scholarly journals A Clinical Evaluation Study of Cardiothoracic Ratio Measurement Using Artificial Intelligence

2021 ◽  
Author(s):  
Pairash Saiviroonporn ◽  
Suwimon Wonglaksanapimon ◽  
Warasinee Chaisangmongkon ◽  
Isarun Chamveha ◽  
Pakorn Yodprom ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Operating Time ◽  
Evaluation Study ◽  
Clinical Implementation ◽  
Single Model ◽  
Combined Model ◽  
Ratio Measurement ◽  
Manual Operation ◽  
Cardiothoracic Ratio ◽  
Evaluation Dataset

Abstract Background Artificial Intelligence, particularly the Deep Learning (DL) model, can provide reliable results for automated cardiothoracic ratio (CTR) measurement on Chest x-ray (CXR) images. In everyday clinical use, however, this technology is usually implemented in a non-automated (AI-assisted) capacity because it still requires approval from radiologists. We investigated the performance and efficiency of our recently proposed models for the AI-assisted method intended for clinical practice. Methods We validated four proposed DL models (AlbuNet, SegNet, VGG-11, and VGG-16) to find the best model for clinical implementation using a dataset of 7,517 CXR images from manual operations. These models were investigated in single-model and combined-model modes to find the model with the highest percentage of results where the user could accept the results without further interaction (excellent grade), and with measurement variation within ± 1.8% of the human-operating range. The best model from the validation study was then tested on an evaluation dataset of 9,386 CXR images using the AI-assisted method with two radiologists to measure the yield of excellent grade results, observer variation, and operating time. A Bland-Altman plot with coefficient of variation (CV) was employed to evaluate agreement between measurements. Results The VGG-16 gave the highest excellent grade result (68.9%) of any single-model mode with a CV comparable to manual operation (2.12% vs 2.13%). No DL model produced a failure-grade result. The combined-model mode of AlbuNet+VGG-11 model yielded excellent grades in 82.7% of images and a CV of 1.36%. Using the evaluation dataset, the AlbuNet+VGG-11 model produced excellent grade results in 77.8% of images, a CV of 1.55%, and reduced operating time by almost ten-fold (1.07 ± 2.62 secs vs 10.6 ± 1.5 sec) compared to manual operation. Conclusion Due to its exceptional accuracy and speed, the AlbuNet+VGG-11 model could be clinically implemented to assist radiologists with CTR measurement.

scholarly journals Cardiothoracic Ratio Measurement Using Artificial Intelligence: Observer and Method Validation Studies

2021 ◽  
Author(s):  
Pairash Saiviroonporn ◽  
Kanchanaporn Rodbangyang ◽  
Trongtum Tongdee ◽  
Warasinee Chaisangmongkon ◽  
Pakorn Yodprom ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Operation Time ◽  
Coefficient Of Determination ◽  
X Rays ◽  
Large Dataset ◽  
Ratio Measurement ◽  
Manual Operation ◽  
Cardiothoracic Ratio ◽  
Manual Methods ◽  
Classification Test

Abstract Background Artificial Intelligence (AI) technique for cardiothoracic ratio (CTR) measurement is a promising tool that has been technically validated but not clinically evaluated on a large dataset. This study observes and validates AI and manual methods for CTR measurement on a large dataset and investigates the clinical utility of the AI method. Results Five thousand normal chest x-rays and 2,517 images with cardiomegaly and CTR values, were analyzed using manual, AI-assisted, and AI only methods. AI methods obtained CTR values from a VGG-16 U-Net model. An in-house software was used to aid the study and to record measurement time. Intra and inter-observer experiments were performed on manual and AI-assisted methods and the average of each method was employed in a method variation study. AI outcomes were graded in the AI-assisted method as excellent (accepted by both users independently), good (required adjustment), and poor (failed outcome). Bland-Altman plot with coefficient of variation (CV), and coefficient of determination (R-squared) were employed to evaluate agreement and correlation between measurements. Finally, the performance of a cardiomegaly classification test was evaluated using a CTR cutoff at the standard (0.5), optimum, and maximum sensitivity. Manual CTR measurements on cardiomegaly data were comparable to the previous radiologist reports (CV of 2.13% vs 2.04%). The observer and method variations from the AI method were about three times higher than from the manual method (CV of 5.78% vs 2.13%). AI assistance resulted in 40% excellent, 56% good, and 4% poor grading. AI assistance significantly improved agreement on inter-observer measurement compared to manual methods (CV; bias: 1.72%; -0.61% vs 2.13%; -1.62%) and was faster to perform (2.2 ± 2.4 secs vs 10.6 ± 1.5 secs). R-squared and classification-test were not reliable indicators to verify that the AI method could replace manual operation. Conclusion AI alone is not suitable to replace manual operation due to its high variation, but it is useful to assist the radiologist because it can reduce observer variation and operation time. Agreement of measurement should be used to compare AI and manual methods, rather than R-square or classification performance tests.

