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Lundin RM, Falcao VP, Kannangara S, Eakin CW, Abdar M, O'Neill J, Khosravi A, Eyre H, Nahavandi S, Loo C, Berk M. Machine Learning in Electroconvulsive Therapy: A Systematic Review. J ECT 2024:00124509-990000000-00167. [PMID: 38857315 DOI: 10.1097/yct.0000000000001009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/12/2024]
Abstract
ABSTRACT Despite years of research, we are still not able to reliably predict who might benefit from electroconvulsive therapy (ECT) treatment. As we exhaust what is possible using traditional statistical analysis, ECT remains a good candidate for machine learning approaches due to the large data sets with data captured through electroencephalography (EEG) and other objective measures. A systematic review of 6 databases led to the full-text examination of 26 articles using machine learning approaches in examining data predicting response to ECT treatment. The identified articles used a wide variety of data types covering structural and functional imaging data (n = 15), clinical data (n = 5), a combination of clinical and imaging data (n = 2), EEG (n = 3), and social media posts (n = 1). The clinical indications in which response prediction was assessed were depression (n = 21) and psychosis (n = 4). Changes in multiple anatomical regions in the brain were identified as holding a predictive value for response to ECT. These primarily centered on the limbic system and associated networks. Clinical features predicting good response to ECT in depression included shorter duration, lower severity, higher medication dose, psychotic features, low cortisol levels, and positive family history. It has also been possible to predict the likelihood of relapse of readmission with psychosis after ECT treatment, including a better response if higher transfer entropy was calculated from EEG signals. A transdisciplinary approach with an international consortium collecting a wide range of retrospective and prospective data may help to refine and extend these outcomes and translate them into clinical practice.
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Affiliation(s)
| | | | | | - Charles W Eakin
- From the Mental Health, Drug and Alcohol Services, Barwon Health
| | - Moloud Abdar
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, Victoria, Australia
| | - John O'Neill
- Waikato District Health Board, Hamilton, New Zealand
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, Victoria, Australia
| | | | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, Victoria, Australia
| | | | - Michael Berk
- IMPACT - the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Deakin University, Geelong, Victoria, Australia
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Barbieri MA, Battini V, Sessa M. Artificial intelligence for the optimal management of community-acquired pneumonia. Curr Opin Pulm Med 2024; 30:252-257. [PMID: 38305352 DOI: 10.1097/mcp.0000000000001055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
PURPOSE OF REVIEW This timely review explores the integration of artificial intelligence (AI) into community-acquired pneumonia (CAP) management, emphasizing its relevance in predicting the risk of hospitalization. With CAP remaining a global public health concern, the review highlights the need for efficient and reliable AI tools to optimize resource allocation and improve patient outcomes. RECENT FINDINGS Challenges in CAP management delve into the application of AI in predicting CAP-related hospitalization risks, and complications, and mortality. The integration of AI-based risk scores in managing CAP has the potential to enhance the accuracy of predicting patients at higher risk, facilitating timely intervention and resource allocation. Moreover, AI algorithms reduce variability associated with subjective clinical judgment, promoting consistency in decision-making, and provide real-time risk assessments, aiding in the dynamic management of patients with CAP. SUMMARY The development and implementation of AI-tools for hospitalization in CAP represent a transformative approach to improving patient outcomes. The integration of AI into healthcare has the potential to revolutionize the way we identify and manage individuals at risk of severe outcomes, ultimately leading to more efficient resource utilization and better overall patient care.
