scholarly journals An AI-Based Algorithm for the Automatic Classification of Thoracic Radiographs in Cats

2021 ◽  
Vol 8 ◽  
Author(s):  
Tommaso Banzato ◽  
Marek Wodzinski ◽  
Federico Tauceri ◽  
Chiara Donà ◽  
Filippo Scavazza ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Pleural Effusion ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Computer Aided Detection ◽  
Radiographic Findings ◽  
Computer Aided ◽  
Operating Characteristic Curve

An artificial intelligence (AI)-based computer-aided detection (CAD) algorithm to detect some of the most common radiographic findings in the feline thorax was developed and tested. The database used for training comprised radiographs acquired at two different institutions. Only correctly exposed and positioned radiographs were included in the database used for training. The presence of several radiographic findings was recorded. Consequenly, the radiographic findings included for training were: no findings, bronchial pattern, pleural effusion, mass, alveolar pattern, pneumothorax, cardiomegaly. Multi-label convolutional neural networks (CNNs) were used to develop the CAD algorithm, and the performance of two different CNN architectures, ResNet 50 and Inception V3, was compared. Both architectures had an area under the receiver operating characteristic curve (AUC) above 0.9 for alveolar pattern, bronchial pattern and pleural effusion, an AUC above 0.8 for no findings and pneumothorax, and an AUC above 0.7 for cardiomegaly. The AUC for mass was low (above 0.5) for both architectures. No significant differences were evident in the diagnostic accuracy of either architecture.

scholarly journals Development of an Artificial Intelligence–Based Automated Recommendation System for Clinical Laboratory Tests: Retrospective Analysis of the National Health Insurance Database

10.2196/24163 ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. e24163
Author(s):  
Md Mohaimenul Islam ◽  
Hsuan-Chia Yang ◽  
Tahmina Nasrin Poly ◽  
Yu-Chuan Jack Li
Keyword(s):  
Artificial Intelligence ◽  
Patient Safety ◽  
Health Insurance ◽  
Laboratory Test ◽  
Laboratory Tests ◽  
Operating Characteristic ◽  
Recommendation System ◽  
Characteristic Curve ◽  
Operating Characteristic Curve ◽  
Insurance Database

Background Laboratory tests are considered an essential part of patient safety as patients’ screening, diagnosis, and follow-up are solely based on laboratory tests. Diagnosis of patients could be wrong, missed, or delayed if laboratory tests are performed erroneously. However, recognizing the value of correct laboratory test ordering remains underestimated by policymakers and clinicians. Nowadays, artificial intelligence methods such as machine learning and deep learning (DL) have been extensively used as powerful tools for pattern recognition in large data sets. Therefore, developing an automated laboratory test recommendation tool using available data from electronic health records (EHRs) could support current clinical practice. Objective The objective of this study was to develop an artificial intelligence–based automated model that can provide laboratory tests recommendation based on simple variables available in EHRs. Methods A retrospective analysis of the National Health Insurance database between January 1, 2013, and December 31, 2013, was performed. We reviewed the record of all patients who visited the cardiology department at least once and were prescribed laboratory tests. The data set was split into training and testing sets (80:20) to develop the DL model. In the internal validation, 25% of data were randomly selected from the training set to evaluate the performance of this model. Results We used the area under the receiver operating characteristic curve, precision, recall, and hamming loss as comparative measures. A total of 129,938 prescriptions were used in our model. The DL-based automated recommendation system for laboratory tests achieved a significantly higher area under the receiver operating characteristic curve (AUROCmacro and AUROCmicro of 0.76 and 0.87, respectively). Using a low cutoff, the model identified appropriate laboratory tests with 99% sensitivity. Conclusions The developed artificial intelligence model based on DL exhibited good discriminative capability for predicting laboratory tests using routinely collected EHR data. Utilization of DL approaches can facilitate optimal laboratory test selection for patients, which may in turn improve patient safety. However, future study is recommended to assess the cost-effectiveness for implementing this model in real-world clinical settings.