scholarly journals Cardiothoracic ratio measurement using artificial intelligence: observer and method validation studies

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pairash Saiviroonporn ◽  
Kanchanaporn Rodbangyang ◽  
Trongtum Tongdee ◽  
Warasinee Chaisangmongkon ◽  
Pakorn Yodprom ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Operating Time ◽  
Operation Time ◽  
Coefficient Of Determination ◽  
X Rays ◽  
Large Dataset ◽  
Ratio Measurement ◽  
Cardiothoracic Ratio ◽  
Manual Methods ◽  
Classification Test

Abstract Background Artificial Intelligence (AI) is a promising tool for cardiothoracic ratio (CTR) measurement that has been technically validated but not clinically evaluated on a large dataset. We observed and validated AI and manual methods for CTR measurement using a large dataset and investigated the clinical utility of the AI method. Methods Five thousand normal chest x-rays and 2,517 images with cardiomegaly and CTR values, were analyzed using manual, AI-assisted, and AI-only methods. AI-only methods obtained CTR values from a VGG-16 U-Net model. An in-house software was used to aid the manual and AI-assisted measurements and to record operating time. Intra and inter-observer experiments were performed on manual and AI-assisted methods and the averages were used in a method variation study. AI outcomes were graded in the AI-assisted method as excellent (accepted by both users independently), good (required adjustment), and poor (failed outcome). Bland–Altman plot with coefficient of variation (CV), and coefficient of determination (R-squared) were used to evaluate agreement and correlation between measurements. Finally, the performance of a cardiomegaly classification test was evaluated using a CTR cutoff at the standard (0.5), optimum, and maximum sensitivity. Results Manual CTR measurements on cardiomegaly data were comparable to previous radiologist reports (CV of 2.13% vs 2.04%). The observer and method variations from the AI-only method were about three times higher than from the manual method (CV of 5.78% vs 2.13%). AI assistance resulted in 40% excellent, 56% good, and 4% poor grading. AI assistance significantly improved agreement on inter-observer measurement compared to manual methods (CV; bias: 1.72%; − 0.61% vs 2.13%; − 1.62%) and was faster to perform (2.2 ± 2.4 secs vs 10.6 ± 1.5 secs). The R-squared and classification-test were not reliable indicators to verify that the AI-only method could replace manual operation. Conclusions AI alone is not yet suitable to replace manual operations due to its high variation, but it is useful to assist the radiologist because it can reduce observer variation and operation time. Agreement of measurement should be used to compare AI and manual methods, rather than R-square or classification performance tests.

