Neural Network-Based Prediction and Monitoring of Blood Glucose Response to Nutritional Factors in Type-1 Diabetes

Angrisani, L.; Annuzzi, Giovanni; Arpaïa, Pasquale; Bozzetto, Lutgarda; Cataldo, Andrea; Corrado, Alessandra; Benedetto, Egidio De; Capua, Vincenzo Di; Prevete, Roberto; Vallefuoco, Ersilia

doi:10.1109/i2mtc48687.2022.9806611

Cited by 7 publications

(7 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the literature, several ML-based strategies, such as deep neural networks (DNNs), have demonstrated potential in BGL prediction and early detection of hypoand hyperglycemic events, leading to improved preprandial insulin administration. In particular, numerous studies have investigated BGL prediction using different neural network models, including Feed Forward Neural Network (FFNN) [10], [22]- [24], Long Short Term Memory (LSTM) [25]- [30], and Convolutional Neural Network (CNN) [31], [32]. Although these ML models achieve satisfactory performance in predicting BGLs (see Table I), their lack of interpretability remains a significant issue [33].…”

Section: Related Workmentioning

confidence: 99%

“…This study was conducted on the AI4PG dataset, provided by the Diabetes Outpatient Clinic of Federico II University Hospital in Naples, Italy [25]. The utilization of this dataset in the present study received the necessary ethical approval from the Ethical Committee of University of Naples Federico II (Registration number 338/20).…”

Section: A Dataset Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Exploring Nutritional Influence on Blood Glucose Forecasting for Type 1 Diabetes Using Explainable AI

Annuzzi,

Apicella,

Arpaia

et al. 2024

IEEE J. Biomed. Health Inform.

Self Cite

View full text Add to dashboard Cite

Type 1 diabetes mellitus (T1DM) is characterized by insulin deficiency and blood sugar control issues. The state-of-the-art solution is the artificial pancreas (AP), which integrates basal insulin delivery and glucose monitoring. However, APs are unable to manage postprandial glucose response (PGR) due to limited knowledge of its determinants, requiring additional information for accurate bolus delivery, such as estimated carbohydrate intake. This study aims to quantify the influence of various mealrelated factors on predicting postprandial blood glucose levels (BGLs) at different time intervals (15 min, 60 min, and 120 min) after meals by using deep neural network (DNN) models. The prediction models incorporate preprandial blood glucose values, insulin dosage, and various mealrelated nutritional factors such as intake of energy, carbohydrates, proteins, lipids, fatty acids, fibers, glycemic index, and glycemic load as input variables. The impact of input features was assessed by exploiting eXplainable Artificial Intelligence (XAI) methodologies, specifically SHapley Additive exPlanations (SHAP), which provide insights into each feature's contribution to the model predictions. By leveraging XAI methodologies, this study aims to enhance the interpretability and transparency of BGL prediction models and validate clinical literature hypotheses. The findings can aid in the development of decision-support tools for individuals with T1DM, facilitating PGR management and reducing the risks of adverse events. The improved understanding of PGR determinants may lead to advancements in AP technology and improve the overall quality of life for T1DM patients.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: A Dataset Descriptionmentioning

confidence: 99%

Exploring Nutritional Influence on Blood Glucose Forecasting for Type 1 Diabetes Using Explainable AI

Annuzzi,

Apicella,

Arpaia

et al. 2024

IEEE J. Biomed. Health Inform.

Self Cite

View full text Add to dashboard Cite

show abstract

“…• Predicción de patrones de glucemia: Uno de los aspectos más interesantes de la aplicación de la inteligencia artificial en la atención de los pacientes con diabetes es la capacidad de predecir los patrones de glucemia. Las técnicas de aprendizaje automático están siendo utilizadas para predecir la glucemia y recomendar dosis de insulina, con una precisión razonable [168].…”

Section: Obstáculos a Superar En La Gestión De La Diabetes Basada En ...unclassified

El Internet de las Cosas Médicas (IoMT): Una Revolución Tecnológica aplicable a la Gestión de la Diabetes Mellitus Tipo 1

