Usability Evaluation of a Blood Glucose Monitoring System With a Spill-Resistant Vial, Easier Strip Handling, and Connectivity to a Mobile App: Improvement of Patient Convenience and Satisfaction

Artificial intelligence in the management and treatment of burns: a systematic review

BACKGROUND

Artificial intelligence (AI) is an innovative field with potential for improving burn care. This article provides an updated review on machine learning in burn care and discusses future challenges and the role of healthcare professionals in the successful implementation of AI technologies.

METHODS

A systematic search was carried out on MEDLINE, Embase and PubMed databases for English-language articles studying machine learning in burns. Articles were reviewed quantitatively and qualitatively for clinical applications, key features, algorithms, outcomes and validation methods.

RESULTS

A total of 46 observational studies were included for review. Assessment of burn depth (n = 26), support vector machines (n = 19) and 10-fold cross-validation (n = 11) were the most common application, algorithm and validation tool used, respectively.

CONCLUSION

AI should be incorporated into clinical practice as an adjunct to the experienced burns provider once direct comparative analysis to current gold standards outlining its benefits and risks have been studied. Future considerations must include the development of a burn-specific common framework. Authors should use common validation tools to allow for effective comparisons. Level I/II evidence is required to produce robust proof about clinical and economic impacts.

Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future

Artificial intelligence (AI) is the use of mathematical algorithms to mimic human cognitive abilities and to address difficult healthcare challenges including complex biological abnormalities like cancer. The exponential growth of AI in the last decade is evidenced to be the potential platform for optimal decision-making by super-intelligence, where the human mind is limited to process huge data in a narrow time range. Cancer is a complex and multifaced disorder with thousands of genetic and epigenetic variations. AI-based algorithms hold great promise to pave the way to identify these genetic mutations and aberrant protein interactions at a very early stage. Modern biomedical research is also focused to bring AI technology to the clinics safely and ethically. AI-based assistance to pathologists and physicians could be the great leap forward towards prediction for disease risk, diagnosis, prognosis, and treatments. Clinical applications of AI and Machine Learning (ML) in cancer diagnosis and treatment are the future of medical guidance towards faster mapping of a new treatment for every individual. By using AI base system approach, researchers can collaborate in real-time and share knowledge digitally to potentially heal millions. In this review, we focused to present game-changing technology of the future in clinics, by connecting biology with Artificial Intelligence and explain how AI-based assistance help oncologist for precise treatment.

Role of Artificial Intelligence (AI) in Surgery: Introduction, General Principles, and Potential Applications

AI (Artificial intelligence) is an interdisciplinary field aimed at the development of algorithms to endow machines with the capability of executing cognitive tasks. The number of publications regarding AI and surgery has increased dramatically over the last two decades. This phenomenon can partly be explained by the exponential growth in computing power available to the largest AI training runs. AI can be classified into different sub-domains with extensive potential clinical applications in the surgical setting. AI will increasingly become a major component of clinical practice in surgery. The aim of the present Narrative Review is to give a general introduction and summarized overview of AI, as well as to present additional remarks on potential surgical applications and future perspectives in surgery.

Reducing Treatment Errors Through Point-of-Care Glucometer Configuration

BACKGROUND

Blood glucose (BG) testing is the most widely performed point-of-care (POC) test in a hospital setting. Multiple adverse events reported to the Food and Drug Administration (FDA) revealed that treatment decisions may be affected by information displayed on the POC glucometer's results screen. A randomized, crossover simulation study was conducted to compare two results screen configurations for ACCU-CHEK Inform II, a POC glucometer.

METHODS

Prior to the study, a heuristic evaluation of the results screen configurations and a pilot study were conducted to select the two results screen configurations for comparison. At two multicampus medical centers, 66 nurse participants experienced two computer-based simulation scenarios that asked them to interpret glucometer readings and make treatment decisions for simulated patients with 32 mg/dL BG levels and subtle symptoms of hypoglycemia. One scenario displayed a numeric value ("32 mg/dL"), and the other displayed a range abbreviation, such as "RR LO" (out of reportable range; low). Treatment errors were recorded when the participant did not treat the hypoglycemic patient with glucose or when they administered insulin.

RESULTS

When ACCU-CHEK Inform II displayed an "RR LO" reading, 10.6% of participants made a treatment error, including 6.7% of participants with prior training on the meaning of an "RR LO" reading. None of the participants made a treatment error when ACCU-CHEK Inform II displayed a "32 mg/dL" reading.

CONCLUSION

Displaying a numeric BG reading eliminated potentially life-threating treatment errors caused by confusing range abbreviations. Manufacturers should consider these findings during future research and development of POC glucometers.

