Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs

Pyrros, Ayis; Borstelmann, Stephen M.; Mantravadi, Ramana; Zaiman, Zachary; Thomas, Kaesha; Price, Brandon; Greenstein, Eugene; Siddiqui, Nasir; Willis, Melinda; Shulhan, Ihar; Hines-Shah, John; Horowitz, Jeanne M.; Nikolaidis, Paul; Lungren, Matthew P.; Rodríguez-Fernández, Jorge Mario; Gichoya, Judy Wawira; Koyejo, Sanmi; Flanders, Adam E; Khandwala, Nishith; Gupta, Amit; Garrett, John W.; Cohen, Joseph Paul; Layden, Brian T.; Pickhardt, Perry J.; Galanter, William

doi:10.1038/s41467-023-39631-x

Cited by 22 publications

(9 citation statements)

References 42 publications

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“…Recently, a new model based on AI was developed to detect diabetes warning signs, even in patients who did not meet the guidelines for diabetes elevated risk. This model can enhance type 2 diabetes (T2D) detection; it uses the patient’s X-ray image collected during routine medical care and their medical records to detect T2D ( 68 ).…”

Section: Ai In Diabetesmentioning

confidence: 99%

The leading global health challenges in the artificial intelligence era

Zaidan

2023

Front. Public Health

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Millions of people’s health is at risk because of several factors and multiple overlapping crises, all of which hit the vulnerable the most. These challenges are dynamic and evolve in response to emerging health challenges and concerns, which need effective collaboration among countries working toward achieving Sustainable Development Goals (SDGs) and securing global health. Mental Health, the Impact of climate change, cardiovascular diseases (CVDs), diabetes, Infectious diseases, health system, and population aging are examples of challenges known to pose a vast burden worldwide. We are at a point known as the “digital revolution,” characterized by the expansion of artificial intelligence (AI) and a fusion of technology types. AI has emerged as a powerful tool for addressing various health challenges, and the last ten years have been influential due to the rapid expansion in the production and accessibility of health-related data. The computational models and algorithms can understand complicated health and medical data to perform various functions and deep-learning strategies. This narrative mini-review summarizes the most current AI applications to address the leading global health challenges. Harnessing its capabilities can ultimately mitigate the Impact of these challenges and revolutionize the field. It has the ability to strengthen global health through personalized health care and improved preparedness and response to future challenges. However, ethical and legal concerns about individual or community privacy and autonomy must be addressed for effective implementation.

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Section: Ai In Diabetesmentioning

confidence: 99%

The leading global health challenges in the artificial intelligence era

Zaidan

2023

Front. Public Health

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“…area, colour) providing relevant alerts in the event of clinical deterioration. Interestingly, deep-learning approaches have recently been used to uncover new uses for electronic-health-record imaging data collected for other purposes, as demonstrated by a recent study presenting a model that predicts type 2 diabetes diagnosis from chest radiographs [ 38 ]. Explainability analysis suggested distribution of adiposity (particularly mediastinal lipomatosis) as a predictive driver, yielding biological insights in addition to predictive screening potential.…”

Section: Introductionmentioning

confidence: 99%

Diabetes and artificial intelligence beyond the closed loop: a review of the landscape, promise and challenges

Mackenzie,

Sainsbury,

Wake

2023

Diabetologia

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The discourse amongst diabetes specialists and academics regarding technology and artificial intelligence (AI) typically centres around the 10% of people with diabetes who have type 1 diabetes, focusing on glucose sensors, insulin pumps and, increasingly, closed-loop systems. This focus is reflected in conference topics, strategy documents, technology appraisals and funding streams. What is often overlooked is the wider application of data and AI, as demonstrated through published literature and emerging marketplace products, that offers promising avenues for enhanced clinical care, health-service efficiency and cost-effectiveness. This review provides an overview of AI techniques and explores the use and potential of AI and data-driven systems in a broad context, covering all diabetes types, encompassing: (1) patient education and self-management; (2) clinical decision support systems and predictive analytics, including diagnostic support, treatment and screening advice, complications prediction; and (3) the use of multimodal data, such as imaging or genetic data. The review provides a perspective on how data- and AI-driven systems could transform diabetes care in the coming years and how they could be integrated into daily clinical practice. We discuss evidence for benefits and potential harms, and consider existing barriers to scalable adoption, including challenges related to data availability and exchange, health inequality, clinician hesitancy and regulation. Stakeholders, including clinicians, academics, commissioners, policymakers and those with lived experience, must proactively collaborate to realise the potential benefits that AI-supported diabetes care could bring, whilst mitigating risk and navigating the challenges along the way. Graphical Abstract

