyle Therapy for Type 2 Diabetes Through a Predictive Algorithm-Driven Digital Therapeutic

To evaluate the clinical impact of a CGM-AI-powered digital therapeutic on glycemic management and weight in non-insulin-treated T2D patients

31-78 years (mean 55.5)

58% female (n = 69), 38% male (n = 45)

118 participants

HbA1c: mean 7.46% (SD 0.93), range 6.5–11.0%; SDs for glucose/BMI included

T2D

NR

64% had comorbidities; HTN, dyslipidemia, mental & CV disorders

Germany

Direct-to-patient recruitment

Real-world

6 months (2–3-month phases)

Free-living

⁶⁹

Hotta 2024

Optimizing postprandial glucose prediction through integration of diet and exercise

To improve postprandial glucose prediction in GDM patients by integrating diet and exercise data using Bayesian transfer learning informed by randomized controlled trial data.

18–45 years

100% F

68

NR

Gestational diabetes

NR

The study included only GDM patients without major comorbidities

Finland

eMoM GDM trial & separate healthy-subject RCT data

Private; in vivo

GDM data: 3-day sessions collected monthly during pregnancy

Healthy controls: 6-day data collection per participant

Real-world

⁷⁰

Metwally, 2024

Predicting Type 2 Diabetes Metabolic Phenotypes Using Continuous Glucose Monitoring and a Machine Learning Framework

To identify distinct metabolic subphenotypes in individuals with normoglycemia or prediabetes using OGTT-derived CGM data and machine learning to inform precision prevention of T2D.

Mean ~55 years

50% F

56 total (32 training, 24 validation, 29 CGM)

Normoglycemia or prediabetes

Pre-diabetes and early T2D

74% Caucasian, 27% Asian

NR

USA

Stanford CTRU & at-home OGTT

In vivo human trial

Single 10-day CGM session

Clinical + At-home

⁷¹

Goncharov 2024

Insertable Glucose Sensor Using a Compact and Cost-Effective Phosphorescence Lifetime Imager and Multi-Parametric Data-Driven Calibration

Demonstrate feasibility of a low-cost, compact phosphorescence lifetime imager integrated into an insertable glucose sensor system

N/A

N/A

no human/animal participants. The glucose sensor was tested in phosphate-buffered saline (PBS) glucose solutions ranging from 0 to 30 mM.

N/A

N/A

N/A

N/A

Finland

Sensor and animal lab data

Private; in vitro

Short-term sensor evaluation (hours)

Controlled laboratory; experimental study

⁷²

Dénes-Fazakas 2024

Physical Activity Detection for Diabetes Mellitus Patients Using Recurrent Neural Networks

Detect physical activity in T1DM patients using CGM, heart rate, and step data with RNNs

Mean (SD): years 50 (10)

OhioT1DM: 5 (42%) F

12

Mean (SD): mg/dL 162 (20)

T1D

NR

NR

US

Ohio T1DM dataset

Public; in vivo

8 weeks per participant

Controlled

⁷³

Dave, 2024

Detection of Hypoglycemia and Hyperglycemia Using Noninvasive Wearable Sensors: Electrocardiograms and Accelerometry

To detect hypoglycemia and hyperglycemia using noninvasive ECG and accelerometer data as alternatives to CGMs

27-58 years, mean 42.6

3 men, 2 women

5 participants

Sensitivity & specificity: 76% for hypoglycemia, 79% for hyperglycemia with fusion model

Healthy

NR

NR

UK (data origin), USA (analysis)

Warwick University (UK), Texas A&M (USA)

Secondary analysis

14 days

Free-living

⁷⁴

Chowdhury, 2024

Multi-modal Approach to Estimate interstitial Glucose Using Multi-Stream and Cross-Modality Attention

To improve non-invasive interstitial glucose estimation by integrating CGM, ECG, and PPG using multi-stream transformer networks

NR

NR

12 subjects (randomly selected from 22)

RMSE (CGM only): 14.28; RMSE (multi-modal): 12.81

T2D

NR

NR

Singapore

Private dataset from NTU

Private

NR

Lab-based

⁷⁵

Chikwetu, 2024

Carbohydrate Content Classification Using Postprandial Heart Rate Responses from Non-Invasive Wearables

Classify carbohydrate meal content (low, medium, high) using heart rate response from wearables

Mean (SD): years 27.6 (4.2)