Combined Method of Artificial Intelligence Regression Forecasting Models Under Fluctuating Errors

2019 ◽  
Vol 28 (05) ◽  
pp. 1950018
Author(s):  
Hongjiu Liu ◽  
Qingyang Liu ◽  
Yanrong Hu
Keyword(s):  
Artificial Intelligence ◽  
Combined Method ◽  
Model Combination ◽  
Single Model ◽  
Combined Model ◽  
Regression Methods ◽  
Forecasting Models ◽  
Improve Accuracy ◽  
Combined Models ◽  
Artificial Intelligence Regression

Because precisions of different multiple-regression methods are different for the same samples, how to improve accuracy of forecasting has therefore generated wide interest. This paper attempted to improve precision of forecast by combining multiple linear regression and three artificial intelligence regressive models. In our study, a novel frame of model combination is proposed by fluctuating degree, complementarity and compatibility. Complementarity is available to judge which models can be combined to decease the errors and establish its sets. The assigned weights of each single model in complementary sets are calculated by fluctuating degree. Compatibility, superiors and inferiors of a combined model are evaluated by MAE, RMSE and MAPE. The empirical case of predicting electric demand demonstrated that the combined models based on fluctuating degree increase predicting precision of sample period and extrapolative forecast if there exists complement between different single-models.

The Artificial Intelligence Doctor: Considerations for the Clinical Implementation of Ethical AI

2021 ◽  
pp. 257-261
Author(s):  
Julius M. Kernbach ◽  
Karlijn Hakvoort ◽  
Jonas Ort ◽  
Hans Clusmann ◽  
Georg Neuloh ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Implementation

scholarly journals Multiresolution Application of Artificial Intelligence in Digital Pathology for Prediction of Positive Lymph Nodes From Primary Tumors in Bladder Cancer

2020 ◽  
pp. 367-382 ◽  
Author(s):  
Stephanie A. Harmon ◽  
Thomas H. Sanford ◽  
G. Thomas Brown ◽  
Chris Yang ◽  
Sherif Mehralivand ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Digital Pathology ◽  
Area Under The Curve ◽  
The Cancer Genome Atlas ◽  
Clinicopathologic Features ◽  
Combined Model ◽  
Primary Tumors ◽  
Logistic Regression Models ◽  
Spatially Resolved ◽  
Digital Evaluation

PURPOSE To develop an artificial intelligence (AI)–based model for identifying patients with lymph node (LN) metastasis based on digital evaluation of primary tumors and train the model using cystectomy specimens available from The Cancer Genome Atlas (TCGA) Project; patients from our institution were included for validation of the leave-out test cohort. METHODS In all, 307 patients were identified for inclusion in the study (TCGA, n = 294; in-house, n = 13). Deep learning models were trained from image patches at 2.5×, 5×, 10×, and 20× magnifications, and spatially resolved prediction maps were combined with microenvironment (lymphocyte infiltration) features to derive a final patient-level AI score (probability of LN metastasis). Training and validation included 219 patients (training, n = 146; validation, n = 73); 89 patients (TCGA, n = 75; in-house, n = 13) were reserved as an independent testing set. Multivariable logistic regression models for predicting LN status based on clinicopathologic features alone and a combined model with AI score were fit to training and validation sets. RESULTS Several patients were determined to have positive LN metastasis in TCGA (n = 105; 35.7%) and in-house (n = 3; 23.1%) cohorts. A clinicopathologic model that considered using factors such as age, T stage, and lymphovascular invasion demonstrated an area under the curve (AUC) of 0.755 (95% CI, 0.680 to 0.831) in the training and validation cohorts compared with the cross validation of the AI score (likelihood of positive LNs), which achieved an AUC of 0.866 (95% CI, 0.812 to 0.920; P = .021). Performance in the test cohort was similar, with a clinicopathologic model AUC of 0.678 (95% CI, 0.554 to 0.802) and an AI score of 0.784 (95% CI, 0.702 to 0.896; P = .21). In addition, the AI score remained significant after adjusting for clinicopathologic variables ( P = 1.08 × 10−9), and the combined model significantly outperformed clinicopathologic features alone in the test cohort with an AUC of 0.807 (95% CI, 0.702 to 0.912; P = .047). CONCLUSION Patients who are at higher risk of having positive LNs during cystectomy can be identified on primary tumor samples using novel AI-based methodologies applied to digital hematoxylin and eosin–stained slides.

scholarly journals P071: Artificial intelligence in emergency medicine: A scoping review

