ABSTRACT
Regular screening for the early detection of common chronic diseases might benefit from the use of deep-learning approaches, particularly in resource-poor or remote settings. Here we show that deep-learning models can be used to identify chronic kidney disease and type 2 diabetes solely from fundus images or in combination with clinical metadata (age, sex, height, weight, body-mass index and blood pressure) with areas under the receiver operating characteristic curve of 0.85-0.93. The models were trained and validated with a total of 115,344 retinal fundus photographs from 57,672 patients and can also be used to predict estimated glomerulal filtration rates and blood-glucose levels, with mean absolute errors of 11.1-13.4 ml min-1 per 1.73 m2 and 0.65-1.1 mmol l-1, and to stratify patients according to disease-progression risk. We evaluated the generalizability of the models for the identification of chronic kidney disease and type 2 diabetes with population-based external validation cohorts and via a prospective study with fundus images captured with smartphones, and assessed the feasibility of predicting disease progression in a longitudinal cohort.
Subject(s)
Deep Learning , Diabetes Mellitus, Type 2/diagnostic imaging , Image Interpretation, Computer-Assisted/statistics & numerical data , Photography/statistics & numerical data , Renal Insufficiency, Chronic/diagnostic imaging , Retina/diagnostic imaging , Area Under Curve , Blood Glucose/metabolism , Body Height , Body Mass Index , Body Weight , Diabetes Mellitus, Type 2/metabolism , Diabetes Mellitus, Type 2/pathology , Disease Progression , Female , Fundus Oculi , Glomerular Filtration Rate , Humans , Male , Metadata/statistics & numerical data , Middle Aged , Neural Networks, Computer , Photography/methods , Prospective Studies , ROC Curve , Renal Insufficiency, Chronic/metabolism , Renal Insufficiency, Chronic/pathology , Retina/metabolism , Retina/pathologyABSTRACT
Common lung diseases are first diagnosed using chest X-rays. Here, we show that a fully automated deep-learning pipeline for the standardization of chest X-ray images, for the visualization of lesions and for disease diagnosis can identify viral pneumonia caused by coronavirus disease 2019 (COVID-19) and assess its severity, and can also discriminate between viral pneumonia caused by COVID-19 and other types of pneumonia. The deep-learning system was developed using a heterogeneous multicentre dataset of 145,202 images, and tested retrospectively and prospectively with thousands of additional images across four patient cohorts and multiple countries. The system generalized across settings, discriminating between viral pneumonia, other types of pneumonia and the absence of disease with areas under the receiver operating characteristic curve (AUCs) of 0.94-0.98; between severe and non-severe COVID-19 with an AUC of 0.87; and between COVID-19 pneumonia and other viral or non-viral pneumonia with AUCs of 0.87-0.97. In an independent set of 440 chest X-rays, the system performed comparably to senior radiologists and improved the performance of junior radiologists. Automated deep-learning systems for the assessment of pneumonia could facilitate early intervention and provide support for clinical decision-making.
Subject(s)
COVID-19/diagnostic imaging , Databases, Factual , Deep Learning , SARS-CoV-2 , Tomography, X-Ray Computed , Diagnosis, Differential , Female , Humans , Male , Severity of Illness IndexABSTRACT
Many COVID-19 patients infected by SARS-CoV-2 virus develop pneumonia (called novel coronavirus pneumonia, NCP) and rapidly progress to respiratory failure. However, rapid diagnosis and identification of high-risk patients for early intervention are challenging. Using a large computed tomography (CT) database from 3,777 patients, we developed an AI system that can diagnose NCP and differentiate it from other common pneumonia and normal controls. The AI system can assist radiologists and physicians in performing a quick diagnosis especially when the health system is overloaded. Significantly, our AI system identified important clinical markers that correlated with the NCP lesion properties. Together with the clinical data, our AI system was able to provide accurate clinical prognosis that can aid clinicians to consider appropriate early clinical management and allocate resources appropriately. We have made this AI system available globally to assist the clinicians to combat COVID-19.