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Affiliation(s)
- Maria Antonietta Barbieri
- Department of Clinical and Experimental Medicine, University of Messina, Messina
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Vera Battini
- Pharmacovigilance & Clinical Research, International Centre for Pesticides and Health Risk Prevention, Department of Biomedical and Clinical Sciences (DIBIC), ASST, Fatebenefratelli-Sacco University Hospital, Università degli Studi di Milano, Milan, Italy
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Maurizio Sessa
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
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Al-Anazi S, Al-Omari A, Alanazi S, Marar A, Asad M, Alawaji F, Alwateid S. Artificial intelligence in respiratory care: Current scenario and future perspective. Ann Thorac Med 2024; 19:117-130. [PMID: 38766378 PMCID: PMC11100474 DOI: 10.4103/atm.atm_192_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 09/08/2023] [Accepted: 09/11/2023] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND This narrative review aims to explore the current state and future perspective of artificial intelligence (AI) in respiratory care. The objective is to provide insights into the potential impact of AI in this field. METHODS A comprehensive analysis of relevant literature and research studies was conducted to examine the applications of AI in respiratory care and identify areas of advancement. The analysis included studies on remote monitoring, early detection, smart ventilation systems, and collaborative decision-making. RESULTS The obtained results highlight the transformative potential of AI in respiratory care. AI algorithms have shown promising capabilities in enabling tailored treatment plans based on patient-specific data. Remote monitoring using AI-powered devices allows for real-time feedback to health-care providers, enhancing patient care. AI algorithms have also demonstrated the ability to detect respiratory conditions at an early stage, leading to timely interventions and improved outcomes. Moreover, AI can optimize mechanical ventilation through continuous monitoring, enhancing patient comfort and reducing complications. Collaborative AI systems have the potential to augment the expertise of health-care professionals, leading to more accurate diagnoses and effective treatment strategies. CONCLUSION By improving diagnosis, AI has the potential to revolutionize respiratory care, treatment planning, and patient monitoring. While challenges and ethical considerations remain, the transformative impact of AI in this domain cannot be overstated. By leveraging the advancements and insights from this narrative review, health-care professionals and researchers can continue to harness the power of AI to improve patient outcomes and enhance respiratory care practices. IMPROVEMENTS Based on the findings, future research should focus on refining AI algorithms to enhance their accuracy, reliability, and interpretability. In addition, attention should be given to addressing ethical considerations, ensuring data privacy, and establishing regulatory frameworks to govern the responsible implementation of AI in respiratory care.
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Affiliation(s)
- Saad Al-Anazi
- Lead Clincial Appliaction AzeerTrade (Lowenstein Medical Company), Riyadh, Saudi Arabia
| | - Awad Al-Omari
- Department of Intensive Care, Dr. Sulaiman Al-Habib Group Hospitals, Riyadh, Saudi Arabia
| | - Safug Alanazi
- Intensivist, Al Hammadi Hospital, Riyadh, Saudi Arabia
| | - Aqeelah Marar
- Respiratory Care Administration, King Fahad Medical City, Riyadh, Saudi Arabia
| | - Mohammed Asad
- Department of Emergency, Dr. Sulaiman Al-Habib Group Hospitals, Riyadh, Saudi Arabia
| | - Fadi Alawaji
- Ar Rass General Hospital, Qassim Health Cluster, Senior Laboratory Specialist, Rass Region, Qassim City, Saudi Arabia
| | - Salman Alwateid
- Respiratory Care Administration, King Fahad Medical City, Riyadh, Saudi Arabia
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Stivi T, Padawer D, Dirini N, Nachshon A, Batzofin BM, Ledot S. Using Artificial Intelligence to Predict Mechanical Ventilation Weaning Success in Patients with Respiratory Failure, Including Those with Acute Respiratory Distress Syndrome. J Clin Med 2024; 13:1505. [PMID: 38592696 PMCID: PMC10934889 DOI: 10.3390/jcm13051505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 02/29/2024] [Accepted: 03/03/2024] [Indexed: 04/10/2024] Open
Abstract
The management of mechanical ventilation (MV) remains a challenge in intensive care units (ICUs). The digitalization of healthcare and the implementation of artificial intelligence (AI) and machine learning (ML) has significantly influenced medical decision-making capabilities, potentially enhancing patient outcomes. Acute respiratory distress syndrome, an overwhelming inflammatory lung disease, is common in ICUs. Most patients require MV. Prolonged MV is associated with an increased length of stay, morbidity, and mortality. Shortening the MV duration has both clinical and economic benefits and emphasizes the need for better MV weaning management. AI and ML models can assist the physician in weaning patients from MV by providing predictive tools based on big data. Many ML models have been developed in recent years, dealing with this unmet need. Such models provide an important prediction regarding the success of the individual patient's MV weaning. Some AI models have shown a notable impact on clinical outcomes. However, there are challenges in integrating AI models into clinical practice due to the unfamiliar nature of AI for many physicians and the complexity of some AI models. Our review explores the evolution of weaning methods up to and including AI and ML as weaning aids.