CJEM ◽  
2020 ◽  
Vol 22 (S1) ◽  
pp. S90-S90
Author(s):  
A. Kirubarajan ◽  
A. Taher ◽  
S. Khan ◽  
S. Masood
Keyword(s):  
Artificial Intelligence ◽  
Emergency Medicine ◽  
Language Processing ◽  
Scoping Review ◽  
Clinical Decision Making ◽  
Data Extraction ◽  
A Priori ◽  
Clinical Decision ◽  
Hospital Environment ◽  
Clinical Implementation

Introduction: The study of artificial intelligence (AI) in medicine has become increasingly popular over the last decade. The emergency department (ED) is uniquely situated to benefit from AI due to its power of diagnostic prediction, and its ability to continuously improve with time. However, there is a lack of understanding of the breadth and scope of AI applications in emergency medicine, and evidence supporting its use. Methods: Our scoping review was completed according to PRISMA-ScR guidelines and was published a priori on Open Science Forum. We systematically searched databases (Medline-OVID, EMBASE, CINAHL, and IEEE) for AI interventions relevant to the ED. Study selection and data extraction was performed independently by two investigators. We categorized studies based on type of AI model used, location of intervention, clinical focus, intervention sub-type, and type of comparator. Results: Of the 1483 original database citations, a total of 181 studies were included in the scoping review. Inter-rater reliability for study screening for titles and abstracts was 89.1%, and for full-text review was 77.8%. Overall, we found that 44 (24.3%) studies utilized supervised learning, 63 (34.8%) studies evaluated unsupervised learning, and 13 (7.2%) studies utilized natural language processing. 17 (9.4%) studies were conducted in the pre-hospital environment, with the remainder occurring either in the ED or the trauma bay. The majority of interventions centered around prediction (n = 73, 40.3%). 48 studies (25.5%) analyzed AI interventions for diagnosis. 23 (12.7%) interventions focused on diagnostic imaging. 89 (49.2%) studies did not have a comparator to their AI intervention. 63 (34.8%) studies used statistical models as a comparator, 19 (10.5%) of which were clinical decision making tools. 15 (8.3%) studies used humans as comparators, with 12 of the 15 (80%) studies showing superiority in favour of the AI intervention when compared to a human. Conclusion: AI-related research is rapidly increasing in emergency medicine. AI interventions are heterogeneous in both purpose and design, but primarily focus on predictive modeling. Most studies do not involve a human comparator and lack information on patient-oriented outcomes. While some studies show promising results for AI-based interventions, there remains uncertainty regarding their superiority over standard practice, and further research is needed prior to clinical implementation.

scholarly journals Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine

Cancers ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3532 ◽  
Author(s):  
Ryuji Hamamoto ◽  
Kruthi Suvarna ◽  
Masayoshi Yamada ◽  
Kazuma Kobayashi ◽  
Norio Shinkai ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Precision Medicine ◽  
Medical Devices ◽  
The United States ◽  
Medical Data ◽  
Clinical Implementation ◽  
Medical Field ◽  
Core Technology ◽  
Artificial Intelligence Technology ◽  
History Of

In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI technology in the medical field. More than 60 AI-equipped medical devices have already been approved by the Food and Drug Administration (FDA) in the United States, and the active introduction of AI technology is considered to be an inevitable trend in the future of medicine. In the field of oncology, clinical applications of medical devices using AI technology are already underway, mainly in radiology, and AI technology is expected to be positioned as an important core technology. In particular, “precision medicine,” a medical treatment that selects the most appropriate treatment for each patient based on a vast amount of medical data such as genome information, has become a worldwide trend; AI technology is expected to be utilized in the process of extracting truly useful information from a large amount of medical data and applying it to diagnosis and treatment. In this review, we would like to introduce the history of AI technology and the current state of medical AI, especially in the oncology field, as well as discuss the possibilities and challenges of AI technology in the medical field.