Survivability prediction of colon cancer patients using neural networks

2017 ◽  
Vol 25 (3) ◽  
pp. 878-891 ◽  
Author(s):  
Reda Al-Bahrani ◽  
Ankit Agrawal ◽  
Alok Choudhary
Keyword(s):  
Neural Networks ◽  
Colon Cancer ◽  
Deep Neural Networks ◽  
Operating Characteristic ◽  
Prediction Models ◽  
Patient Survival ◽  
Characteristic Curve ◽  
Conditional Survival ◽  
Operating Characteristic Curve ◽  
Multiple Neural Network

We utilize deep neural networks to develop prediction models for patient survival and conditional survival of colon cancer. Our models are trained and validated on data obtained from the Surveillance, Epidemiology, and End Results Program. We provide an online outcome calculator for 1, 2, and 5 years survival periods. We experimented with multiple neural network structures and found that a network with five hidden layers produces the best results for these data. Moreover, the online outcome calculator provides conditional survival of 1, 2, and 5 years after surviving the mentioned survival periods. In this article, we report an approximate 0.87 area under the receiver operating characteristic curve measurements, higher than the 0.85 reported by Stojadinovic et al.

scholarly journals Development of an Artificial Intelligence–Based Automated Recommendation System for Clinical Laboratory Tests: Retrospective Analysis of the National Health Insurance Database (Preprint)

2020 ◽  
Author(s):  
Md Mohaimenul Islam ◽  
Hsuan-Chia Yang ◽  
Tahmina Nasrin Poly ◽  
Yu-Chuan Jack Li
Keyword(s):  
Artificial Intelligence ◽  
Patient Safety ◽  
Health Insurance ◽  
Laboratory Test ◽  
Laboratory Tests ◽  
Operating Characteristic ◽  
Recommendation System ◽  
Characteristic Curve ◽  
Operating Characteristic Curve ◽  
Insurance Database

BACKGROUND Laboratory tests are considered an essential part of patient safety as patients’ screening, diagnosis, and follow-up are solely based on laboratory tests. Diagnosis of patients could be wrong, missed, or delayed if laboratory tests are performed erroneously. However, recognizing the value of correct laboratory test ordering remains underestimated by policymakers and clinicians. Nowadays, artificial intelligence methods such as machine learning and deep learning (DL) have been extensively used as powerful tools for pattern recognition in large data sets. Therefore, developing an automated laboratory test recommendation tool using available data from electronic health records (EHRs) could support current clinical practice. OBJECTIVE The objective of this study was to develop an artificial intelligence–based automated model that can provide laboratory tests recommendation based on simple variables available in EHRs. METHODS A retrospective analysis of the National Health Insurance database between January 1, 2013, and December 31, 2013, was performed. We reviewed the record of all patients who visited the cardiology department at least once and were prescribed laboratory tests. The data set was split into training and testing sets (80:20) to develop the DL model. In the internal validation, 25% of data were randomly selected from the training set to evaluate the performance of this model. RESULTS We used the area under the receiver operating characteristic curve, precision, recall, and hamming loss as comparative measures. A total of 129,938 prescriptions were used in our model. The DL-based automated recommendation system for laboratory tests achieved a significantly higher area under the receiver operating characteristic curve (AUROCmacro and AUROCmicro of 0.76 and 0.87, respectively). Using a low cutoff, the model identified appropriate laboratory tests with 99% sensitivity. CONCLUSIONS The developed artificial intelligence model based on DL exhibited good discriminative capability for predicting laboratory tests using routinely collected EHR data. Utilization of DL approaches can facilitate optimal laboratory test selection for patients, which may in turn improve patient safety. However, future study is recommended to assess the cost-effectiveness for implementing this model in real-world clinical settings.

