Use of Artificial Intelligence to Improve Diabetes Outcomes in Patients Using Multiple Daily Injections Therapy

Forlenza, Gregory P.

doi:10.1089/dia.2019.0077

Cited by 21 publications

(7 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Indeed, AI/ML-based technologies have been shown to support several medical specialties from radiology 2 and oncology 3 to ophthalmology 4 and general medical decision-making 5 . ML models have been shown to reduce waiting times 6 ; improve medication adherence 7 ; customize insulin dosages 8 ; or help interpret magnetic resonance images 9 , among others.…”

Section: Introductionmentioning

confidence: 99%

The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database

Dhunnoo²,

2020

View full text Add to dashboard Cite

At the beginning of the artificial intelligence (AI)/machine learning (ML) era, the expectations are high, and experts foresee that AI/ML shows potential for diagnosing, managing and treating a wide variety of medical conditions. However, the obstacles for implementation of AI/ML in daily clinical practice are numerous, especially regarding the regulation of these technologies. Therefore, we provide an insight into the currently available AI/ML-based medical devices and algorithms that have been approved by the US Food & Drugs Administration (FDA). We aimed to raise awareness of the importance of regulatory bodies, clearly stating whether a medical device is AI/ML based or not. Cross-checking and validating all approvals, we identified 64 AI/ML based, FDA approved medical devices and algorithms. Out of those, only 29 (45%) mentioned any AI/ML-related expressions in the official FDA announcement. The majority (85.9%) was approved by the FDA with a 510(k) clearance, while 8 (12.5%) received de novo pathway clearance and one (1.6%) premarket approval (PMA) clearance. Most of these technologies, notably 30 (46.9%), 16 (25.0%), and 10 (15.6%) were developed for the fields of Radiology, Cardiology and Internal Medicine/General Practice respectively. We have launched the first comprehensive and open access database of strictly AI/ML-based medical technologies that have been approved by the FDA. The database will be constantly updated.

show abstract

Section: Introductionmentioning

confidence: 99%

The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database

Dhunnoo²,

2020

View full text Add to dashboard Cite

show abstract

“…On the other hand, some studies have previously reported that diabetes distress may be a barrier to the use of diabetes technology [39]. In particular, individuals perceiving more barriers to device use also report more diabetes distress and lower rates of device use [13,40].…”

Section: Discussionmentioning

confidence: 99%

Assessing attitudes towards insulin pump therapy in adults with type 1 diabetes: Italian validation of the Insulin Pump Attitudes Questionnaire (IT-IPA questionnaire)

et al. 2023

View full text Add to dashboard Cite

Aims The aim of the study was to adapt the German version of the insulin pump therapy (IPA) questionnaire to Italian (IT-IPA) and to evaluate its psychometric properties in adults with type 1 diabetes. Methods We conducted a cross-sectional study, data were collected through an online survey. In addition to IT-IPA, questionnaires evaluating depression, anxiety, diabetes distress, self-efficacy, and treatment satisfaction were administered. The six factors identified in the IPA German version were assessed using confirmatory factor analysis; psychometric testing included construct validity and internal consistency. Results The online survey was compiled by 182 individuals with type 1 diabetes: 45.6% continuous subcutaneous insulin infusion (CSII) users and 54.4% multiple daily insulin injection users. The six-factor model had a very good fit in our sample. The internal consistency was acceptable (Cronbach’s α = 0.75; 95% IC [0.65–0.81]). Diabetes treatment satisfaction was positively correlated with a positive attitude towards CSII therapy (Spearman’s rho = 0.31; p < 0.01), less Technology Dependency, higher Ease of Use, and less Impaired Body Image. Furthermore, less Technology Dependency was associated with lower diabetes distress and depressive symptoms. Conclusions The IT-IPA is a valid and reliable questionnaire evaluating attitudes towards insulin pump therapy. The questionnaire can be used for clinical practice during consultations for shared decision-making to CSII therapy.

show abstract

“…Overall, the reasons for the limited clinical research in this field are unclear, but may be related to the primary focus of research being on closed‐loop systems, the limited use of clinical diabetes apps in the real‐world setting, a challenging regulatory environment for approval and the lack of connected insulin pens to fully support insulin inputs into an automated system 50 . We propose that, with the growing use of HCL systems, there is an ever‐increasing need for adaptive bolus calculators to support a fully automated closed loop system.…”

Section: Discussionmentioning

confidence: 99%

Adaptive bolus calculators for people with type 1 diabetes: A systematic review

Unsworth,

Avari,

Lett

et al. 2023

Diabetes Obesity Metabolism

View full text Add to dashboard Cite

AimTo conduct a systematic review of studies assessing adaptive insulin bolus calculators for people with type 1 diabetes (T1D).MethodsElectronic databases (Medline, Embase and Web of Science) were systematically searched from date of inception to 13 October 2022 for single‐arm or randomized controlled studies assessing adaptive bolus calculators only, in children or adults with T1D on multiple daily injections or insulin pumps with glycaemic outcomes reported. The Clinicaltrials.gov registry was searched for recently completed studies evaluating decision support in T1D. The quality of extracted studies was assessed using the Standard Quality Assessment criteria and the Cochrane Risk of Bias assessment tool.ResultsSix studies were identified. Extracted data were synthesized in a descriptive review because of heterogeneity. All the studies were small feasibility studies or were not suitably powered, and all were deemed to be at a high risk of performance and detection bias because they were unblinded. Overall, these studies did not show a significant glycaemic improvement. Two studies showed a reduction in postprandial time below range or an incremental change in blood glucose concentration; however, these were in controlled environments over a short duration.ConclusionsThere are limited clinical trials evaluating adaptive bolus calculators. Although results from small trials or in‐silico data are promising, further studies are required to support personalized and adaptive management of T1D.

show abstract

Use of Artificial Intelligence to Improve Diabetes Outcomes in Patients Using Multiple Daily Injections Therapy

Cited by 21 publications

References 29 publications

The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database

The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database

Assessing attitudes towards insulin pump therapy in adults with type 1 diabetes: Italian validation of the Insulin Pump Attitudes Questionnaire (IT-IPA questionnaire)

Adaptive bolus calculators for people with type 1 diabetes: A systematic review

Contact Info

Product

Resources

About