Deep-learning-based artificial intelligence algorithm for detecting anemia using electrocardiogram

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
K.H Jeon ◽  
J.M Kwon ◽  
K.H Kim ◽  
M.J Kim ◽  
S.H Lee ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Predictive Value ◽  
External Validation ◽  
Oxygen Demand ◽  
Operating Characteristics ◽  
Internal Validation ◽  
Non Invasive ◽  
Receiver Operating Characteristics Curve ◽  
Artificial Intelligence Algorithm

Abstract Background Anemia changed the morphology of electrocardiography (ECG), and researchers suggested that mismatching oxygen demand and supply in the myocardium affects the ECG Purpose A deep-learning-based algorithm (DLA) that enables non-invasive anemia screening from electrocardiograms (ECGs) may improve the detection of anemia. Methods A DLA was developed using 57,435 ECGs from 31,898 patients and was internally validated using 7,369 ECGs from 7,369 patients taken at one hospital. External validation was performed using 4,068 ECGs from 4,068 patients admitted at another hospital. Three types of DLA were developed using 12-lead ECGs to detect hemoglobin levels of 10 mg/dL or less. The DLA was built by a convolutional neural network and used 500-Hz raw ECG, age, and sex as input data. Results During internal and external validation, the area under the receiver operating characteristics curve (AUROC) of the DLA using a 12-lead ECG for detecting anemia was 0.941 and 0.904, respectively. Using a 90% sensitivity operating point, the specificity, negative predictive value, and positive predictive value of internal validation were 0.889, 0.998, and 0.151, respectively, and those of external validation were 0.785, 0.994, and 0.166, respectively. The predicted Hgb level based on the DLA was correlated with the actual Hgb level (r=0.891, 95% CI 0.890–0.893, P<0.0001). 57 patients of moderate to severe anemia were treated with appropriated blood transfusion and predicted DLA score of most patients who received transfusion decreased after transfusion according to increase in hemoglobin level. Conclusion In this study, using raw ECG data, a DLA accurately detected anemia. The application of artificial intelligence to ECGs may enable screening for anemia. Correlation between DLA and actual Hb Funding Acknowledgement Type of funding source: None

scholarly journals Diagnostic Performance of Endoscopic Ultrasound-Artificial Intelligence Using Deep Learning Analysis of Gallbladder Polypoid Lesions: A Feasibility Study

2021 ◽  
Author(s):  
Sung Ill Jang ◽  
Young Jae Kim ◽  
Eui Joo Kim ◽  
Huapyong Kang ◽  
Seung Jin Shon ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Differential Diagnosis ◽  
Deep Learning ◽  
Endoscopic Ultrasound ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
Validation Cohort ◽  
External Validation ◽  
Diagnostic Modality ◽  
Internal Validation

Abstract Endoscopic ultrasound (EUS) is the most accurate diagnostic modality for polypoid lesions of the gallbladder (GB), but is limited by subjective interpretation. We evaluated the diagnostic performance of deep learning-based artificial intelligence (AI) in differentiating polypoid lesions using EUS images. The diagnostic performance of the EUS-AI system with ResNet50 architecture was evaluated via three processes: training, internal validation, and testing. The diagnostic performance was also verified using an external validation cohort and compared with the performance of EUS endoscopists. In the AI development cohort, the diagnostic performance of EUS-AI including sensitivity, specificity, positive predictive value, negative predictive value and accuracy. For the differential diagnosis of neoplastic and non-neoplastic GB polyps, these values for EUS-AI were 77.8%, 91.6%, 57.9%, 96.5%, and 89.8%, respectively. In the external validation cohort, the differential diagnosis of neoplastic and non-neoplastic GB polyps, these values were 60.3%, 77.4%, 36.2%, 90.2%, and 74.4%, respectively, for EUS-AI; they were 74.2%, 44.9%, 75.4%, 46.2%, and 65.3%, respectively, for the endoscopists. The accuracy of the EUS-AI was between the accuracies of mid-level (66.7%) and expert EUS endoscopists (77.5%). This EUS-AI system showed favorable performance for the diagnosis of neoplastic GB polyps, with a performance comparable to that of EUS endoscopists.