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Affiliation(s)
- Tamar Stivi
- Department of Anesthesia, Critical Care and Pain Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel; (N.D.); (A.N.); (B.M.B.); (S.L.)
| | - Dan Padawer
- Department of Pulmonary Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel;
- Faculty of Medicine, Hebrew University of Jerusalem, Campus Ein Kerem, Jerusalem 9112102, Israel
| | - Noor Dirini
- Department of Anesthesia, Critical Care and Pain Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel; (N.D.); (A.N.); (B.M.B.); (S.L.)
| | - Akiva Nachshon
- Department of Anesthesia, Critical Care and Pain Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel; (N.D.); (A.N.); (B.M.B.); (S.L.)
| | - Baruch M. Batzofin
- Department of Anesthesia, Critical Care and Pain Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel; (N.D.); (A.N.); (B.M.B.); (S.L.)
| | - Stephane Ledot
- Department of Anesthesia, Critical Care and Pain Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel; (N.D.); (A.N.); (B.M.B.); (S.L.)
- Faculty of Medicine, Hebrew University of Jerusalem, Campus Ein Kerem, Jerusalem 9112102, Israel
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van de Beld JJ, Crull D, Mikhal J, Geerdink J, Veldhuis A, Poel M, Kouwenhoven EA. Complication Prediction after Esophagectomy with Machine Learning. Diagnostics (Basel) 2024; 14:439. [PMID: 38396478 PMCID: PMC10888312 DOI: 10.3390/diagnostics14040439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 12/28/2023] [Accepted: 12/29/2023] [Indexed: 02/25/2024] Open
Abstract
Esophageal cancer can be treated effectively with esophagectomy; however, the postoperative complication rate is high. In this paper, we study to what extent machine learning methods can predict anastomotic leakage and pneumonia up to two days in advance. We use a dataset with 417 patients who underwent esophagectomy between 2011 and 2021. The dataset contains multimodal temporal information, specifically, laboratory results, vital signs, thorax images, and preoperative patient characteristics. The best models scored mean test set AUROCs of 0.87 and 0.82 for leakage 1 and 2 days ahead, respectively. For pneumonia, this was 0.74 and 0.61 for 1 and 2 days ahead, respectively. We conclude that machine learning models can effectively predict anastomotic leakage and pneumonia after esophagectomy.
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Affiliation(s)
- Jorn-Jan van de Beld
- Faculty of EEMCS, University of Twente, 7500 AE Enschede, The Netherlands
- Hospital Group Twente (ZGT), 7609 PP Almelo, The Netherlands
| | - David Crull
- Hospital Group Twente (ZGT), 7609 PP Almelo, The Netherlands
| | - Julia Mikhal
- Hospital Group Twente (ZGT), 7609 PP Almelo, The Netherlands
- Faculty of BMS, University of Twente, 7500 AE Enschede, The Netherlands
| | - Jeroen Geerdink
- Hospital Group Twente (ZGT), 7609 PP Almelo, The Netherlands
| | - Anouk Veldhuis
- Hospital Group Twente (ZGT), 7609 PP Almelo, The Netherlands
| | - Mannes Poel
- Faculty of EEMCS, University of Twente, 7500 AE Enschede, The Netherlands
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Kumar S, Kumar H, Kumar G, Singh SP, Bijalwan A, Diwakar M. A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review. BMC Med Imaging 2024; 24:30. [PMID: 38302883 PMCID: PMC10832080 DOI: 10.1186/s12880-024-01192-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 01/03/2024] [Indexed: 02/03/2024] Open
Abstract
BACKGROUND Lung diseases, both infectious and non-infectious, are the most prevalent cause of mortality overall in the world. Medical research has identified pneumonia, lung cancer, and Corona Virus Disease 2019 (COVID-19) as prominent lung diseases prioritized over others. Imaging modalities, including X-rays, computer tomography (CT) scans, magnetic resonance imaging (MRIs), positron emission tomography (PET) scans, and others, are primarily employed in medical assessments because they provide computed data that can be utilized as input datasets for computer-assisted diagnostic systems. Imaging datasets are used to develop and evaluate machine learning (ML) methods to analyze and predict prominent lung diseases. OBJECTIVE This review analyzes ML paradigms, imaging modalities' utilization, and recent developments for prominent lung diseases. Furthermore, the research also explores various datasets available publically that are being used for prominent lung diseases. METHODS The well-known databases of academic studies that have been subjected to peer review, namely ScienceDirect, arXiv, IEEE Xplore, MDPI, and many more, were used for the search of relevant articles. Applied keywords and combinations used to search procedures with primary considerations for review, such as pneumonia, lung cancer, COVID-19, various imaging modalities, ML, convolutional neural networks (CNNs), transfer learning, and ensemble learning. RESULTS This research finding indicates that X-ray datasets are preferred for detecting pneumonia, while CT scan datasets are predominantly favored for detecting lung cancer. Furthermore, in COVID-19 detection, X-ray datasets are prioritized over CT scan datasets. The analysis reveals that X-rays and CT scans have surpassed all other imaging techniques. It has been observed that using CNNs yields a high degree of accuracy and practicability in identifying prominent lung diseases. Transfer learning and ensemble learning are complementary techniques to CNNs to facilitate analysis. Furthermore, accuracy is the most favored metric for assessment.
