1
|
Aromiwura AA, Settle T, Umer M, Joshi J, Shotwell M, Mattumpuram J, Vorla M, Sztukowska M, Contractor S, Amini A, Kalra DK. Artificial intelligence in cardiac computed tomography. Prog Cardiovasc Dis 2023; 81:54-77. [PMID: 37689230 DOI: 10.1016/j.pcad.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 09/04/2023] [Indexed: 09/11/2023]
Abstract
Artificial Intelligence (AI) is a broad discipline of computer science and engineering. Modern application of AI encompasses intelligent models and algorithms for automated data analysis and processing, data generation, and prediction with applications in visual perception, speech understanding, and language translation. AI in healthcare uses machine learning (ML) and other predictive analytical techniques to help sort through vast amounts of data and generate outputs that aid in diagnosis, clinical decision support, workflow automation, and prognostication. Coronary computed tomography angiography (CCTA) is an ideal union for these applications due to vast amounts of data generation and analysis during cardiac segmentation, coronary calcium scoring, plaque quantification, adipose tissue quantification, peri-operative planning, fractional flow reserve quantification, and cardiac event prediction. In the past 5 years, there has been an exponential increase in the number of studies exploring the use of AI for cardiac computed tomography (CT) image acquisition, de-noising, analysis, and prognosis. Beyond image processing, AI has also been applied to improve the imaging workflow in areas such as patient scheduling, urgent result notification, report generation, and report communication. In this review, we discuss algorithms applicable to AI and radiomic analysis; we then present a summary of current and emerging clinical applications of AI in cardiac CT. We conclude with AI's advantages and limitations in this new field.
Collapse
Affiliation(s)
| | - Tyler Settle
- Medical Imaging Laboratory, Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA
| | - Muhammad Umer
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Jonathan Joshi
- Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA
| | - Matthew Shotwell
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Jishanth Mattumpuram
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Mounica Vorla
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Maryta Sztukowska
- Clinical Trials Unit, University of Louisville, Louisville, KY, USA; University of Information Technology and Management, Rzeszow, Poland
| | - Sohail Contractor
- Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA
| | - Amir Amini
- Medical Imaging Laboratory, Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA; Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA
| | - Dinesh K Kalra
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA; Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA.
| |
Collapse
|
2
|
Cildoz M, Ibarra A, Mallor F. Acuity-based rotational patient-to-physician assignment in an emergency department using electronic health records in triage. Health Informatics J 2023; 29:14604582231167430. [PMID: 37068379 DOI: 10.1177/14604582231167430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2023]
Abstract
Emergency department (ED) operational metrics generated by a new acuity-based rotational patient-to-physician assignment (ARPA) algorithm are compared with those obtained with a simple rotational patient assignment (SRPA) system aimed only at an equitable patient distribution. The new ARPA method theoretically guarantees that no two physicians' assigned patient loads can differ by more than one, either partially (by acuity levels) or in total; whereas SRPA guarantees only the latter. The performance of the ARPA method was assessed in practice in the ED of the main public hospital (Hospital Compound of Navarra) in the region of Navarre in Spain. This ED attends over 140 000 patients every year. Data analysis was conducted on 9,063 ED patients in the SRPA cohort, and 8,892 ED patients in the ARPA cohort. The metrics of interest are related both to patient access to healthcare and physician workload distribution: patient length of stay; arrival-to-provider time; ratio of patients exceeding the APT target threshold; and range of assigned patients across physicians by priority levels. The transition from SRPA to ARPA is associated with improvements in all ED operational metrics. This research demonstrates that ARPA is a simple and useful strategy for redesigning front-end ED processes.
Collapse
Affiliation(s)
- Marta Cildoz
- Institute of Smart Cities, Public University of Navarre, Pamplona, Spain
| | | | - Fermin Mallor
- Institute of Smart Cities, Public University of Navarre, Pamplona, Spain
| |
Collapse
|
3
|
Pala Z, Atıcı R, Yaldız E. Forecasting Future Monthly Patient Volume using Deep Learning and Statistical Models. WIRELESS PERSONAL COMMUNICATIONS 2023; 130:1479-1502. [PMID: 37168439 PMCID: PMC10004452 DOI: 10.1007/s11277-023-10341-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 02/25/2023] [Indexed: 05/13/2023]
Abstract
The variety of diseases is increasing day by day, and the demand for hospitals, especially for emergency and radiology units, is also increasing. As in other units, it is necessary to prepare the radiology unit for the future, to take into account the needs and to plan for the future. Due to the radiation emitted by the devices in the radiology unit, minimizing the time spent by the patients for the radiological image is of vital importance both for the unit staff and the patient. In order to solve the aforementioned problem, in this study, it is desired to estimate the monthly number of images in the radiology unit by using deep learning models and statistical-based models, and thus to be prepared for the future in a more planned way. For prediction processes, both deep learning models such as LSTM, MLP, NNAR and ELM, as well as statistical based prediction models such as ARIMA, SES, TBATS, HOLT and THETAF were used. In order to evaluate the performance of the models, the symmetric mean absolute percentage error (sMAPE) and mean absolute scaled error (MASE) metrics, which have been in demand recently, were preferred. The results showed that the LSTM model outperformed the deep learning group in estimating the monthly number of radiological case images, while the AUTO.ARIMA model performed better in the statistical-based group. It is believed that the findings obtained will speed up the procedures of the patients who come to the hospital and are referred to the radiology unit, and will facilitate the hospital managers in managing the patient flow more efficiently, increasing both the service quality and patient satisfaction, and making important contributions to the future planning of the hospital.