Automated Pancreas Segmentation And Volumetry Using Deep Neural Network On Computed Tomography

2021 ◽  
Author(s):  
Sang-Heon Lim ◽  
Young Jae Kim ◽  
Yeon-Ho Park ◽  
Doojin Kim ◽  
Kwang Gi Kim ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Automatic Segmentation ◽  
External Validation ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Quantitative Information ◽  
Similarity Coefficients ◽  
Internal Validation ◽  
Pancreas Segmentation

Abstract Pancreas segmentation is necessary for observing lesions, analyzing anatomical structures, and predicting patient prognosis. Therefore, various studies have designed segmentation models based on convolutional neural networks for pancreas segmentation. However, the deep learning approach is limited by a lack of data, and studies conducted on a large computed tomography dataset are scarce. Therefore, this study aims to perform deep-learning-based semantic segmentation on 1,006 participants and evaluate the automatic segmentation performance of the pancreas via four individual three-dimensional segmentation networks. In this study, we performed internal validation with 1,006 patients and external validation using the Cancer Imaging Archive (TCIA) pancreas dataset. We obtained mean precision, recall, and dice similarity coefficients of 0.869, 0.842, and 0.842, respectively, for internal validation via a relevant approach among the four deep learning networks. Using the external dataset, the deep learning network achieved mean precision, recall, and dice similarity coefficients of 0.779, 0.749, and 0.735, respectively. We expect that generalized deep-learning-based systems can assist clinical decisions by providing accurate pancreatic segmentation and quantitative information of the pancreas for abdominal computed tomography.

An Artificial Intelligence System for the Detection of Bladder Cancer via Cystoscopy: A Multicenter Diagnostic Study

2021 ◽  
Author(s):  
Shaoxu Wu ◽  
Xiong Chen ◽  
Jiexin Pan ◽  
Wen Dong ◽  
Xiayao Diao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bladder Cancer ◽  
Diagnostic Accuracy ◽  
External Validation ◽  
Operation Time ◽  
Short Latency ◽  
Lesion Detection ◽  
Diagnostic Study ◽  
Internal Validation ◽  
Predictive Values

Abstract Background Cystoscopy plays an important role in bladder cancer (BCa) diagnosis and treatment, but its sensitivity needs improvement. Artificial intelligence has shown promise in endoscopy, but few cystoscopic applications have been reported. We report a Cystoscopy Artificial Intelligence Diagnostic System (CAIDS) for BCa diagnosis. Methods In total, 69,204 images from 10,729 consecutive patients from six hospitals were collected and divided into training, internal validation, and external validation sets. The CAIDS was built using a pyramid scene parsing network and transfer learning. A subset (n = 260) of the validation sets was used for a performance comparison between the CAIDS and urologists for complex lesion detection. The diagnostic accuracy, sensitivity, specificity, and positive and negative predictive values and 95% confidence intervals (CIs) were calculated using the Clopper-Pearson method. Results The diagnostic accuracies of the CAIDS were 0.977 (95% CI = 0.974–0.979) in the internal validation set and 0.990 (95% CI = 0.979–0.996), 0.982 (95% CI = 0.974–0.988), 0.978 (95% CI = 0.959–0.989), and 0.991 (95% CI = 0.987–0.994) in different external validation sets. In the CAIDS versus urologists’ comparisons, the CAIDS showed high accuracy and sensitivity (accuracy = 0.939, 95% CI = 0.902–0.964; and sensitivity = 0.954, 95% CI = 0.902–0.983) with a short latency of 12 s, much more accurate and quicker than the expert urologists. Conclusions The CAIDS achieved accurate BCa detection with a short latency. The CAIDS may provide many clinical benefits, from increasing the diagnostic accuracy for BCa, even for commonly misdiagnosed cases such as flat cancerous tissue (carcinoma in situ), to reducing the operation time for cystoscopy.

scholarly journals Automatic differentiation of thyroid scintigram by deep convolutional neural network: a dual center study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pei Yang ◽  
Yong Pi ◽  
Tao He ◽  
Jiangming Sun ◽  
Jianan Wei ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Predictive Value ◽  
Automatic Differentiation ◽  
External Validation ◽  
Deep Convolutional Neural Network ◽  
Image Feature ◽  
Training Dataset ◽  
Internal Validation