33% F

9

N/A

Healthy

NR

NR

US

Empatica E4 device, collected at Duke University

Private; in vivo

9 days per participant

Controlled meals in real-world setting

⁷⁶

Bonet, 2024

Smart Algorithms for Efficient Insulin Therapy Initiation in Individuals With Type 2 Diabetes: An in Silico Study

To develop and evaluate new AI-driven algorithms for faster and safer basal insulin titration in insulin-naive T2D individuals

51 years (median, range 37–65)

102 women out of 300 virtual subjects

300 in silico subjects

TIR, TAR, TBR1 ( < 70 mg/dL), TBR2 ( < 54 mg/dL), OID (U), insulin dosing frequency

T2D

NR

NR

Italy

Padova T2D simulator

Synthetic

12 months

Simulated

⁷⁷

Beolet 2024

End-to-end offline reinforcement learning for glycemia control

Develop personalized offline RL models for basal insulin control using real-world CGM data from a commercial closed-loop system

Mean (SD): years 44.8 (13.0)

150

Mean (SD)

Glucose: 160.8 mg/dL; TIR: 68.4%; TBR: 1.3%; TAR: 30.3%; CV: 33%

Weight: 77.1 (17.5)

T1D

France

Real-life usage data from Diabeloop DBLG1 closed-loop system

Private; in vivo

9 months per patient on average

⁷⁸

Allam, 2024

Using nonlinear auto-regressive with exogenous input neural network in interstitial glucose prediction

To improve long-horizon interstitial glucose prediction using a modified RNN architecture.

3–18 years

0% F

9 patients; 4916 samples total

Range: 3.5-15.5 mmol/L; RMSEs range from 0.74 to 1.34 mmol/L; FIT from 75.2% to 84.7%

T1D

NR

NR

NR (Data from DirecNet, likely USA)

Diabetes Research in Children Network (DirecNet)

Public

~2 days per patient

Real-world

⁷⁹

Zanelli, 2023

Type 2 Diabetes Detection With Light CNN From Single Raw PPG Wave

To detect type 2 diabetes using a lightweight CNN applied to raw PPG pulses, with and without transfer learning.

50–70 years (middle-aged subjects)

Reported as input but specific distribution NR

100 subjects in DB_DT2 (15% with T2D); additional shape and HT datasets for pretraining

NR (diabetes based on clinical records, not glucose values)

T2D

NR

Excluded HT and other overlapping conditions

France (University Hospital of Nice)

DB_DT2 dataset; also DB_shape and DB_HT; collected using pOpm©tre device

Mixed

NR (single pulse acquisition from PPG; sampling rate = 1 kHz)

Controlled setting (non-clinical + clinical PPG acquisition)

⁸⁰

Yang, 2023

Glucose trend prediction model based on improved wavelet transform and gated recurrent unit

To improve CGM-based glucose trend prediction using an enhanced wavelet denoising algorithm combined with GRU.

56.1 ± 8.3 years

54 male / 26 female

80 patients

NR (but includes performance metrics: RMSE = 0.5537 mmol/L, MAPE = 2.21%, R² = 0.989)

T2D

NR

NR

China

Silicon-based CGM device (not named); 5-min interval data

Private

7 consecutive days per patient

Real-world

⁸¹

Tao, 2023

A deep learning nomogram of continuous glucose monitoring data for the risk prediction of diabetic retinopathy in type 2 diabetes

To predict the risk of diabetic retinopathy (DR) in T2D patients using a deep learning nomogram based on CGM data.

Mean age ~58.5 years across cohorts

~38% female

788 patients (Training: 494, Testing: 294)

TIR, SDBG, GRADE, CV, MBG, MAGE, LI, ADRR, etc.

T2D

NR

Excluded patients with malignancy, mental illness, or acute conditions

China

Shanghai Jiao Tong University Affiliated Sixth Peoples Hospital (iPro2 CGM device)

Private

3 days of CGM per patient

Hospital

⁸²

Site, 2023

Machine-learning-based diabetes prediction using multi-sensor data

To compare diabetes prediction performance using single vs. multi-sensor combinations from wearable data (glucose, ECG, accelerometer, breathing).

NR

NR

29 participants (20 non-diabetic, 9 diabetic)

NR (diabetes diagnosis status used, not glucose levels directly)

Type 2 Diabetes (T2D) and healthy controls

NR

NR

Finland (Tampere University)

D1NAMO dataset (Zephyr BioHarness 3 device)

Private

4 days of continuous data per subject

Free-living

⁸³

Nazha, 2023

Portable Infrared-Based Glucometer Reinforced with Fuzzy Logic

To develop a non-invasive portable glucometer that estimates glucose using infrared sensors and fuzzy logic, incorporating both finger and tear measurements.