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Affiliation(s)
- Sunil Kumar
- Department of Computer Engineering, J. C. Bose University of Science and Technology, YMCA, Faridabad, India
- Department of Information Technology, School of Engineering and Technology (UIET), CSJM University, Kanpur, India
| | - Harish Kumar
- Department of Computer Engineering, J. C. Bose University of Science and Technology, YMCA, Faridabad, India
| | - Gyanendra Kumar
- Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India
| | | | - Anchit Bijalwan
- Faculty of Electrical and Computer Engineering, Arba Minch University, Arba Minch, Ethiopia.
| | - Manoj Diwakar
- Department of Computer Science and Engineering, Graphic Era Deemed to Be University, Dehradun, India
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Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior KJ, Poirrier JE, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Rev Pharmacoecon Outcomes Res 2024; 24:63-115. [PMID: 37955147 DOI: 10.1080/14737167.2023.2279107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 10/31/2023] [Indexed: 11/14/2023]
Abstract
INTRODUCTION The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery. METHODS A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted. RESULTS In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively). EXPERT OPINION The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.
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Affiliation(s)
- Katarzyna Kolasa
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | - Bisrat Admassu
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
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Cilloniz C, Pericas JM, Curioso WH. Interventions to improve outcomes in community-acquired pneumonia. Expert Rev Anti Infect Ther 2023; 21:1071-1086. [PMID: 37691049 DOI: 10.1080/14787210.2023.2257392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 08/25/2023] [Accepted: 09/06/2023] [Indexed: 09/12/2023]
Abstract
INTRODUCTION Community-acquired pneumonia (CAP) is a common infection associated with high morbimortality and a highly deleterious impact on patients' quality of life and functionality. We comprehensively review the factors related to the host, the causative microorganism, the therapeutic approach and the organization of health systems (e.g. setting for care and systems for allocation) that might have an impact on CAP-associated outcomes. Our main aims are to discuss the most controversial points and to provide some recommendations that may guide further research and the management of patients with CAP, in order to improve their outcomes, beyond mortality. AREA COVERED In this review, we aim to provide a critical account of potential measures to improve outcomes of CAP and the supporting evidence from observational studies and clinical trials. EXPERT OPINION CAP is associated with high mortality and a highly deleterious impact on patients' quality of life. To improve CAP-associated outcomes, it is important to understand the factors related to the patient, etiology, therapeutics, and the organization of health systems.
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Affiliation(s)
- Catia Cilloniz
- IDIBAPS, Center for Biomedical Research in Respiratory Diseases Network (CIBERES), Barcelona, Spain
- Facultad de Ciencias de la Salud, Universidad Continental, Huancayo, Peru
| | - Juan Manuel Pericas
- Liver Unit, Internal Medicine Department, Vall d'Hebron University Hospital, Vall d'Hebron Institute for Research (VHIR), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (Ciberehd), Barcelona, Spain
| | - Walter H Curioso
- Facultad de Ciencias de la Salud, Universidad Continental, Huancayo, Peru
- Health Services Administration, Continental University of Florida, Margate, FL, USA
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Song X, Li H, Chen Q, Zhang T, Huang G, Zou L, Du D. Predicting pneumonia during hospitalization in flail chest patients using machine learning approaches. Front Surg 2023; 9:1060691. [PMID: 36684357 PMCID: PMC9852626 DOI: 10.3389/fsurg.2022.1060691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 11/14/2022] [Indexed: 01/07/2023] Open
Abstract
Objective Pneumonia is a common pulmonary complication of flail chest, causing high morbidity and mortality rates in affected patients. The existing methods for identifying pneumonia have low accuracy, and their use may delay antimicrobial therapy. However, machine learning can be combined with electronic medical record systems to identify information and assist in quick clinical decision-making. Our study aimed to develop a novel machine-learning model to predict pneumonia risk in flail chest patients. Methods From January 2011 to December 2021, the electronic medical records of 169 adult patients with flail chest at a tertiary teaching hospital in an urban level I Trauma Centre in Chongqing were retrospectively analysed. Then, the patients were randomly divided into training and test sets at a ratio of 7:3. Using the Fisher score, the best subset of variables was chosen. The performance of the seven models was evaluated by computing the area under the receiver operating characteristic curve (AUC). The output of the XGBoost model was shown using the Shapley Additive exPlanation (SHAP) method. Results Of 802 multiple rib fracture patients, 169 flail chest patients were eventually included, and 86 (50.80%) were diagnosed with pneumonia. The XGBoost model performed the best among all seven machine-learning models. The AUC of the XGBoost model was 0.895 (sensitivity: 84.3%; specificity: 80.0%).Pneumonia in flail chest patients was associated with several features: systolic blood pressure, pH value, blood transfusion, and ISS. Conclusion Our study demonstrated that the XGBoost model with 32 variables had high reliability in assessing risk indicators of pneumonia in flail chest patients. The SHAP method can identify vital pneumonia risk factors, making the XGBoost model's output clinically meaningful.