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“…17 In brief, a myriad of variables were chosen for diabetes prediction, encompassing anthropometric measurements, 18 laboratory test results 19 and even radiographic images. 20 Widely employed algorithms, including logistic regression, random forest (RF), decision tree, gradient boosting machine and deep neural network, have been recognized for their superior predictive accuracy 17,21 (details reviewed in the Supporting Information section). The model performance may depend on the coherence between the developing and validation datasets of the model.…”

Section: Introductionmentioning

confidence: 99%

“…Current advances in artificial intelligence, particularly ML algorithms, in diabetes prediction were summarized and reviewed 17 . In brief, a myriad of variables were chosen for diabetes prediction, encompassing anthropometric measurements, 18 laboratory test results 19 and even radiographic images 20 . Widely employed algorithms, including logistic regression, random forest (RF), decision tree, gradient boosting machine and deep neural network, have been recognized for their superior predictive accuracy 17,21 (details reviewed in the Supporting Information section).…”

Section: Introductionmentioning

confidence: 99%

Differential effect of interventions in patients with prediabetes stratified by a machine learning‐based diabetes progression prediction model

Zou,

Luo,

Huang

et al. 2023

Diabetes Obesity Metabolism

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AimTo investigate whether stratifying participants with prediabetes according to their diabetes progression risks (PR) could affect their responses to interventions.MethodsWe developed a machine learning‐based model to predict the 1‐year diabetes PR (ML‐PR) with the least predictors. The model was developed and internally validated in participants with prediabetes in the Pinggu Study (a prospective population‐based survey in suburban Beijing; n = 622). Patients from the Beijing Prediabetes Reversion Program cohort (a multicentre randomized control trial to evaluate the efficacy of lifestyle and/or pioglitazone on prediabetes reversion; n = 1936) were stratified to low‐, medium‐ and high‐risk groups using ML‐PR. Different effect of four interventions within subgroups on prediabetes reversal and diabetes progression was assessed.ResultsUsing least predictors including fasting plasma glucose, 2‐h postprandial glucose after 75 g glucose administration, glycated haemoglobin, high‐density lipoprotein cholesterol and triglycerides, and the ML algorithm XGBoost, ML‐PR successfully predicted the 1‐year progression of participants with prediabetes in the Pinggu study [internal area under the curve of the receiver operating characteristic curve 0.80 (0.72–0.89)] and Beijing Prediabetes Reversion Program [external area under the curve of the receiver operating characteristic curve 0.80 (0.74–0.86)]. In the high‐risk group pioglitazone plus intensive lifestyle therapy significantly reduced diabetes progression by about 50% at year l and the end of the trial in the high‐risk group compared with conventional lifestyle therapy with placebo. In the medium‐ or low‐risk group, intensified lifestyle therapy, pioglitazone or their combination did not show any benefit on diabetes progression and prediabetes reversion.ConclusionsThis study suggests personalized treatment for prediabetes according to their PR is necessary. ML‐PR model with simple clinical variables may facilitate personal treatment strategies in participants with prediabetes.

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Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs

Cited by 22 publications

References 42 publications

The leading global health challenges in the artificial intelligence era

The leading global health challenges in the artificial intelligence era

Diabetes and artificial intelligence beyond the closed loop: a review of the landscape, promise and challenges

Differential effect of interventions in patients with prediabetes stratified by a machine learning‐based diabetes progression prediction model

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