Abstract Background 99mTc-pertechnetate thyroid scintigraphy is a valid complementary avenue for evaluating thyroid disease in the clinic, the image feature of thyroid scintigram is relatively simple but the interpretation still has a moderate consistency among physicians. Thus, we aimed to develop an artificial intelligence (AI) system to automatically classify the four patterns of thyroid scintigram. Methods We collected 3087 thyroid scintigrams from center 1 to construct the training dataset (n = 2468) and internal validating dataset (n = 619), and another 302 cases from center 2 as external validating datasets. Four pre-trained neural networks that included ResNet50, DenseNet169, InceptionV3, and InceptionResNetV2 were implemented to construct AI models. The models were trained separately with transfer learning. We evaluated each model’s performance with metrics as following: accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), recall, precision, and F1-score. Results The overall accuracy of four pre-trained neural networks in classifying four common uptake patterns of thyroid scintigrams all exceeded 90%, and the InceptionV3 stands out from others. It reached the highest performance with an overall accuracy of 92.73% for internal validation and 87.75% for external validation, respectively. As for each category of thyroid scintigrams, the area under the receiver operator characteristic curve (AUC) was 0.986 for ‘diffusely increased,’ 0.997 for ‘diffusely decreased,’ 0.998 for ‘focal increased,’ and 0.945 for ‘heterogeneous uptake’ in internal validation, respectively. Accordingly, the corresponding performances also obtained an ideal result of 0.939, 1.000, 0.974, and 0.915 in external validation, respectively. Conclusions Deep convolutional neural network-based AI model represented considerable performance in the classification of thyroid scintigrams, which may help physicians improve the interpretation of thyroid scintigrams more consistently and efficiently.

scholarly journals Macrophage Markers Do Not Add to the Prediction of Liver Fibrosis by Transient Elastography in Patients With Metabolic Associated Fatty Liver Disease

2020 ◽  
Vol 7 ◽  
Author(s):  
Konstantin Kazankov ◽  
Chiara Rosso ◽  
Ramy Younes ◽  
Angelo Armandi ◽  
Hannes Hagström ◽  
...  
Keyword(s):  
Liver Disease ◽  
Liver Fibrosis ◽  
Fatty Liver ◽  
Fatty Liver Disease ◽  
Transient Elastography ◽  
Mannose Receptor ◽  
Operating Characteristics ◽  
Non Invasive ◽  
Receiver Operating Characteristics Curve ◽  
Italian Cohort

Background and Aims: Non-invasive fibrosis staging is essential in metabolic associated fatty liver disease (MAFLD). Transient elastography (TE) is a well-established method for liver fibrosis assessment. We have previously shown that the macrophage marker sCD163 is an independent predictor for fibrosis in MAFLD. In the present study we tested whether the combination of macrophage markers and TE improves fibrosis prediction.Methods: We measured macrophage markers soluble (s)CD163 and mannose receptor (sMR) in two independent cohorts from Italy (n = 141) and Sweden (n = 70) with biopsy-proven MAFLD and available TE.Results: In the Italian cohort, TE and sCD163 showed similar moderate associations with liver fibrosis (rho = 0.56, p < 0.001 and rho = 0.42, p < 0.001, respectively). TE had an area under the Receiver Operating Characteristics curve (AUROC, with 95% CI) for fibrosis; F ≥ 2 = 0.79 (0.72–0.86), F ≥ 3 = 0.81 (0.73–0.89), F4 = 0.95 (0.90–1.0). sCD163 also predicted fibrosis well [F ≥ 2 = 0.71 (0.63–0.80), F ≥ 3 = 0.82 (0.74–0.90), F4 = 0.89 (0.76–1.0)]. However, combining sCD163 and TE did not improve the AUROCs significantly [F ≥ 2 = 0.79 (0.72–0.86), F ≥ 3 = 0.85 (0.78–0.92), F4 = 0.97 (0.93–1.0)]. In the Swedish cohort, TE showed a closer association with fibrosis (rho = 0.73, p < 0.001) than sCD163 (rho = 0.43, p < 0.001) and sMR (rho = 0.46, p < 0.001). TE predicted fibrosis well [F ≥ 2 = 0.88 (0.80–0.97), F ≥ 3 = 0.90 (0.83–0.97), F4 = 0.87 (0.78–0.96)], whereas sCD163 did not (best AUROC 0.75). sMR showed a better prediction [F ≥ 2 = 0.68 (0.56–0.81), F ≥ 3 = 0.82 (0.71–0.92), F4 = 0.79 (0.66–0.93)], but the addition of sMR did not further improve the prediction of fibrosis by TE.Conclusion: In these cohorts of MAFLD patients, TE was superior to macrophage markers for fibrosis prediction and in contrast to our hypothesis the addition of these markers to TE did not improve its predictive capability.