Assembly Line Re-Balancing Using Ranked Positional Weight Technique and Longest Operating Time Technique: A Comparative Analysis

2013 ◽  
Vol 824 ◽  
pp. 568-578 ◽  
Author(s):  
Ralph O. Edokpia ◽  
F.U. Owu
Keyword(s):  
Large Scale ◽  
Assembly Line ◽  
Production Systems ◽  
Operating Cost ◽  
Operating Time ◽  
Assembly Line Balancing ◽  
Line Balancing ◽  
Single Model ◽  
Minimum Number ◽  
Positional Weight

Assembly line balancing is an attractive means of mass manufacturing and large-scale serial production systems. Traditionally, assembly lines are arranged in straight single-model lines and the problem is known as Simple Assembly Line balancing problem (SALBP). In this study, two heuristic assembly line balancing techniques known as the Ranked Positional Weight Technique, and the longest operational time technique, were applied to solve the problem of single-model line balancing problem in an assembling company with the aim of comparing the efficiencies of the application of the two algorithms. By using both methods, different restrictions were taken into consideration and two different lines balancing results were obtained. From the results obtained, Longest Operating Time Technique has higher line efficiency (85.16%) as compared to Ranked positional weight technique (79.28%) and it is easy to apply. The LOT technique gave the minimum number of workstations (27) as compared to the RPW technique (29); however the line efficiency and the number of workstation of the existing line are 74.67% and 31 respectively. This implies that the LOT technique has a better reduction in operating cost.

scholarly journals Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology

2020 ◽  
Vol 9 (3) ◽  
pp. 146-154
Author(s):  
Rutger R van de Leur ◽  
Machteld J Boonstra ◽  
Ayoub Bagheri ◽  
Rob W Roudijk ◽  
Arjan Sammani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
External Validity ◽  
Clinical Performance ◽  
Model Performance ◽  
Clinical Implementation ◽  
Ethical Aspects ◽  
Electrophysiological Studies ◽  
Clinical Benefits ◽  
Diagnostic Modalities

The combination of big data and artificial intelligence (AI) is having an increasing impact on the field of electrophysiology. Algorithms are created to improve the automated diagnosis of clinical ECGs or ambulatory rhythm devices. Furthermore, the use of AI during invasive electrophysiological studies or combining several diagnostic modalities into AI algorithms to aid diagnostics are being investigated. However, the clinical performance and applicability of created algorithms are yet unknown. In this narrative review, opportunities and threats of AI in the field of electrophysiology are described, mainly focusing on ECGs. Current opportunities are discussed with their potential clinical benefits as well as the challenges. Challenges in data acquisition, model performance, (external) validity, clinical implementation, algorithm interpretation as well as the ethical aspects of AI research are discussed. This article aims to guide clinicians in the evaluation of new AI applications for electrophysiology before their clinical implementation.

Artificial Intelligence in Lung Cancer: Bridging the Gap Between Computational Power and Clinical Decision-Making

2020 ◽  
pp. 084653712094143
Author(s):  
Jaryd R. Christie ◽  
Pencilla Lang ◽  
Lauren M. Zelko ◽  
David A. Palma ◽  
Mohamed Abdelrazek ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Biomarker Discovery ◽  
Surgical Techniques ◽  
Clinical Decision ◽  
Clinical Implementation ◽  
Cancer Management

Lung cancer remains the most common cause of cancer death worldwide. Recent advances in lung cancer screening, radiotherapy, surgical techniques, and systemic therapy have led to increasing complexity in diagnosis, treatment decision-making, and assessment of recurrence. Artificial intelligence (AI)–based prediction models are being developed to address these issues and may have a future role in screening, diagnosis, treatment selection, and decision-making around salvage therapy. Imaging plays an essential role in all components of lung cancer management and has the potential to play a key role in AI applications. Artificial intelligence has demonstrated value in prognostic biomarker discovery in lung cancer diagnosis, treatment, and response assessment, putting it at the forefront of the next phase of personalized medicine. However, although exploratory studies demonstrate potential utility, there is a need for rigorous validation and standardization before AI can be utilized in clinical decision-making. In this review, we will provide a summary of the current literature implementing AI for outcome prediction in lung cancer. We will describe the anticipated impact of AI on the management of patients with lung cancer and discuss the challenges of clinical implementation of these techniques.

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