Subject(s)
Artificial Intelligence , Coronavirus Infections/diagnosis , Pneumonia, Viral/diagnosis , Tomography, X-Ray Computed , COVID-19 , China , Cohort Studies , Coronavirus Infections/pathology , Coronavirus Infections/therapy , Datasets as Topic , Humans , Lung/pathology , Models, Biological , Pandemics , Pilot Projects , Pneumonia, Viral/pathology , Pneumonia, Viral/therapy , Prognosis , Radiologists , Respiratory Insufficiency/diagnosisABSTRACT
The ability to identify a specific type of leukemia using minimally invasive biopsies holds great promise to improve the diagnosis, treatment selection, and prognosis prediction of patients. Using genome-wide methylation profiling and machine learning methods, we investigated the utility of CpG methylation status to differentiate blood from patients with acute lymphocytic leukemia (ALL) or acute myelogenous leukemia (AML) from normal blood. We established a CpG methylation panel that can distinguish ALL and AML blood from normal blood as well as ALL blood from AML blood with high sensitivity and specificity. We then developed a methylation-based survival classifier with 23 CpGs for ALL and 20 CpGs for AML that could successfully divide patients into high-risk and low-risk groups, with significant differences in clinical outcome in each leukemia type. Together, these findings demonstrate that methylation profiles can be highly sensitive and specific in the accurate diagnosis of ALL and AML, with implications for the prediction of prognosis and treatment selection.
Subject(s)
Biomarkers, Tumor/genetics , DNA Methylation/genetics , Leukemia/genetics , Prognosis , Adolescent , Adult , Aged , Aged, 80 and over , Child , Child, Preschool , CpG Islands/genetics , Female , Gene Expression Regulation, Neoplastic/genetics , Humans , Infant , Leukemia/classification , Leukemia/diagnosis , Leukemia/pathology , Machine Learning , Male , Middle Aged , Promoter Regions, Genetic/genetics , Young AdultABSTRACT
Epigenetic alterations and metabolic dysfunction are two hallmarks of aging. However, the mechanism of how their interaction regulates aging, particularly in mammals, remains largely unknown. Here we show ELOVL fatty acid elongase 2 (Elovl2), a gene whose epigenetic alterations are most highly correlated with age prediction, contributes to aging by regulating lipid metabolism. Impaired Elovl2 function disturbs lipid synthesis with increased endoplasmic reticulum stress and mitochondrial dysfunction, leading to key accelerated aging phenotypes. Restoration of mitochondrial activity can rescue age-related macular degeneration (AMD) phenotypes induced by Elovl2 deficiency in human retinal pigmental epithelial (RPE) cells. We revealed an epigenetic-metabolism axis contributing to aging and potentially to antiaging therapy.
ABSTRACT
Artificial intelligence (AI)-based methods have emerged as powerful tools to transform medical care. Although machine learning classifiers (MLCs) have already demonstrated strong performance in image-based diagnoses, analysis of diverse and massive electronic health record (EHR) data remains challenging. Here, we show that MLCs can query EHRs in a manner similar to the hypothetico-deductive reasoning used by physicians and unearth associations that previous statistical methods have not found. Our model applies an automated natural language processing system using deep learning techniques to extract clinically relevant information from EHRs. In total, 101.6 million data points from 1,362,559 pediatric patient visits presenting to a major referral center were analyzed to train and validate the framework. Our model demonstrates high diagnostic accuracy across multiple organ systems and is comparable to experienced pediatricians in diagnosing common childhood diseases. Our study provides a proof of concept for implementing an AI-based system as a means to aid physicians in tackling large amounts of data, augmenting diagnostic evaluations, and to provide clinical decision support in cases of diagnostic uncertainty or complexity. Although this impact may be most evident in areas where healthcare providers are in relative shortage, the benefits of such an AI system are likely to be universal.