Artificial Intelligence in Surgery: Promises and Perils

OBJECTIVE

The aim of this review was to summarize major topics in artificial intelligence (AI), including their applications and limitations in surgery. This paper reviews the key capabilities of AI to help surgeons understand and critically evaluate new AI applications and to contribute to new developments.

SUMMARY BACKGROUND DATA

AI is composed of various subfields that each provide potential solutions to clinical problems. Each of the core subfields of AI reviewed in this piece has also been used in other industries such as the autonomous car, social networks, and deep learning computers.

METHODS

A review of AI papers across computer science, statistics, and medical sources was conducted to identify key concepts and techniques within AI that are driving innovation across industries, including surgery. Limitations and challenges of working with AI were also reviewed.

RESULTS

Four main subfields of AI were defined: (1) machine learning, (2) artificial neural networks, (3) natural language processing, and (4) computer vision. Their current and future applications to surgical practice were introduced, including big data analytics and clinical decision support systems. The implications of AI for surgeons and the role of surgeons in advancing the technology to optimize clinical effectiveness were discussed.

CONCLUSIONS

Surgeons are well positioned to help integrate AI into modern practice. Surgeons should partner with data scientists to capture data across phases of care and to provide clinical context, for AI has the potential to revolutionize the way surgery is taught and practiced with the promise of a future optimized for the highest quality patient care.

One Drop | Mobile on iPhone and Apple Watch: An Evaluation of HbA1c Improvement Associated With Tracking Self-Care

Background

The One Drop | Mobile app supports manual and passive (via HealthKit and One Drop’s glucose meter) tracking of self-care and glycated hemoglobin A_1c (HbA_1c).

Objective

We assessed the HbA_1c change of a sample of people with type 1 diabetes (T1D) or type 2 diabetes (T2D) using the One Drop | Mobile app on iPhone and Apple Watch, and tested relationships between self-care tracking with the app and HbA_1c change.

Methods

In June 2017, we identified people with diabetes using the One Drop | Mobile app on iPhone and Apple Watch who entered two HbA_1c measurements in the app 60 to 365 days apart. We assessed the relationship between using the app and HbA_1c change.

Results

Users had T1D (n=65) or T2D (n=191), were 22.7% (58/219) female, with diabetes for a mean 8.34 (SD 8.79) years, and tracked a mean 2176.35 (SD 3430.23) self-care activities between HbA_1c entries. There was a significant 1.36% or 14.9 mmol/mol HbA_1c reduction (F=62.60, P<.001) from the first (8.72%, 71.8 mmol/mol) to second HbA_1c (7.36%, 56.9 mmol/mol) measurement. Tracking carbohydrates was independently associated with greater HbA_1c improvement (all P<.01).

Conclusions

Using One Drop | Mobile on iPhone and Apple Watch may favorably impact glycemic control.

Improved Glycemic Control in a Patient Group Performing 7-Point Profile Self-Monitoring of Blood Glucose and Intensive Data Documentation: An Open-Label, Multicenter, Observational Study

INTRODUCTION

Regular self-monitoring of blood glucose (SMBG) is recommended as an integral part of therapy for all patients with diabetes treated with insulin. In the current study, the effects on glycemic control of taking 7-point SMBG profiles and using a diabetes management system (DMA) on a smartphone were investigated.

METHODS

In a 12-week, open-label, multicenter, observational study, 51 patients [26 with type 1 diabetes mellitus (T1DM) and 25 with type 2 diabetes mellitus (T2DM)] were instructed to perform SMBG at least seven times a day using DMA combined with the iBGStar ^® SMBG system. HbA1c was measured at regular visits to the study sites. Patients reviewed and managed their data as well as their treatment on their own and there were no further assistance or treatment recommendations. Adverse events (AEs) were recorded throughout.

RESULTS

Overall, mean (SD) change from baseline in HbA1c at week 12 was -0.46 (0.57)% [-5 (6) mmol/mol (p < 0.0001)]. The change in HbA1c was observed in patients with T1DM [-0.27 (0.45)% (-3 [5] mmol/mol; p = 0.0063)] and T2DM [-0.65 (0.62)% (-7 [7] mmol/mol; p < 0.0001)]. The change in HbA1c was not correlated with an increased number of hypoglycemic events (blood glucose less than 55 mg/dL). The majority of AEs were symptomatic hypoglycemic events (42 events; nine patients).

CONCLUSIONS

Glycemic control can be improved, without receiving any recommendations or advice on insulin dose, by performing daily 7-point SMBG profiles and using electronic documentation with a smartphone app. These results must be confirmed in a larger controlled trial, but they already strengthen the importance of structured SMBG in diabetes therapy.

FUNDING

Sanofi.