Validation of an artificial intelligence algorithm applied to a metabolic substrate analysis of urine for detection of urothelial cancer.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e16008-e16008
Author(s):  
Nikola Kaludov ◽  
Mohummad Minhaj Siddiqui ◽  
Max Kates ◽  
Hemantkumar Tripathi ◽  
Amatul Nasir Salma ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Predictive Value ◽  
Urothelial Cancer ◽  
Profile Analysis ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Data Points ◽  
Artificial Intelligence Algorithm ◽  
Insight Into ◽  
Made In

e16008 Background: Urine tests such as urine cytology are commonly used for the diagnosis and monitoring of urothelial cancer. These tests are often limited by issues related to sensitivity or specificity. It is well known that derangement of cellular metabolism is one of the hallmarks of carcinogenesis. As urothelial cancer is in constant contact with urine, we hypothesize that metabolite composition in the urine may provide insight into possible urothelial cancer presence in the urinary tract. In this study, we evaluated a metabolomics based urine test for the detection of urothelial cancer. Methods: In this prospective, multi-institutional IRB approved study, urine samples were collected from a total of 57 urothelial cancer patients and non-urothelial cancer controls. Gas chromatography profiles of urine small molecule metabolites were generated to yield over 2400 data points of metabolite peaks and troughs for every urine sample. A machine-learning based algorithm (Abilis Life Sciences) was constructed to predict urothelial cancer versus non-cancer controls through analysis of peaks and trough patterns of urine metabolomics profiles. Predictions were made in a blinded fashion and descriptive statistics of test sensitivity and specificity were generated. Results: The urine metabolite composition of 57 patients were analyzed and urothelial cancer predictions were generated. The test demonstrated an overall accuracy of 89.5% (51 out of 57 cases correctly predicted). The sensitivity of the test was 97.1% (34 out of 35) and specificity was 77.3% (17 out of 22). The Positive Predictive Value is 87.2%, while the Negative Predictive Value is 94.4%. The area under the curve for the receiver operating characteristic curve was 0.87. Conclusions: Urine based metabolic profile analysis using artificial intelligence algorithms is a promising potential diagnostic test for detection of urothelial cancer. Further testing is ongoing to increase robustness of the validation.

scholarly journals Artificial Intelligence Algorithm for Predicting Cardiac Arrest Using Electrocardiography

2020 ◽  
Author(s):  
Joon-myoung Kwon ◽  
Kyung-Hee Kim ◽  
Ki-Hyun Jeon ◽  
Soo Youn Lee ◽  
Jinsik Park ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Intensive Care Unit ◽  
Cardiac Arrest ◽  
Intensive Care ◽  
External Validation ◽  
High Risk Group ◽  
Validation Data ◽  
Intelligence Algorithm ◽  
Artificial Intelligence Algorithm ◽  
Intensive Care Unit Transfer

Abstract Background: In-hospital cardiac arrest is a major burden in health care. Although several track-and-trigger systems are used to predict cardiac arrest, they often have unsatisfactory performances. We hypothesized that a deep-learning-based artificial intelligence algorithm (DLA) could effectively predict cardiac arrest using electrocardiography (ECG). We developed and validated a DLA for predicting cardiac arrest using ECG. Methods: We conducted a retrospective study that included 47,505 ECGs of 25,672 adult patients admitted to two hospitals, who underwent at least one ECG from October 2016 to September 2019. The endpoint was occurrence of cardiac arrest within 24 hours from ECG. Using subgroup analyses in patients who were initially classified as non-event, we confirmed the delayed occurrence of cardiac arrest and unexpected intensive care unit transfer over 14 days.Results: We used 32,294 ECGs of 10,461 patients and 4,483 ECGs of 4,483 patients from a hospital were used as development and internal validation data, respectively. Additionally, 10,728 ECGs of 10,728 patients from another hospital were used as external validation data, which confirmed the robustness of the developed DLA. During internal and external validation, the areas under the receiver operating characteristic curves of the DLA in predicting cardiac arrest within 24 hours were 0.913 and 0.948, respectively. The high risk group of the DLA showed a significantly higher hazard for delayed cardiac arrest (5.74% vs. 0.33%, P < 0.001) and unexpected intensive care unit transfer (4.23% vs. 0.82%, P < 0.001). A sensitivity map of the DLA displayed the ECG regions used to predict cardiac arrest, with the DLA focused most on the QRS complex. Conclusions: Our DLA successfully predicted cardiac arrest using diverse formats of ECG. The results indicate that cardiac arrest could be screened and predicted not only with a conventional 12-lead ECG, but also with a single-lead ECG using a wearable device that employs our DLA.