Subject(s)
Deep Learning , Diagnosis, Computer-Assisted , Electronic Health Records , Natural Language Processing , Pediatrics , Adolescent , Artificial Intelligence , Child , Child, Preschool , China , Female , Humans , Infant , Infant, Newborn , Machine Learning , Male , Proof of Concept Study , Reproducibility of Results , Retrospective StudiesABSTRACT
The implementation of clinical-decision support algorithms for medical imaging faces challenges with reliability and interpretability. Here, we establish a diagnostic tool based on a deep-learning framework for the screening of patients with common treatable blinding retinal diseases. Our framework utilizes transfer learning, which trains a neural network with a fraction of the data of conventional approaches. Applying this approach to a dataset of optical coherence tomography images, we demonstrate performance comparable to that of human experts in classifying age-related macular degeneration and diabetic macular edema. We also provide a more transparent and interpretable diagnosis by highlighting the regions recognized by the neural network. We further demonstrate the general applicability of our AI system for diagnosis of pediatric pneumonia using chest X-ray images. This tool may ultimately aid in expediting the diagnosis and referral of these treatable conditions, thereby facilitating earlier treatment, resulting in improved clinical outcomes. VIDEO ABSTRACT.
Subject(s)
Deep Learning , Diagnostic Imaging , Pneumonia/diagnosis , Child , Humans , Neural Networks, Computer , Pneumonia/diagnostic imaging , ROC Curve , Reproducibility of Results , Tomography, Optical CoherenceABSTRACT
An effective blood-based method for the diagnosis and prognosis of hepatocellular carcinoma (HCC) has not yet been developed. Circulating tumour DNA (ctDNA) carrying cancer-specific genetic and epigenetic aberrations may enable a noninvasive 'liquid biopsy' for diagnosis and monitoring of cancer. Here, we identified an HCC-specific methylation marker panel by comparing HCC tissue and normal blood leukocytes and showed that methylation profiles of HCC tumour DNA and matched plasma ctDNA are highly correlated. Using cfDNA samples from a large cohort of 1,098 HCC patients and 835 normal controls, we constructed a diagnostic prediction model that showed high diagnostic specificity and sensitivity (P < 0.001) and was highly correlated with tumour burden, treatment response, and stage. Additionally, we constructed a prognostic prediction model that effectively predicted prognosis and survival (P < 0.001). Together, these findings demonstrate in a large clinical cohort the utility of ctDNA methylation markers in the diagnosis, surveillance, and prognosis of HCC.
Subject(s)
Biomarkers, Tumor , Carcinoma, Hepatocellular , Circulating Tumor DNA , DNA Methylation , Liver Neoplasms , Models, Biological , Biomarkers, Tumor/blood , Biomarkers, Tumor/genetics , Carcinoma, Hepatocellular/blood , Carcinoma, Hepatocellular/diagnosis , Carcinoma, Hepatocellular/genetics , Circulating Tumor DNA/blood , Circulating Tumor DNA/genetics , Female , Humans , Liver Neoplasms/blood , Liver Neoplasms/diagnosis , Liver Neoplasms/genetics , Male , PrognosisABSTRACT
Glaucoma, a blinding neurodegenerative disease, whose risk factors include elevated intraocular pressure (IOP), age, and genetics, is characterized by accelerated and progressive retinal ganglion cell (RGC) death. Despite decades of research, the mechanism of RGC death in glaucoma is still unknown. Here, we demonstrate that the genetic effect of the SIX6 risk variant (rs33912345, His141Asn) is enhanced by another major POAG risk gene, p16INK4a (cyclin-dependent kinase inhibitor 2A, isoform INK4a). We further show that the upregulation of homozygous SIX6 risk alleles (CC) leads to an increase in p16INK4a expression, with subsequent cellular senescence, as evidenced in a mouse model of elevated IOP and in human POAG eyes. Our data indicate that SIX6 and/or IOP promotes POAG by directly increasing p16INK4a expression, leading to RGC senescence in adult human retinas. Our study provides important insights linking genetic susceptibility to the underlying mechanism of RGC death and provides a unified theory of glaucoma pathogenesis.