scholarly journals Microseismic Signal Classification Based on Artificial Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Chong-wei Xin ◽  
Fu-xing Jiang ◽  
Guo-dong Jin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Activation Function ◽  
Data Set ◽  
Waveform Data ◽  
Artificial Neural ◽  
Microseismic Signal

The classification of multichannel microseismic waveform is essential for real-time monitoring and hazard prediction. The accuracy and efficiency could not be guaranteed by manual identification. Thus, based on 37310 waveform data of Junde Coal Mine, eight features of statistics, spectrum, and waveform were extracted to generate a complete data set. An automatic classification algorithm based on artificial neural networks (ANNs) has been proposed. The model presented an excellent performance in identifying three preclassified signals in the test set. Operated with two hidden layers and the Logistic activation function, the multiclass area under the receiver operating characteristic curve (AUC) reached 98.6%.

scholarly journals The Role of Corneal Biomechanics for the Evaluation of Ectasia Patients

2020 ◽  
Vol 17 (6) ◽  
pp. 2113 ◽  
Author(s):  
Marcella Q. Salomão ◽  
Ana Luisa Hofling-Lima ◽  
Louise Pellegrino Gomes Esporcatte ◽  
Bernardo Lopes ◽  
Riccardo Vinciguerra ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Corneal Biomechanics ◽  
Healthy Group ◽  
Biomechanical Parameter ◽  
Operating Characteristic Curve ◽  
Corneal Tomography ◽  
Fellow Eyes

Purpose: To review the role of corneal biomechanics for the clinical evaluation of patients with ectatic corneal diseases. Methods: A total of 1295 eyes were included for analysis in this study. The normal healthy group (group N) included one eye randomly selected from 736 patients with healthy corneas, the keratoconus group (group KC) included one eye randomly selected from 321 patients with keratoconus. The 113 nonoperated ectatic eyes from 125 patients with very asymmetric ectasia (group VAE-E), whose fellow eyes presented relatively normal topography (group VAE-NT), were also included. The parameters from corneal tomography and biomechanics were obtained using the Pentacam HR and Corvis ST (Oculus Optikgeräte GmbH, Wetzlar, Germany). The accuracies of the tested variables for distinguishing all cases (KC, VAE-E, and VAE-NT), for detecting clinical ectasia (KC + VAE-E) and for identifying abnormalities among the VAE-NT, were investigated. A comparison was performed considering the areas under the receiver operating characteristic curve (AUC; DeLong’s method). Results: Considering all cases (KC, VAE-E, and VAE-NT), the AUC of the tomographic-biomechanical parameter (TBI) was 0.992, which was statistically higher than all individual parameters (DeLong’s; p < 0.05): PRFI- Pentacam Random Forest Index (0.982), BAD-D- Belin -Ambrosio D value (0.959), CBI -corneal biomechanical index (0.91), and IS Abs- Inferior-superior value (0.91). The AUC of the TBI for detecting clinical ectasia (KC + VAE-E) was 0.999, and this was again statistically higher than all parameters (DeLong’s; p < 0.05): PRFI (0.996), BAD-D (0.995), CBI (0.949), and IS Abs (0.977). Considering the VAE-NT group, the AUC of the TBI was 0.966, which was also statistically higher than all parameters (DeLong’s; p < 0.05): PRFI (0.934), BAD- D (0.834), CBI (0.774), and IS Abs (0.677). Conclusions: Corneal biomechanical data enhances the evaluation of patients with corneal ectasia and meaningfully adds to the multimodal diagnostic armamentarium. The integration of biomechanical data and corneal tomography with artificial intelligence data augments the sensitivity and specificity for screening and enhancing early diagnosis. Besides, corneal biomechanics may be relevant for determining the prognosis and staging the disease.