Collapse
Affiliation(s)
- Zeydin Pala
- Department of Software Engineering, Engineering Faculty, Mus Alparslan University, Mus, Turkey
| | - Ramazan Atıcı
- Department of Electricity and Automation, Technical Sciences Vocational School, Mus Alparslan University, Mus, Turkey
| | - Erkan Yaldız
- Halkbank IT Assistant Specialist, Istanbul, Turkey
| |
Collapse
|
4
|
Cengil AB, Eksioglu B, Eksioglu S, Eswaran H, Hayes CJ, Bogulski CA. Using data analytics for telehealth utilization: A case study in Arkansas. J Telemed Telecare 2023:1357633X231160039. [PMID: 36883218 DOI: 10.1177/1357633x231160039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Abstract
INTRODUCTION Many patients used telehealth services during the COVID-19 pandemic. In this study, we evaluate how different factors have affected telehealth utilization in recent years. Decision makers at the federal and state levels can use the results of this study to inform their healthcare-related policy decisions. METHODS We implemented data analytics techniques to determine the factors that explain the use of telehealth by developing a case study using data from Arkansas. Specifically, we built a random forest regression model which helps us identify the important factors in telehealth utilization. We evaluated how each factor impacts the number of telehealth patients in Arkansas counties. RESULTS Of the 11 factors evaluated, five are demographic, and six are socioeconomic factors. Socioeconomic factors are relatively easier to influence in the short term. Based on our results, broadband subscription is the most important socioeconomic factor and population density is the most important demographic factor. These two factors were followed by education level, computer use, and disability in terms of their importance as it relates to telehealth use. DISCUSSION Based on studies in the literature, telehealth has the potential to improve healthcare services by improving doctor utilization, reducing direct and indirect waiting times, and reducing costs. Thus, federal and state decision makers can influence the utilization of telehealth in specific locations by focusing on important factors. For example, investments can be made to increase broadband subscriptions, education levels, and computer use in targeted locations.
Collapse
Affiliation(s)
- Aysenur Betul Cengil
- Department of Industrial Engineering, University of Arkansas, Fayetteville, AR, USA
| | - Burak Eksioglu
- Department of Industrial Engineering, University of Arkansas, Fayetteville, AR, USA
| | - Sandra Eksioglu
- Department of Industrial Engineering, University of Arkansas, Fayetteville, AR, USA
| | - Hari Eswaran
- Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA
- Institute for Digital Health and Innovation, University of Arkansas for Medical Sciences, Little Rock, AR, USA
- Department of Obstetrics/Gynecology, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Corey J Hayes
- Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA
- Institute for Digital Health and Innovation, University of Arkansas for Medical Sciences, Little Rock, AR, USA
- Center for Mental Healthcare and Outcomes Research, Central Arkansas Veterans Healthcare System, North Little Rock, AR, USA
| | - Cari A Bogulski
- Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA
- Institute for Digital Health and Innovation, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| |
Collapse
|
5
|
Gavahi SS, Hosseini SMH, Moheimani A. An application of quality function deployment and SERVQUAL approaches to enhance the service quality in radiology centres. BENCHMARKING-AN INTERNATIONAL JOURNAL 2022. [DOI: 10.1108/bij-07-2021-0411] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeMeeting the patients' requirements as customers of the health care sector is crucially important as a social responsibility. According to the resource constraints, only an efficient utilisation of health services can provide that purpose. This study aims to develop a quantitative assessment framework for radiology centres as a vital section in healthcare to translate the patients' requirements into service quality specifications. This would help to achieve quality improvement by emphasising the voice of customers.Design/methodology/approachA literature review is conducted to specify the service quality criteria and the patients' requirements related to healthcare and hospitals. Based on the experts' opinions, these criteria and requirements are later customised for the radiology centres. Moreover, the requirements are categorised into five dimensions of SERVQUAL. The interrelations between service elements are also determined through expert group consensus using Pearson correlation. Afterwards, by applying the QFD method, the relations between the requirements and criteria are explored. Additionally, a customer satisfaction survey is executed in Tehran public hospitals to prioritise these requirements and provide an importance-satisfaction analysis.FindingsBased on the result of the case study, service elements are prioritised for improvement, and practical suggestions are provided using the Delphi technique for quality improvement. In addition, a cause-and-effect diagram is presented to highlight the improvement area and provide enhancement suggestions.Originality/valueThis study is the first empirical attempt to benefit from the VOC in evaluating and enhancing the quality of service delivered to radiology patients. In doing so, the study applies a hybrid approach of QFD and SERVQUAL as well as other tools to highlight the improvement area and provide enhancement suggestions. The findings can be readily used by the practitioners.