16 to 65 years (as shown in participant table)

NR (Sex shown for each participant in table, no summary reported)

30 participants (15 with diabetes, 15 healthy controls)

Estimated glucose via voltage correlation; reported range 96-178 mg/dL; Error <3%

Mixed

NR

NR

Syria (Tartous University) and Germany (Otto Von Guericke University)

Study-specific device; not based on existing dataset

Private

Single-visit, with 12 measurements per participant pre- and post-meal

Free-living

⁸⁴

Lin, 2023

Prediction of interstitial Glucose Concentration Based on OptiScanner and XGBoost in ICU

To improve ICU interstitial glucose prediction using mid-IR spectral data collected from OptiScanner and XGBoost, accounting for hetastarch treatment effects.

NR

NR

1,021 ICU patients (Training: 633, Test: 388)

Reference glucose measured using YSI STAT 2300; RMSE used as evaluation metric

Unspecified

NR

Includes subgroup treated with hetastarch for hypovolemia

Taiwan

OptiScanner mid-IR spectrometry (data not publicly available)

Private

NR (bedside OptiScanner data acquisition)

Hospital

⁸⁵

Lee, 2023

An Integrated Digital Health Care Platform for Diabetes Management With AI-Based Dietary Management

To evaluate whether an integrated AI-based dietary management platform improves glycemic management and weight in T2D patients

Mean 56.1 years

66.3% male (181/273)

294

HbA1c (change from baseline)

T2D

Korean

Not specified

South Korea

3 university hospitals in Seoul

RCT

48 weeks

Outpatient

⁸⁶

Lee, 2023

Glucose Transformer: Forecasting Glucose Level and Events of Hyperglycemia and Hypoglycemia

To develop a deep learning model using the Transformer encoder to predict glucose levels and classify events of hypoglycemia/hyperglycemia in hospitalized T2D patients.

T1D dataset: mean age 44 years; T2D: age 20-90 years

NR

104 T2D patients; 226 T1D patients for transfer learning

T2D group: higher distribution and larger SD than T1D; glucose values ranged from 40 to 400 mg/dL

Type 2 Diabetes (T2D); transfer learning data from T1D patients

NR

NR

South Korea

Soonchunhyang University Cheonan Hospital (T2D); OhioT1DM dataset (T1D)

Public

3 to 7 days per patient

Hospital

⁸⁷

Kistkins, 2023

Comparative Analysis of Predictive Interstitial Glucose Level Classification Models

Compare the efficacy of ARIMA, logistic regression, and LSTM models in classifying glucose states (hypo-, eu-, hyperglycemia) 15 min and 1 h in advance.

Mean age: 47 ± 11 years

6 female, 5 male

11 T1D patients (real data); 30 virtual patients (in silico: 10 adults, 10 adolescents, 10 children)

Mean HbA1c: 57 ± 8 mmol/mol; variability discussed, not numerically detailed

T1D

NR

NR

Austria

COVAC-DM clinical study; UVA/Padova T1D Simulator (via simglucose v0.2.1)

Real-world

Two CGM phases per participant pre- and post-vaccination; virtual: 10 days

Clinical cohort and simulation study

⁸⁸

Arbi, 2023

interstitial glucose estimation based on ECG signal

To develop a method to estimate interstitial glucose concentration (BGC) using ECG signal features instead of invasive CGMs.

NR

NR

3 T1D patients from D1NAMO dataset

NR (BGC range evaluated per patient, model R² up to 98%)

T1D

NR

NR

Algeria

D1NAMO dataset (real-world, open access); PhysioNet QT dataset (for training ECG segmentation)

Public

8 days of ECG and BGC measurements per patient

Free-living

²³

Avram, 2020

A digital biomarker of diabetes from smartphone-based vascular signals

To detect prevalent diabetes using smartphone-based photoplethysmography and deep learning

Mean age ~45–55 yrs

~59% male with diabetes in primary cohort

53,870 (primary cohort)

HbA1c linear assoc: β=2.28 per SD DNN score; DNN AUC: 0.766 (primary), 0.740 (contemp), 0.682 (clinic)

T2D

NH White, Black, Asian, Hispanic, Multi-ethnic

Hypertension, hypercholesterolemia, CAD, CHF, PVD, stroke, sleep apnea

USA (and Canada for clinic)

Health eHeart + 3 clinical sites

Mixed

2014-2019 (PPG + HbA1c matched within 180 days)