scholarly journals Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea

2020 ◽  
Vol 13 (8) ◽  
Author(s):  
Demilade Adedinsewo ◽  
Rickey E. Carter ◽  
Zachi Attia ◽  
Patrick Johnson ◽  
Anthony H. Kashou ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Sensitivity And Specificity ◽  
Operating Characteristic ◽  
Left Ventricular Systolic Dysfunction ◽  
Characteristic Curve ◽  
Left Ventricular ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Background: Identification of systolic heart failure among patients presenting to the emergency department (ED) with acute dyspnea is challenging. The reasons for dyspnea are often multifactorial. A focused physical evaluation and diagnostic testing can lack sensitivity and specificity. The objective of this study was to assess the accuracy of an artificial intelligence-enabled ECG to identify patients presenting with dyspnea who have left ventricular systolic dysfunction (LVSD). Methods: We retrospectively applied a validated artificial intelligence-enabled ECG algorithm for the identification of LVSD (defined as LV ejection fraction ≤35%) to a cohort of patients aged ≥18 years who were evaluated in the ED at a Mayo Clinic site with dyspnea. Patients were included if they had at least one standard 12-lead ECG acquired on the date of the ED visit and an echocardiogram performed within 30 days of presentation. Patients with prior LVSD were excluded. We assessed the model performance using area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity. Results: A total of 1606 patients were included. Median time from ECG to echocardiogram was 1 day (Q1: 1, Q3: 2). The artificial intelligence-enabled ECG algorithm identified LVSD with an area under the receiver operating characteristic curve of 0.89 (95% CI, 0.86–0.91) and accuracy of 85.9%. Sensitivity, specificity, negative predictive value, and positive predictive value were 74%, 87%, 97%, and 40%, respectively. To identify an ejection fraction <50%, the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were 0.85 (95% CI, 0.83–0.88), 86%, 63%, and 91%, respectively. NT-proBNP (N-terminal pro-B-type natriuretic peptide) alone at a cutoff of >800 identified LVSD with an area under the receiver operating characteristic curve of 0.80 (95% CI, 0.76–0.84). Conclusions: The ECG is an inexpensive, ubiquitous, painless test which can be quickly obtained in the ED. It effectively identifies LVSD in selected patients presenting to the ED with dyspnea when analyzed with artificial intelligence and outperforms NT-proBNP. Graphic Abstract: A graphic abstract is available for this article.

Clinical application of artificial intelligence-assisted diagnosis using anteroposterior pelvic radiographs in children with developmental dysplasia of the hip

2020 ◽  
Vol 102-B (11) ◽  
pp. 1574-1581
Author(s):  
Si-Cheng Zhang ◽  
Jun Sun ◽  
Chuan-Bin Liu ◽  
Ji-Hong Fang ◽  
Hong-Tao Xie ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Receiver Operating Characteristic Curve ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Developmental Dysplasia ◽  
Learning System ◽  
Pelvic Radiographs ◽  
Operating Characteristic Curve ◽  
Dysplasia Of The Hip

Aims The diagnosis of developmental dysplasia of the hip (DDH) is challenging owing to extensive variation in paediatric pelvic anatomy. Artificial intelligence (AI) may represent an effective diagnostic tool for DDH. Here, we aimed to develop an anteroposterior pelvic radiograph deep learning system for diagnosing DDH in children and analyze the feasibility of its application. Methods In total, 10,219 anteroposterior pelvic radiographs were retrospectively collected from April 2014 to December 2018. Clinicians labelled each radiograph using a uniform standard method. Radiographs were grouped according to age and into ‘dislocation’ (dislocation and subluxation) and ‘non-dislocation’ (normal cases and those with dysplasia of the acetabulum) groups based on clinical diagnosis. The deep learning system was trained and optimized using 9,081 radiographs; 1,138 test radiographs were then used to compare the diagnoses made by deep learning system and clinicians. The accuracy of the deep learning system was determined using a receiver operating characteristic curve, and the consistency of acetabular index measurements was evaluated using Bland-Altman plots. Results In all, 1,138 patients (242 males; 896 females; mean age 1.5 years (SD 1.79; 0 to 10) were included in this study. The area under the receiver operating characteristic curve, sensitivity, and specificity of the deep learning system for diagnosing hip dislocation were 0.975, 276/289 (95.5%), and 1,978/1,987 (99.5%), respectively. Compared with clinical diagnoses, the Bland-Altman 95% limits of agreement for acetabular index, as determined by the deep learning system from the radiographs of non-dislocated and dislocated hips, were -3.27° - 2.94° and -7.36° - 5.36°, respectively (p < 0.001). Conclusion The deep learning system was highly consistent, more convenient, and more effective for diagnosing DDH compared with clinician-led diagnoses. Deep learning systems should be considered for analysis of anteroposterior pelvic radiographs when diagnosing DDH. The deep learning system will improve the current artificially complicated screening referral process. Cite this article: Bone Joint J 2020;102-B(11):1574–1581.

