First Generation of a Modular Interoperable Closed-Loop System for Automated Insulin Delivery in Patients With Type 1 Diabetes: Lessons From Trials and Real-Life Data

Benhamou, Pierre Yves; Adenis, Alice; Lablanche, Sandrine; Franc, Sylvia; Amadou, Coralie; Penfornis, A.; Kariyawasam, Dulanjalee; Beltrand, Jacques; Charpentier, G.

doi:10.1177/19322968231186976

“…Taking into account the need to fulfill these four principles, our group regards the proof of methodological development in the fulfillment of system interoperability, which is not always addressed by the majority of deep learning models published in Gastroenterology. Thus, the interoperability challenge is a discussion topic in multiple science fields [ 34 , 35 , 36 ]. Our group has addressed that concern, developing a CNN that works in three different enteroscopes, comprising single-balloon, double-balloon and motorized spiral enteroscopy devices, solving a fundamental interoperability challenge for applying the technology.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence and Panendoscopy—Automatic Detection of Clinically Relevant Lesions in Multibrand Device-Assisted Enteroscopy

Mendes,

Mascarenhas,

Ribeiro

et al. 2024

Cancers

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Device-assisted enteroscopy (DAE) is capable of evaluating the entire gastrointestinal tract, identifying multiple lesions. Nevertheless, DAE’s diagnostic yield is suboptimal. Convolutional neural networks (CNN) are multi-layer architecture artificial intelligence models suitable for image analysis, but there is a lack of studies about their application in DAE. Our group aimed to develop a multidevice CNN for panendoscopic detection of clinically relevant lesions during DAE. In total, 338 exams performed in two specialized centers were retrospectively evaluated, with 152 single-balloon enteroscopies (Fujifilm®, Porto, Portugal), 172 double-balloon enteroscopies (Olympus®, Porto, Portugal) and 14 motorized spiral enteroscopies (Olympus®, Porto, Portugal); then, 40,655 images were divided in a training dataset (90% of the images, n = 36,599) and testing dataset (10% of the images, n = 4066) used to evaluate the model. The CNN’s output was compared to an expert consensus classification. The model was evaluated by its sensitivity, specificity, positive (PPV) and negative predictive values (NPV), accuracy and area under the precision recall curve (AUC-PR). The CNN had an 88.9% sensitivity, 98.9% specificity, 95.8% PPV, 97.1% NPV, 96.8% accuracy and an AUC-PR of 0.97. Our group developed the first multidevice CNN for panendoscopic detection of clinically relevant lesions during DAE. The development of accurate deep learning models is of utmost importance for increasing the diagnostic yield of DAE-based panendoscopy.

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7. Diabetes Technology: Standards of Care in Diabetes—2024

ElSayed,

Aleppo,

Bannuru

et al. 2023

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The American Diabetes Association (ADA) “Standards of Care in Diabetes” includes the ADA’s current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA’s clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

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7. Diabetes Technology: Standards of Care in Diabetes—2025

ElSayed,

McCoy,

Aleppo

et al. 2024

Diabetes Care

0

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The American Diabetes Association (ADA) “Standards of Care in Diabetes” includes the ADA’s current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA’s clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

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First Generation of a Modular Interoperable Closed-Loop System for Automated Insulin Delivery in Patients With Type 1 Diabetes: Lessons From Trials and Real-Life Data

Cited by 3 publications

References 32 publications

Artificial Intelligence and Panendoscopy—Automatic Detection of Clinically Relevant Lesions in Multibrand Device-Assisted Enteroscopy

Artificial Intelligence and Panendoscopy—Automatic Detection of Clinically Relevant Lesions in Multibrand Device-Assisted Enteroscopy

7. Diabetes Technology: Standards of Care in Diabetes—2024

7. Diabetes Technology: Standards of Care in Diabetes—2025

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