Remote

⁸⁹

Andellini, 2023

Artificial intelligence for non-invasive glycaemic-events detection via ECG in a pediatric population

To validate an AI-based algorithm for detecting glycaemic events (hypo/hyperglycemia) using ECG signals in T1D children

<18 years

NR

64 pediatric T1D patients

HbA1c, mean glucose, glycaemic variability, time in range, event frequency

T1D

NR

Excluded: Celiac disease, cardiovascular disease, arrhythmias, pregnancy

Italy

Bambino Ges¹ Childrens Hospital

Observational

3 days per participant

Real-world

⁹⁰

Alvarado, 2023

Combining wavelet transform with convolutional neural networks for hypoglycemia events prediction from CGM data

To detect and predict hypoglycemia events up to 24 hours in advance using deep learning and wavelet transforms

40.16 ± 11.86 years

50% female

20 T1D participants

Time in range, % in hypo/hyper ranges, glycemic variability, interquartile range plots

T1D

NR

NR

Spain

Hospital Prncipe de Asturias, Alcal¡ de Henares

Real-world

~15 days/patient

Clinical trial

⁹¹

Ahmed, 2023

Performance of artificial intelligence models in estimating interstitial glucose level among diabetic patients using non-invasive wearable device data

To evaluate AI model performance in estimating interstitial glucose levels using non-invasive wearable device data

9–77 years

5 females, 8 males

13

RMSE: 0.099-0.197, MAE: 0.097-0.112

Mixed

NR

NR

Qatar

Open-source dataset (Riversong Smart Band, Freestyle LibrePro CGM)

Public

3 months/patient (June 2020-Dec 2021)

Wearable study

⁹²

Zale, 2022

Machine Learning Models for Inpatient Glucose Prediction

Review clinical evidence on machine learning models for predicting glucose trends in hospitalized patients and assess their predictive performance and clinical applicability.

NR

NR

Ranged from N = 20 to >100,000 across reviewed models; Zale 2022 model used data from 5 hospitals totaling over 100,000 patients

Outcomes included hypoglycemia (<70 mg/dL, <54 mg/dL), hyperglycemia (>180 mg/dL), and glucose trajectory; CGM studies reported mean squared errors (e.g., RMSE = 21.5 mg/dL)

Mixed

NR

CKD (severe), use of sulfonylureas or insulin, Charlson Comorbidity Index considered in some models

USA, multiple hospitals including Johns Hopkins; international studies also cited

EHRs from multiple hospitals (including Johns Hopkins), CGM data, MIMIC-III ICU database

Real-world

NR for most models; prediction horizons discussed rather than total study periods

Hospital

⁹³

Xiong, 2022

Identifying daily activities of patient work for type 2 diabetes and co-morbidities: a deep learning and wearable camera approach

To classify self-management activities of people with type 2 diabetes and comorbidities using wearable camera data and deep learning.

Median age: 72 years

10 females, 16 males

26 participants

NR

Type 2 Diabetes with comorbidities

NR

Yes; multiple chronic conditions

Australia

Wearable camera data from Macquarie University study

Private

1 day of wearable camera footage ( ~ 16 hours per participant)

Free-living

⁹⁴

Nemat, 2022

interstitial Glucose Level Prediction: Advanced Deep-Ensemble Learning Approach

To improve interstitial glucose prediction in Type 1 diabetes using deep-ensemble learning models and novel meta-learning techniques

20-80 years

5 females, 7 males

12 participants

RMSE, MAE, MCC, Surveillance Error

T1D

NR

NR

UK

Ohio T1DM dataset

Public

8 weeks per participant

Real-world

⁹⁵

Malerbi, 2022

Diabetic Retinopathy Screening Using Artificial Intelligence and Handheld Smartphone-Based Retinal Camera

To evaluate the diagnostic accuracy of a DL algorithm and a handheld retinal camera in detecting diabetic retinopathy in a real-world, underserved setting.

Mean age 60.8 ± 11.4 years

64.9% female

824 enrolled; 679 with gradable images

NR

T2D

NR

Hypertension (68.4%), smoking (48.4%)

Brazil

Itabuna Diabetes Campaign, Bahia State, Brazil

Private

Single-day campaign (Nov 23, 2019)

Other

⁹⁶

Lobo, 2022

A Data-Driven Approach to Classifying Daily Continuous Glucose Monitoring (CGM) Time Series

To identify a finite set of representative daily CGM profiles (motifs) for use in classification, modeling, and automated systems.

