Chronic Complications reduction, but How ?
Despite the rapid development of science and technology in healthcare, diabetes remains an incurable lifelong illness. Diabetes education aiming to improve the self-management skills is an essential way to help patients enhance their metabolic control and quality of life. Artificial intelligence (AI) technologies have made significant progress in transforming available genetic data and clinical information into valuable knowledge.
The application of AI tech in disease education would be extremely beneficial considering their advantages in promoting individualization and full-course education intervention according to the unique pictures of different individuals. This paper reviews and discusses the most recent applications of AI techniques to various aspects of diabetes education.
With the information and evidence collected, this review attempts to provide insight and guidance for the development of prospective, data-driven decision support platforms for diabetes management, with a focus on individualized patient management and lifelong educational interventions.
Monitoring of Diabetes Complications
The most common complications of diabetes include vascular pathologies and peripheral neuropathies. Scholars who assessed adult retinal fundus photographs found that detection of diabetic retinopathies by a deep learning algorithm achieved sensitivity and specificity of more than 93%.
In a retrospective analysis of 9,939 posterior pole photographs from 2,740 diabetes patients, Takahashi et al. confirmed that deep learning can be used to assess diabetic retinopathies. Based on their results, Takahashi et al. presented a new AI disease grading system that can be used to grade the severity of diabetic retinopathies.
Likewise, a mobile app called “FootSnap” was developed to standardize diabetic foot images. Through assessments of various scenarios presented by different practitioners, a trial involving 60 patients was conducted to test the stability of FootSnap. The Jaccard similarity index (JSI) was used to confirm the reproducibility of the foot images.
The resulting JSI values for diabetic feet were 0.89–0.91, with values of 0.93–0.94 for the control group, which suggested that the app had excellent reliability. Kaabouch et al. used asymmetry analysis in conjunction with a genetic algorithm to analyze thermal images and thereby facilitate the early detection of foot ulcerations and the assessment of skin integrity. This study found that the tested technology was a feasible means of detecting inflammation and could effectively predict potential foot ulcerations.
Katigari et al. developed an expert system based on fuzzy logic and applied it to the medical records of 244 patients diagnosed with diabetic neuropathies; they found that the system could intelligently determine the severity of diabetic neuropathies with a sensitivity of 89%, a specificity of 98%, and an accuracy of 93%.
With the development of information systems and technologies, the digital data collected from diabetes patients is growing exponentially. From the above observations, it is clear that AI technologies employing complex and precise methods can serve as useful management tools in processing diabetes databases to derive valuable information. Accordingly, AI plays a key role in these relevant systems as an auxiliary routine management tool for diabetes patients.
We have summarized that the most representative applications of artificial intelligence in diabetes education and management. As described above, the diabetes education and management is an essential means of improving the quality of disease management.
As a consequence, the integration of education and management approaches with mobile health and AI technologies has become an unstoppable trend. However, we've noticed that there are cons and pros of the wide range of AI-based methods. However, the applicability of one algorithm is problem and data specific.
For example, in diabetes classification analysis, based on the classification criteria and characteristic distribution of data, some instances may generate good results with directly applying standard methods supplied by common data analysis tools. However, there are also situations where more advanced models are to be developed to infer a clearer layout of the analyzing object. We will proceed with discussing and comparing the strengths and drawbacks of some well-known AI-based methods when certain standards and benchmark data sets are prepared
In conclusion, the use of AI in educational interventions, while promising, faces certain challenges. In spite of the fact that research on the use of digital and intelligent tools in diabetes management is expanding rapidly, most relevant studies lack sufficient numbers of samples or fail to determine whether the results of the tested intervention have clinical significance. To fully and effectively realize AI-based models and establish individualized educational intervention strategies for patients with different needs, it is necessary to achieve the following:
(1) gather large amounts of patient data and establish individualized patient data profiles,
(2) establish in-depth crossover between the professional knowledge of physicians and AI technologies, (3) continuously update and enlarge the existing knowledge bases,
(4) perform standardized, randomized control studies involving clinical practice, and
(5) involve both clinicians and patients jointly in a system designed to optimize effectiveness.
The resolution of these challenges will depend on scientific research, regulation, and standardization of the targeted areas. In addition, other current challenges include technological, philosophical, and ethical dilemmas, as well as issues surrounding user data security and privacy, and even legal hurdles. Among these issues, information security is a new challenge for the AI era. Further efforts will be needed to promote the rapid and effective application of AI to medical fields.
Despite these challenges, the medical applications of AI have been developing at an extremely rapid rate, and the prospects and research value of these applications cannot be denied. The future use of integrated and comprehensive applications of individual AI technologies in diabetes education can provide full-course, individualized, and intelligent education and thereby provide patients with a lifetime of guidance and protection.
To evaluate whether the model can capture a significant portion of the cost associated with treating adverse outcomes, we computed annual cost for most at-risk patients predicted by the model. Diabetes and its related complications place a significant cost burden on the healthcare system.
Continuous population screening and early detection can lead to significant cost savings through preventative measures and resource planning. However, this would only be possible if the model can accurately predict the costly outcomes, meaning that it can make higher predictions on instances with costly adverse outcomes due to diabetes complications than on negative instances.
The cost is computed by first applying the costing algorithm to estimate total annual healthcare expenditure by category (hospitalizations, prescriptions, etc.) for each patient. The costing algorithm follows a bottom-up approach for ambulatory care to get person-level healthcare expenditure per year and per category of healthcare utilization by mapping the utilization data with cost information.
For inpatient hospitalizations, emergency department visits and same day surgery costs, the algorithm estimates costs based on average provincial costs for these procedures weighted by the resource intensity in a given care setting. Utilization data is directly available through the administrative databases leveraged in this study. Cost information is estimated in the algorithm based on amounts billed to the Ministry of Health and Long Term Care (MOHLTC).
We used this costing algorithm off-the-shelf (without any tweaking) on our cohort. This algorithm has been previously validated and is further described elsewhere. From the category estimates, we then isolated the portion of the cost attributed to adverse outcomes by isolating cost from the relevant hospitalizations and ambulatory usage.
Finally, we sorted all patients according to the model predicted probability of adverse outcome, and computed cumulative cost for each percentages of this sorted list. Cost in one percentile is just the sum of costs of all patients in this percentile.
Physician-scientists in the Smidt Heart Institute at Cedars-Sinai have created an artificial intelligence (AI) tool that can effectively identify and distinguish between two life-threatening heart conditions that are often easy to miss: hypertrophic cardiomyopathy and cardiac amyloidosis
“The use of artificial intelligence in cardiology has evolved rapidly and dramatically in a relatively short period of time,” said Susan Cheng, MD, MPH, director of the Institute for Research on Healthy Aging in the Department of Cardiology at the Smidt Heart Institute and co-senior author of the study. “These remarkable strides—which span research and clinical care—can make a tremendous impact in the lives of our patients
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