Collapse
|
6
|
Effective forecasting of key features in hospital emergency department: Hybrid deep learning-driven methods. MACHINE LEARNING WITH APPLICATIONS 2022. [DOI: 10.1016/j.mlwa.2021.100200] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
|
7
|
Becker AS, Erinjeri JP, Chaim J, Kastango N, Elnajjar P, Hricak H, Vargas HA. Automatic Forecasting of Radiology Examination Volume Trends for Optimal Resource Planning and Allocation. J Digit Imaging 2021; 35:1-8. [PMID: 34755249 PMCID: PMC8577854 DOI: 10.1007/s10278-021-00532-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 09/12/2021] [Accepted: 10/30/2021] [Indexed: 11/11/2022] Open
Abstract
The aim of the study was to evaluate the performance of the Prophet forecasting procedure, part of the Facebook open-source Artificial Intelligence portfolio, for forecasting variations in radiological examination volumes. Daily CT and MRI examination volumes from our institution were extracted from the radiology information system (RIS) database. Data from January 1, 2015, to December 31, 2019, was used for training the Prophet algorithm, and data from January 2020 was used for validation. Algorithm performance was then evaluated prospectively in February and August 2020. Total error and mean error per day were evaluated, and computational time was logged using different Markov chain Monte Carlo (MCMC) samples. Data from 610,570 examinations were used for training; the majority were CTs (82.3%). During retrospective testing, prediction error was reduced from 19 to < 1 per day in CT (total 589 to 17) and from 5 to < 1 per day (total 144 to 27) in MRI by fine-tuning the Prophet procedure. Prospective prediction error in February was 11 per day in CT (9934 predicted, 9667 actual) and 1 per day in MRI (2484 predicted, 2457 actual) and was significantly better than manual weekly predictions (p = 0.001). Inference with MCMC added no substantial improvements while vastly increasing computational time. Prophet accurately models weekly, seasonal, and overall trends paving the way for optimal resource allocation for radiology exam acquisition and interpretation.
Collapse
Affiliation(s)
- Anton S Becker
- Department of Radiology, Body Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Joseph P Erinjeri
- Department of Radiology, Interventional Radiology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Joshua Chaim
- Department of Radiology, Body Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nicholas Kastango
- Department of Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Pierre Elnajjar
- Department of Radiology, Informatics Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Hedvig Hricak
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - H Alberto Vargas
- Department of Radiology, Body Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| |
Collapse
|
8
|
Ranschaert E, Topff L, Pianykh O. Optimization of Radiology Workflow with Artificial Intelligence. Radiol Clin North Am 2021; 59:955-966. [PMID: 34689880 DOI: 10.1016/j.rcl.2021.06.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
The potential of artificial intelligence (AI) in radiology goes far beyond image analysis. AI can be used to optimize all steps of the radiology workflow by supporting a variety of nondiagnostic tasks, including order entry support, patient scheduling, resource allocation, and improving the radiologist's workflow. This article discusses several principal directions of using AI algorithms to improve radiological operations and workflow management, with the intention of providing a broader understanding of the value of applying AI in the radiology department.
Collapse
Affiliation(s)
- Erik Ranschaert
- Elisabeth-Tweesteden Hospital, Hilvarenbeekseweg 60, 5022 GC Tilburg, The Netherlands; Ghent University, C. Heymanslaan 10, 9000 Gent, Belgium.
| | - Laurens Topff
- Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Oleg Pianykh
- Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 25 New Chardon Street, Suite 470, Boston, MA 02114, USA
| |
Collapse
|
9
|
Harrou F, Kadri F, Sun Y, Khadraoui S. Monitoring patient flow in a hospital emergency department: ARMA-based nonparametric GLRT scheme. Health Informatics J 2021; 27:14604582211021649. [PMID: 34096378 DOI: 10.1177/14604582211021649] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Overcrowding in emergency departments (EDs) is a primary concern for hospital administration. They aim to efficiently manage patient demands and reducing stress in the ED. Detection of abnormal ED demands (patient flows) in hospital systems aids ED managers to obtain appropriate decisions by optimally allocating the available resources following patient attendance. This paper presents a monitoring strategy that provides an early alert in an ED when an abnormally high patient influx occurs. Anomaly detection using this strategy involves the amalgamation of autoregressive-moving-average (ARMA) time series models with the generalized likelihood ratio (GLR) chart. A nonparametric procedure based on kernel density estimation is employed to determine the detection threshold of the ARMA-GLR chart. The developed ARMA-based GLR has been validated through practical data from the ED at Lille Hospital, France. Then, the ARMA-based GLR method's performance was compared to that of other commonly used charts, including a Shewhart chart and an exponentially weighted moving average chart; it proved more accurate.