Computer-aided detection for tuberculosis and silicosis in chest radiographs of gold miners of South Africa

2020 ◽  
Vol 24 (4) ◽  
pp. 444-451
Author(s):  
C. Young ◽  
S. Barker ◽  
R. Ehrlich ◽  
B. Kistnasamy ◽  
A. Yassi
Keyword(s):  
Disease Burden ◽  
Operating Characteristic ◽  
Screening Tool ◽  
Characteristic Curve ◽  
Chest Radiographs ◽  
Computer Aided Detection ◽  
High Incidence ◽  
Computer Aided ◽  
Gold Miners ◽  
Compensation Claims

BACKGROUND: For over one hundred years, the gold mining sector has been a considerable source of tuberculosis (TB) and silicosis disease burden across Southern Africa. Reading chest radiographs (CXRs) is an expert and time-intensive process necessary for the screening and diagnosis of lung disease and the provision of evidence for compensation claims. Our study explores the use of computer-aided detection (CAD) of TB and silicosis in CXRs of a population with a high incidence of both diseases.METHODS: A set of 330 CXRs with human expert-determined classifications of silicosis, TB, silcotuberculosis and normal were provided to four health technology companies. The ability of each of their respective CAD systems to predict disease was assessed using receiver operating characteristic curve analysis of the under the curve metric.RESULTS: Three of the four systems differentiated accurately between TB and normal images, while two differentiated accurately between silicosis and normal images. Inclusion of silicotuberculosis images reduced each system's ability to detect either disease. In differentiating between any abnormal from normal CXR, the most accurate system achieved both a sensitivity and specificity of 98.2%.CONCLUSION: The current ability of CAD to differentiate between TB and silicosis is limited, but its use as a mass screening tool for both diseases shows considerable promise.

Dual-energy three compartment breast imaging (3CB) for novel compositional biomarkers to improve detection of malignant lesions

2021 ◽  
Author(s):  
Lambert Leong ◽  
Serghei Malkov ◽  
Karen Drukker ◽  
Bethany Niell ◽  
Peter Sadowski ◽  
...  
Keyword(s):  
Breast Imaging ◽  
Operating Characteristic ◽  
Imaging Technique ◽  
Characteristic Curve ◽  
Dual Energy ◽  
Imaging Biomarkers ◽  
Breast Cancers ◽  
Computer Aided Detection ◽  
Malignant Lesions ◽  
Operating Characteristic Curve

Abstract We explore a compositional breast imaging technique known as three compartment breast (3CB) to improve malignancy detection. The addition of 3CB compositional information to computer-aided detection (CAD) software improved malignancy predictions resulting in an area under the receiver operating characteristic curve (AUC) of 0.81 (confidence interval (CI) of 0.74-0.88) on a held-out test set, while CAD software alone achieved an AUC of 0.69 (CI 0.60-0.78). We also identified that invasive breast cancers have a unique compositional signature characterized by reduced lipid content and increased water and protein content when compared to surrounding tissues. Clinically, 3CB may potentially provide increased accuracy in predicting malignancy and a feasible avenue to explore compositional breast imaging biomarkers.

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