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Affiliation(s)
- Xiaolin Song
- School of Medicine, Chongqing University, Chongqing, China,Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
| | - Hui Li
- Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
| | - Qingsong Chen
- Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
| | - Tao Zhang
- School of Medicine, Chongqing University, Chongqing, China,Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
| | - Guangbin Huang
- Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
| | - Lingyun Zou
- Clinical Data Research Center, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China,Correspondence: Dingyuan Du Lingyun Zou
| | - Dingyuan Du
- Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China,Correspondence: Dingyuan Du Lingyun Zou
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Barakat N, Awad M, Abu-Nabah BA. A machine learning approach on chest X-rays for pediatric pneumonia detection. Digit Health 2023; 9:20552076231180008. [PMID: 37312953 PMCID: PMC10259147 DOI: 10.1177/20552076231180008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 05/11/2023] [Indexed: 06/15/2023] Open
Abstract
Background According to the World Health Organization (WHO), pneumonia is the leading infectious cause of death in children below 5 years old. Hence, the early detection of pediatric pneumonia is crucial to reduce its morbidity and mortality rates. Even though chest radiography is the most commonly employed modality for pneumonia detection, recent studies highlight the existence of poor interobserver agreement in the chest X-ray interpretation of healthcare practitioners when it comes to diagnosing pediatric pneumonia. Thus, there is a significant need for automating the detection process to minimize the potential human error. Since Artificial Intelligence tools such as Deep Learning (DL) and Machine Learning (ML) have the potential to automate disease detection, many researchers explored how such tools can be implemented to detect pneumonia in chest X-rays. Notably, the majority of efforts tackled this problem from a DL point of view. However, ML has shown a higher potential for medical interpretability while being less computationally demanding than DL. Objective The aim of this paper is to automate the early detection process of pediatric pneumonia using ML as it is less computationally demanding than DL. Methods The proposed approach entails performing data augmentation to balance the classes of the utilized dataset, optimizing the feature extraction scheme, and evaluating the performance of several ML models. Moreover, the performance of this approach is compared to a TL benchmark to evaluate its candidacy. Results Using the proposed approach, the Quadratic SVM model yielded an accuracy of 97.58%, surpassing the accuracies reported in the current ML literature. In addition, this model classification time was significantly smaller than that of the TL benchmark. Conclusion The results strongly support the candidacy of the proposed approach in reliably detecting pediatric pneumonia.