Collapse
Affiliation(s)
- Fouzi Harrou
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology, Saudi Arabia
| | - Farid Kadri
- Aeroline and Customer Services, Agence, Sopra Steria Group, France
| | - Ying Sun
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology, Saudi Arabia
| | - Sofiane Khadraoui
- Department of Electrical Engineering, University of Sharjah, United Arab Emirates
| |
Collapse
|
10
|
Crombé A, Lecomte JC, Banaste N, Tazarourte K, Seux M, Nivet H, Thomson V, Gorincour G. Emergency teleradiological activity is an epidemiological estimator and predictor of the covid-19 pandemic in mainland France. Insights Imaging 2021; 12:103. [PMID: 34292414 PMCID: PMC8295630 DOI: 10.1186/s13244-021-01040-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 06/11/2021] [Indexed: 02/07/2023] Open
Abstract
Background COVID-19 pandemic highlighted the need for real-time monitoring of diseases evolution to rapidly adapt restrictive measures. This prospective multicentric study aimed at investigating radiological markers of COVID-19-related emergency activity as global estimators of pandemic evolution in France. We incorporated two sources of data from March to November 2020: an open-source epidemiological dataset, collecting daily hospitalisations, intensive care unit admissions, hospital deaths and discharges, and a teleradiology dataset corresponding to the weekly number of CT-scans performed in 65 emergency centres and interpreted remotely. CT-scans specifically requested for COVID-19 suspicion were monitored. Teleradiological and epidemiological time series were aligned. Their relationships were estimated through a cross-correlation function, and their extremes and breakpoints were compared. Dynamic linear models were trained to forecast the weekly hospitalisations based on teleradiological activity predictors. Results A total of 100,018 CT-scans were included over 36 weeks, and 19,133 (19%) performed within the COVID-19 workflow. Concomitantly, 227,677 hospitalisations were reported. Teleradiological and epidemiological time series were almost perfectly superimposed (cross-correlation coefficients at lag 0: 0.90–0.92). Maximal number of COVID-19 CT-scans was reached the week of 2020-03-23 (1 086 CT-scans), 1 week before the highest hospitalisations (23,542 patients). The best valid forecasting model combined the number of COVID-19 CT-scans and the number of hospitalisations during the prior two weeks and provided the lowest mean absolute percentage (5.09%, testing period: 2020-11-02 to 2020-11-29). Conclusion Monitoring COVID-19 CT-scan activity in emergencies accurately and instantly predicts hospitalisations and helps adjust medical resources, paving the way for complementary public health indicators. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-021-01040-3.
Collapse
Affiliation(s)
- Amandine Crombé
- Imadis Teleradiology, Lyon, Bordeaux, Marseille, France.,University of Bordeaux, Bordeaux, France
| | - Jean-Christophe Lecomte
- Imadis Teleradiology, Lyon, Bordeaux, Marseille, France.,Centre Hospitalier de Saintonge, Saintes, France.,Centre Aquitain D'Imagerie, Bordeaux, France
| | - Nathan Banaste
- Imadis Teleradiology, Lyon, Bordeaux, Marseille, France.,Department of Radiology, Hôpital Nord-Ouest, Villefranche-sur-Saône, France
| | - Karim Tazarourte
- Emergency Department, CHU Edouard Herriot, Hospices Civils de Lyon, Lyon, France.,INSERM 1290 RESHAPE, University of Lyon 1, Lyon, France
| | - Mylène Seux
- Imadis Teleradiology, Lyon, Bordeaux, Marseille, France
| | - Hubert Nivet
- Imadis Teleradiology, Lyon, Bordeaux, Marseille, France.,Centre Hospitalier de Saintonge, Saintes, France.,Centre Aquitain D'Imagerie, Bordeaux, France
| | - Vivien Thomson
- Imadis Teleradiology, Lyon, Bordeaux, Marseille, France.,Ramsay Générale de Santé, Clinique de la Sauvegarde, Lyon, France
| | - Guillaume Gorincour
- Imadis Teleradiology, Lyon, Bordeaux, Marseille, France. .,ELSAN, Clinique Bouchard, Marseille, France.
| |
Collapse
|