scholarly journals Can Autism Be Diagnosed with Artificial Intelligence? A Narrative Review

Diagnostics ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2032
Author(s):  
Ahmad Chaddad ◽  
Jiali Li ◽  
Qizong Lu ◽  
Yujie Li ◽  
Idowu Paul Okuwobi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Texture Analysis ◽  
Molecular Data ◽  
Autism Spectrum ◽  
Invasive Technique ◽  
Non Invasive ◽  
Learning Techniques ◽  
Healthy Control ◽  
Aided Diagnosis

Radiomics with deep learning models have become popular in computer-aided diagnosis and have outperformed human experts on many clinical tasks. Specifically, radiomic models based on artificial intelligence (AI) are using medical data (i.e., images, molecular data, clinical variables, etc.) for predicting clinical tasks such as autism spectrum disorder (ASD). In this review, we summarized and discussed the radiomic techniques used for ASD analysis. Currently, the limited radiomic work of ASD is related to the variation of morphological features of brain thickness that is different from texture analysis. These techniques are based on imaging shape features that can be used with predictive models for predicting ASD. This review explores the progress of ASD-based radiomics with a brief description of ASD and the current non-invasive technique used to classify between ASD and healthy control (HC) subjects. With AI, new radiomic models using the deep learning techniques will be also described. To consider the texture analysis with deep CNNs, more investigations are suggested to be integrated with additional validation steps on various MRI sites.

scholarly journals Deep Learning Model for Screening Sepsis Using Electrocardiography

2021 ◽  
Author(s):  
Joon-myoung Kwon ◽  
Ye Rang Lee ◽  
Min-Seung Jung ◽  
Yoon-Ji Lee ◽  
Yong-Yeon Jo ◽  
...  
Keyword(s):  
Septic Shock ◽  
Deep Learning ◽  
Characteristic Curve ◽  
External Validation ◽  
Medical Emergency ◽  
Validation Dataset ◽  
Internal Validation ◽  
Significant Difference ◽  
Life Threatening ◽  
Sepsis And Septic Shock

Abstract Background: Sepsis is a life-threatening organ dysfunction and is a major healthcare burden worldwide. Although sepsis is a medical emergency that requires immediate management, it is difficult to screen the occurrence of sepsis. In this study, we propose an artificial intelligence based on deep learning-based model (DLM) for screening sepsis using electrocardiography (ECG).Methods: This retrospective cohort study included 46,017 patients who admitted to two hospitals. 1,548 and 639 patients underwent sepsis and septic shock. The DLM was developed using 73,727 ECGs of 18,142 patients and internal validation was conducted using 7,774 ECGs of 7,774 patients. Furthermore, we conducted an external validation with 20,101 ECGs of 20,101 patients from another hospital to verify the applicability of the DLM across centers.Results: During the internal and external validation, the area under the receiver operating characteristic curve (AUC) of an DLM using 12-lead ECG for screening sepsis were 0.901 (95% confidence interval 0.882–0.920) and 0.863 (0.846–0.879), respectively. During internal and external validation, AUC of an DLM for detecting septic shock were 0.906 (95% CI = 0.877–0.936) and 0.899 (95% CI = 0.872–0.925), respectively. The AUC of the DLM for detecting sepsis using 6-lead and single-lead ECGs were 0.845–0.882. A sensitivity map showed that the QRS complex and T wave was associated with sepsis. Subgroup analysis was conducted using ECGs from 4,609 patients who admitted with infectious disease, The AUC of the DLM for predicting in-hospital mortality was 0.817 (0.793–0.840). There was a significant difference in the prediction score of DLM using ECG according to the presence of infection in the validation dataset (0.277 vs 0.574, p<0.001), including severe acute respiratory syndrome coronavirus 2 (0.260 vs 0.725, p=0.018).Conclusions: The DLM demonstrated reasonable performance for screening sepsis using 12-, 6-, and single-lead ECG. The results suggest that sepsis can be screened using not only conventional ECG devices, but also diverse life-type ECG machine employing the DLM, thereby preventing irreversible disease progression and mortality.

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