NR

NR

9741 (training), 14,175 (validation), 42,595 (testing); total = 66,511 profiles from 491 patients

Mean BG, SD, CV, TIR, TAR, TBR, LBGI, HBGI

Mixed (T1D + T2D)

NR

NR

USA

DCLP1, DCLP3, DIA1, DIA2, DSS1, NTLT

Private

3 to 6 months per study

Clinical trial

⁹⁷

Lim, 2022

Multi-Task Disentangled Autoencoder for Time-Series Data in Glucose Dynamics

To develop a multi-task disentangled VAE (MD-VAE) for glucose forecasting, event detection, and temporal clustering

NR

NR

T1DMS: 20 virtual patients (20 days); DCLP3: 112 real patients (6 months CGM)

MAE: 12.02-20.02 mg/dL; RMSE: 16.56-28.84 mg/dL; MARD: 8.01-13.25%; TIR, TAR, TBR, HBGI, LBGI, CV, IPI

T1D

NR

NR

South Korea

T1DMS simulator (FDA-approved), DCLP3 real-world clinical trial (Control-IQ, Dexcom G6)

Real-world

20 days (sim); 6 months (real patients)

In silico

²⁹

Kim, 2022

Identification of Healthy and Unhealthy Lifestyles by a Wearable Activity Tracker in Type 2 Diabetes

To classify healthy vs. unhealthy lifestyle patterns in T2D using data from wearable activity trackers and unsupervised clustering

29–60 years

58.3% male

24 patients

ÎHbA1c after 3 and 9 months; 0.33% lower HbA1c in healthy group at 9 months (not statistically significant)

T2D

NR

NR

South Korea

Fitbit Charge 2 tracker, Korea University Anam Hospital

Prospective observational study

9 months total

Outpatient

⁹⁸

Aloraynan, 2022

A Single Wavelength Mid-Infrared Photoacoustic Spectroscopy for Noninvasive Glucose Detection Using Machine Learning

To develop a noninvasive glucose detection system using single-wavelength MIR photoacoustic spectroscopy with ML classification

NR

NR

In vitro only - 3 samples per class across 10 classes; 40,200 measurements total

Detection resolution of ±25 mg/dL; Clarkes EGA: 96.1% zone A, 3.9% zone B (after preprocessing)

Non-human

NR

NR

Canada

Artificial gelatin-based skin phantoms prepared with glucose concentrations from 75-300 mg/dL

Synthetic

3 days of repeated measurements

Lab-based

²⁴

Yin, 2021

DiabDeep: Pervasive Diabetes Diagnosis Based on Wearable Medical Sensors and Efficient Neural Networks

Develop an efficient, wearable-sensor-based diabetes diagnosis system for edge and server environments

NR

NR

52 participants (14 T1D, 13 T2D, 25 healthy)

NR

Mixed

NR

None reported

USA

Empatica E4 + Samsung S4 smartphone

Real-world

1–1.5 hours/participant

Real-world daily use

⁹⁹

Garcia-Tirado, 2021

Advanced hybrid artificial pancreas system improves on unannounced meal response - In silico comparison to currently available system

To evaluate a fully automated, adaptive hybrid artificial pancreas (RocketAP) with a novel bolus priming system for managing unannounced meals in type 1 diabetes using in silico simulations

Simulated adult cohort

Simulated cohort, not reported

100 virtual adults (UVA/Padova simulator)

TIR, TTR, %time <70 mg/dL, %time >180 mg/dL, LBGI, HBGI, SD-glucose, %CV-glucose, Mean BG; model RMSE (identification: 7.67, validation: 7.73 mg/dL)

T1D

NR

NR

USA

UVA/Padova Type 1 Diabetes Simulator

Synthetic

14 days of training data + test simulations

In silico

²⁵

Deng, 2021

Deep transfer learning and data augmentation improve glucose levels prediction in type 2 diabetes patients

To improve short-term prediction of glucose levels in T2D using transfer learning and data augmentation on imbalanced datasets

65.1 ± 8.8 years

52.5% female

40 patients with T2D

BG range 40-400 mg/dL, mean 130.6 mg/dL; hypoglycemia <80, hyperglycemia >180 mg/dL

T2D

NR

NR

USA

Beth Israel Deaconess Medical Center (BIDMC)

Private

Up to ~170 hours per participant

Outpatient

¹⁰⁰

Bent, 2021

Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches

To develop digital biomarkers from noninvasive wearable data and food logs to predict personalized interstitial glucose excursions and real-time glucose values.