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Affiliation(s)
- Natali Barakat
- Engineering Systems Management Department, American University of Sharjah, College of Engineering, Sharjah, United Arab Emirates
| | - Mahmoud Awad
- Industrial Engineering Department, American University of Sharjah, College of Engineering, Sharjah, United Arab Emirates
| | - Bassam A Abu-Nabah
- Mechanical Engineering Department, American University of Sharjah, College of Engineering, Sharjah, United Arab Emirates
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Modern Machine-Learning Predictive Models for Diagnosing Infectious Diseases. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:6902321. [PMID: 35693267 PMCID: PMC9185172 DOI: 10.1155/2022/6902321] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 04/03/2022] [Accepted: 05/26/2022] [Indexed: 12/16/2022]
Abstract
Controlling infectious diseases is a major health priority because they can spread and infect humans, thus evolving into epidemics or pandemics. Therefore, early detection of infectious diseases is a significant need, and many researchers have developed models to diagnose them in the early stages. This paper reviewed research articles for recent machine-learning (ML) algorithms applied to infectious disease diagnosis. We searched the Web of Science, ScienceDirect, PubMed, Springer, and IEEE databases from 2015 to 2022, identified the pros and cons of the reviewed ML models, and discussed the possible recommendations to advance the studies in this field. We found that most of the articles used small datasets, and few of them used real-time data. Our results demonstrated that a suitable ML technique depends on the nature of the dataset and the desired goal. Moreover, heterogeneous data could ensure the model's generalization, while big data, many features, and a hybrid model will increase the resulting performance. Furthermore, using other techniques such as deep learning and NLP to extract vast features from unstructured data is a powerful approach to enhancing the performance of ML diagnostic models.
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Villa-Parra AC, Criollo I, Valadão C, Silva L, Coelho Y, Lampier L, Rangel L, Sharma G, Delisle-Rodríguez D, Calle-Siguencia J, Urgiles-Ortiz F, Díaz C, Caldeira E, Krishnan S, Bastos-Filho T. Towards Multimodal Equipment to Help in the Diagnosis of COVID-19 Using Machine Learning Algorithms. SENSORS 2022; 22:s22124341. [PMID: 35746121 PMCID: PMC9228002 DOI: 10.3390/s22124341] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 06/01/2022] [Accepted: 06/02/2022] [Indexed: 12/29/2022]
Abstract
COVID-19 occurs due to infection through respiratory droplets containing the SARS-CoV-2 virus, which are released when someone sneezes, coughs, or talks. The gold-standard exam to detect the virus is Real-Time Polymerase Chain Reaction (RT-PCR); however, this is an expensive test and may require up to 3 days after infection for a reliable result, and if there is high demand, the labs could be overwhelmed, which can cause significant delays in providing results. Biomedical data (oxygen saturation level—SpO2, body temperature, heart rate, and cough) are acquired from individuals and are used to help infer infection by COVID-19, using machine learning algorithms. The goal of this study is to introduce the Integrated Portable Medical Assistant (IPMA), which is a multimodal piece of equipment that can collect biomedical data, such as oxygen saturation level, body temperature, heart rate, and cough sound, and helps infer the diagnosis of COVID-19 through machine learning algorithms. The IPMA has the capacity to store the biomedical data for continuous studies and can be used to infer other respiratory diseases. Quadratic kernel-free non-linear Support Vector Machine (QSVM) and Decision Tree (DT) were applied on three datasets with data of cough, speech, body temperature, heart rate, and SpO2, obtaining an Accuracy rate (ACC) and Area Under the Curve (AUC) of approximately up to 88.0% and 0.85, respectively, as well as an ACC up to 99% and AUC = 0.94, respectively, for COVID-19 infection inference. When applied to the data acquired with the IMPA, these algorithms achieved 100% accuracy. Regarding the easiness of using the equipment, 36 volunteers reported that the IPMA has a high usability, according to results from two metrics used for evaluation: System Usability Scale (SUS) and Post Study System Usability Questionnaire (PSSUQ), with scores of 85.5 and 1.41, respectively. In light of the worldwide needs for smart equipment to help fight the COVID-19 pandemic, this new equipment may help with the screening of COVID-19 through data collected from biomedical signals and cough sounds, as well as the use of machine learning algorithms.
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Affiliation(s)
- Ana Cecilia Villa-Parra
- Biomedical Engineering Research Group—GIIB, Universidad Politécnica Salesiana (UPS), Cuenca 010105, Ecuador; (A.C.V.-P.); (I.C.); (J.C.-S.); (F.U.-O.)
| | - Ismael Criollo
- Biomedical Engineering Research Group—GIIB, Universidad Politécnica Salesiana (UPS), Cuenca 010105, Ecuador; (A.C.V.-P.); (I.C.); (J.C.-S.); (F.U.-O.)
| | - Carlos Valadão
- Department of Electrical Engineering, Universidade Federal do Espírito Santo (UFES), Vitoria 29075-910, Brazil; (C.V.); (L.S.); (Y.C.); (L.L.); (L.R.); (D.D.-R.); (C.D.); (E.C.)