Rodríguez‐Rodríguez¹,

Rodríguez²,

Campo-Valera³

2023

View full text Add to dashboard Cite

La diabetes mellitus de tipo 1 (DMT1) es una afección metabólica caracterizada por una hiperglucemia persistente como consecuencia de una síntesis pancreática insuficiente de insulina. Esto obliga a los pacientes a ser conscientes de sus oscilaciones diarias del nivel de glucosa en sangre para deducir un patrón y anticipar la glucemia futura y, por tanto, decidir la cantidad de insulina que debe inyectarse exógenamente para mantener la glucemia dentro del intervalo objetivo. Este enfoque suele adolecer de una imprecisión relativamente alta, que puede resultar peligrosa. Sin embargo, los recientes avances en las tecnologías de la información y la comunicación (TIC) y los innovadores biosensores que podrían permitir una evaluación completa y en tiempo real de la salud del paciente ofrecen un nuevo punto de vista sobre el tratamiento de la DMT1. En este sentido, las tecnologías disruptivas emergentes como Big Data, Internet de las Cosas Médicas (IoMT), Cloud Computing y Machine Learning (ML) pueden desempeñar un papel importante en la gestión de la DMT1. En este trabajo, se realiza una explicación de los enfoques basados en IoMT publicados anteriormente para la gestión de la diabetes, evaluando también los obstáculos que los futuros sistemas inteligentes de gestión IoMT deben superar. Por último, proporcionamos una visión general de una propuesta integral basada en IoMT para la gestión de la DM1 que pretende abordar los límites de los estudios anteriores y, al mismo tiempo, utilizar las tecnologías disruptivas destacadas anteriormente.

show abstract

“…Artificial intelligence (AI) is increasingly being used in clinical practice and translational medical research. Various AI systems have been employed to improve the diagnosis and treatment of diseases such as diabetes [1] and cancer [2]. In surgical pathology, AI technologies have been facilitated by the ability to digitize slides.…”

Section: Introductionmentioning

confidence: 99%

Development of an Artificial-Intelligence-Based Tool for Automated Assessment of Cellularity in Bone Marrow Biopsies in Ph-Negative Myeloproliferative Neoplasms

D’Abbronzo,

D’Antonio,

De Chiara

et al. 2024

Cancers

View full text Add to dashboard Cite

The cellularity assessment in bone marrow biopsies (BMBs) for the diagnosis of Philadelphia chromosome (Ph)-negative myeloproliferative neoplasms (MPNs) is a key diagnostic feature and is usually performed by the human eyes through an optical microscope with consequent inter-observer and intra-observer variability. Thus, the use of an automated tool may reduce variability, improving the uniformity of the evaluation. The aim of this work is to develop an accurate AI-based tool for the automated quantification of cellularity in BMB histology. A total of 55 BMB histological slides, diagnosed as Ph- MPN between January 2018 and June 2023 from the archives of the Pathology Unit of University “Luigi Vanvitelli” in Naples (Italy), were scanned on Ventana DP200 or Epredia P1000 and exported as whole-slide images (WSIs). Fifteen BMBs were randomly selected to obtain a training set of AI-based tools. An expert pathologist and a trained resident performed annotations of hematopoietic tissue and adipose tissue, and annotations were exported as .tiff images and .png labels with two colors (black for hematopoietic tissue and yellow for adipose tissue). Subsequently, we developed a semantic segmentation model for hematopoietic tissue and adipose tissue. The remaining 40 BMBs were used for model verification. The performance of our model was compared with an evaluation of the cellularity of five expert hematopathologists and three trainees; we obtained an optimal concordance between our model and the expert pathologists’ evaluation, with poorer concordance for trainees. There were no significant differences in cellularity assessments between two different scanners.

show abstract

Neural Network-Based Prediction and Monitoring of Blood Glucose Response to Nutritional Factors in Type-1 Diabetes

Cited by 7 publications

References 19 publications

Exploring Nutritional Influence on Blood Glucose Forecasting for Type 1 Diabetes Using Explainable AI

Exploring Nutritional Influence on Blood Glucose Forecasting for Type 1 Diabetes Using Explainable AI

El Internet de las Cosas Médicas (IoMT): Una Revolución Tecnológica aplicable a la Gestión de la Diabetes Mellitus Tipo 1

Development of an Artificial-Intelligence-Based Tool for Automated Assessment of Cellularity in Bone Marrow Biopsies in Ph-Negative Myeloproliferative Neoplasms

Contact Info

Product

Resources

About