35–65 years

NR

16 participants

Mean PersNorm=112.4 mg/dL, PersHigh=149.9, PersLow=90.8; SDs reported

Prediabetes

NR

Excluded: cancer, COPD, CVD, food allergies, antidiabetic meds

USA

Duke University; study-specific collection

Private

8–10 days per participant

Outpatient

¹⁰¹

Baig, 2021

Early Detection of Prediabetes and T2D Using Wearable Sensors and IoT-Based Monitoring Applications

To develop and evaluate an AI-powered early detection system for prediabetes and T2D using wearable technology and IoT-based monitoring

55–62 years

1 male, 1 female in testing; unclear for full training set

36 participants (training), 2 participants (real-time testing)

HbA1c (avg): 87.5 vs 64 mmol/mol; HR, ventilation, cadence, activity ranges; 91% accuracy; sensitivity 94%; specificity 90%; predictability 72%; Kappa = 0.75

T2D, Prediabetes

NR

NR

New Zealand

Hexoskin vest; PhysioNet; manual participant data

Mixed

2 years (follow-up); 10 months data collection

Real-world

²⁷

Zhu, 2020

An Insulin Bolus Advisor for Type 1 Diabetes Using Deep Reinforcement Learning

To optimize mealtime insulin dosing in T1D using deep reinforcement learning bolus advisor with CGM data

NR

NR

20 virtual subjects (10 adults, 10 adolescents)

TIR, TBR, TAR, mean BG, CV, LBGI, HBGI, CVGA

T1D

NR

NR

UK

UVA/Padova T1D simulator

Simulated

6-month training, 3-month testing

In silico

¹⁰²

Xie, 2020

Benchmarking Machine Learning Algorithms on interstitial Glucose Prediction for Type I Diabetes

To compare ML algorithms and classical ARX models for BG prediction in T1D using the same dataset

40–60 years

4 females, 2 males

6 T1D patients

RMSE, Temporal Gain (TG), Normalized ESODn

T1D

NR

NR

USA

OhioT1DM dataset

Public

8 weeks

Real-world

²⁶

He, 2020

interstitial glucose concentration prediction based on kernel canonical correlation analysis with particle swarm optimization and error compensation

To improve short-term interstitial glucose prediction using nonlinear modeling and personalized error compensation

NR

NR

10 patients

RMSE at 5–30 min: 8.01 to 16.40 mg/dL; R² range: 0.95 to 0.98; EC-CCA reduced RMSE by 33.45% over CCA

T1D

NR

NR

China

DirecNet dataset (USA origin)

Public

~4 days per subject

Clinical (pediatric)

²⁸

Sun, 2019

A Dual Mode Adaptive Basal-Bolus Advisor Based on Reinforcement Learning

To develop a personalized, dual-mode basal-bolus insulin advisor (ABBA) using reinforcement learning, adaptable to CGM or SMBG input

NR

NR

100 virtual adults (simulated)

% time in range, % hypo/hyper, LBGI, HBGI, MAGE, total daily insulin dose

T1D

NR

NR

Switzerland

UVA/Padova FDA-accepted T1DM simulator

Synthetic

3 months + 1 week (98 days)

In silico

¹⁰⁴

Samadi, 2018

Automatic Detection and Estimation of Unannounced Meals for Multivariable Artificial Pancreas System

To develop a fuzzy-logic-based system to automatically detect unannounced meals and estimate CARB intake in T1D patients

18–35 years

NR

11 subjects

Time to detection, detection rate, estimation error, false positives, insulin dose change

T1D

NR

NR

USA

University of Chicago clinical trial data

Real-world

~60 hours per subject

In-clinic (closed loop)

¹⁰⁵

Zecchin, 2015

Jump Neural Network for Real-Time Prediction of Glucose Concentration

To predict short-term (30 min) future glucose levels in T1D patients using a jump neural network incorporating CGM and meal information

NR

NR

20 T1D individuals (10 for training, 10 for testing)

T1D

T1D

NR

NR

Italy

DIAdvisor„¢ project CGM dataset (Dexcom SEVEN PLUS)

Public

2–3 days per subject

Free-living

¹⁰⁶

Zecchin, 2014

Jump neural network for online short-time prediction of interstitial glucose…

To evaluate a novel jump neural network architecture for short-term glucose forecasting after meals

NR

NR

11 T1D subjects (37 meal tests)

Reported mean RMSE across horizons: ~20-30 mg/dL

T1D

NR

NR

Italy

Padova Hospital, Italy

Private

3-day sessions post meal

Clinical