| | - Leticia Silva
- Department of Electrical Engineering, Universidade Federal do Espírito Santo (UFES), Vitoria 29075-910, Brazil; (C.V.); (L.S.); (Y.C.); (L.L.); (L.R.); (D.D.-R.); (C.D.); (E.C.)
| | - Yves Coelho
- Department of Electrical Engineering, Universidade Federal do Espírito Santo (UFES), Vitoria 29075-910, Brazil; (C.V.); (L.S.); (Y.C.); (L.L.); (L.R.); (D.D.-R.); (C.D.); (E.C.)
| | - Lucas Lampier
- Department of Electrical Engineering, Universidade Federal do Espírito Santo (UFES), Vitoria 29075-910, Brazil; (C.V.); (L.S.); (Y.C.); (L.L.); (L.R.); (D.D.-R.); (C.D.); (E.C.)
| | - Luara Rangel
- Department of Electrical Engineering, Universidade Federal do Espírito Santo (UFES), Vitoria 29075-910, Brazil; (C.V.); (L.S.); (Y.C.); (L.L.); (L.R.); (D.D.-R.); (C.D.); (E.C.)
| | - Garima Sharma
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada; (G.S.); (S.K.)
| | - Denis Delisle-Rodríguez
- Department of Electrical Engineering, Universidade Federal do Espírito Santo (UFES), Vitoria 29075-910, Brazil; (C.V.); (L.S.); (Y.C.); (L.L.); (L.R.); (D.D.-R.); (C.D.); (E.C.)
| | - John Calle-Siguencia
- Biomedical Engineering Research Group—GIIB, Universidad Politécnica Salesiana (UPS), Cuenca 010105, Ecuador; (A.C.V.-P.); (I.C.); (J.C.-S.); (F.U.-O.)
| | - Fernando Urgiles-Ortiz
- Biomedical Engineering Research Group—GIIB, Universidad Politécnica Salesiana (UPS), Cuenca 010105, Ecuador; (A.C.V.-P.); (I.C.); (J.C.-S.); (F.U.-O.)
| | - Camilo Díaz
- Department of Electrical Engineering, Universidade Federal do Espírito Santo (UFES), Vitoria 29075-910, Brazil; (C.V.); (L.S.); (Y.C.); (L.L.); (L.R.); (D.D.-R.); (C.D.); (E.C.)
| | - Eliete Caldeira
- Department of Electrical Engineering, Universidade Federal do Espírito Santo (UFES), Vitoria 29075-910, Brazil; (C.V.); (L.S.); (Y.C.); (L.L.); (L.R.); (D.D.-R.); (C.D.); (E.C.)
| | - Sridhar Krishnan
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada; (G.S.); (S.K.)
| | - Teodiano Bastos-Filho
- Department of Electrical Engineering, Universidade Federal do Espírito Santo (UFES), Vitoria 29075-910, Brazil; (C.V.); (L.S.); (Y.C.); (L.L.); (L.R.); (D.D.-R.); (C.D.); (E.C.)
- Correspondence: ; Tel.: +593-98-441-2586
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Microscopic Imaging and Labeling Dataset for the Detection of Pneumocystis jirovecii Using Methenamine Silver Staining Method. DATA 2022. [DOI: 10.3390/data7050056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Pneumocystis jirovecii pneumonia is one of the diseases that most affects immunocompromised patients today, and under certain circumstances, it can be fatal. On the other hand, more and more automatic tools based on artificial intelligence are required every day to help diagnose diseases and thus optimize the resources of the healthcare system. It is therefore important to develop techniques and mechanisms that enable early diagnosis. One of the most widely used techniques in diagnostic laboratories for the detection of its etiological agent, Pneumocystis jirovecii, is optical microscopy. Therefore, an image dataset of 29 different patients is presented in this work, which can be used to detect whether a patient is positive or negative for this fungi. These images were taken in at least four random positions on the specimen holder. The dataset consists of a total of 137 RGB images. Likewise, it contains realistic, annotated, and high-quality microscope images. In addition, we provide image segmentation and labeling that can also be used in numerous studies based on artificial intelligence implementation. The labeling was also validated by an expert, allowing it to be used as a reference in the training of automatic algorithms with supervised learning methods and thus to develop diagnostic assistance systems. Therefore, the dataset will open new opportunities for researchers working in image segmentation, detection, and classification problems related to Pneumocystis jirovecii pneumonia diagnosis.
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