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Tahir AM, Guo L, Ward RK, Yu X, Rideout A, Hore M, Wang ZJ. Explainable machine learning for assessing upper respiratory tract of racehorses from endoscopy videos. Comput Biol Med 2024; 181:109030. [PMID: 39173488 DOI: 10.1016/j.compbiomed.2024.109030] [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: 12/02/2023] [Revised: 06/20/2024] [Accepted: 08/13/2024] [Indexed: 08/24/2024]
Abstract
Laryngeal hemiplegia (LH) is a major upper respiratory tract (URT) complication in racehorses. Endoscopy imaging of horse throat is a gold standard for URT assessment. However, current manual assessment faces several challenges, stemming from the poor quality of endoscopy videos and subjectivity of manual grading. To overcome such limitations, we propose an explainable machine learning (ML)-based solution for efficient URT assessment. Specifically, a cascaded YOLOv8 architecture is utilized to segment the key semantic regions and landmarks per frame. Several spatiotemporal features are then extracted from key landmarks points and fed to a decision tree (DT) model to classify LH as Grade 1,2,3 or 4 denoting absence of LH, mild, moderate, and severe LH, respectively. The proposed method, validated through 5-fold cross-validation on 107 videos, showed promising performance in classifying different LH grades with 100%, 91.18%, 94.74% and 100% sensitivity values for Grade 1 to 4, respectively. Further validation on an external dataset of 72 cases confirmed its generalization capability with 90%, 80.95%, 100%, and 100% sensitivity values for Grade 1 to 4, respectively. We introduced several explainability related assessment functions, including: (i) visualization of YOLOv8 output to detect landmark estimation errors which can affect the final classification, (ii) time-series visualization to assess video quality, and (iii) backtracking of the DT output to identify borderline cases. We incorporated domain knowledge (e.g., veterinarian diagnostic procedures) into the proposed ML framework. This provides an assistive tool with clinical-relevance and explainability that can ease and speed up the URT assessment by veterinarians.
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Affiliation(s)
- Anas Mohammed Tahir
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
| | - Li Guo
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
| | - Rabab K Ward
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
| | - Xinhui Yu
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
| | - Andrew Rideout
- Point To Point Research & Development, Vancouver, BC, Canada.
| | - Michael Hore
- Hagyard Equine Medical Institute, Lexington, KY, USA.
| | - Z Jane Wang
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
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Datta D, Ray S, Martinez L, Newman D, Dalmida SG, Hashemi J, Sareli C, Eckardt P. Feature Identification Using Interpretability Machine Learning Predicting Risk Factors for Disease Severity of In-Patients with COVID-19 in South Florida. Diagnostics (Basel) 2024; 14:1866. [PMID: 39272651 PMCID: PMC11394003 DOI: 10.3390/diagnostics14171866] [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: 07/12/2024] [Revised: 08/16/2024] [Accepted: 08/21/2024] [Indexed: 09/15/2024] Open
Abstract
Objective: The objective of the study was to establish an AI-driven decision support system by identifying the most important features in the severity of disease for Intensive Care Unit (ICU) with Mechanical Ventilation (MV) requirement, ICU, and InterMediate Care Unit (IMCU) admission for hospitalized patients with COVID-19 in South Florida. The features implicated in the risk factors identified by the model interpretability can be used to forecast treatment plans faster before critical conditions exacerbate. Methods: We analyzed eHR data from 5371 patients diagnosed with COVID-19 from South Florida Memorial Healthcare Systems admitted between March 2020 and January 2021 to predict the need for ICU with MV, ICU, and IMCU admission. A Random Forest classifier was trained on patients' data augmented by SMOTE, collected at hospital admission. We then compared the importance of features utilizing different model interpretability analyses, such as SHAP, MDI, and Permutation Importance. Results: The models for ICU with MV, ICU, and IMCU admission identified the following factors overlapping as the most important predictors among the three outcomes: age, race, sex, BMI, diarrhea, diabetes, hypertension, early stages of kidney disease, and pneumonia. It was observed that individuals over 65 years ('older adults'), males, current smokers, and BMI classified as 'overweight' and 'obese' were at greater risk of severity of illness. The severity was intensified by the co-occurrence of two interacting features (e.g., diarrhea and diabetes). Conclusions: The top features identified by the models' interpretability were from the 'sociodemographic characteristics', 'pre-hospital comorbidities', and 'medications' categories. However, 'pre-hospital comorbidities' played a vital role in different critical conditions. In addition to individual feature importance, the feature interactions also provide crucial information for predicting the most likely outcome of patients' conditions when urgent treatment plans are needed during the surge of patients during the pandemic.
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Affiliation(s)
- Debarshi Datta
- Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL 33431, USA
| | - Subhosit Ray
- Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL 33431, USA
| | - Laurie Martinez
- Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL 33431, USA
| | - David Newman
- Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL 33431, USA
| | - Safiya George Dalmida
- Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL 33431, USA
| | - Javad Hashemi
- College of Engineering & Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA
| | | | - Paula Eckardt
- Memorial Healthcare System, Hollywood, FL 33021, USA
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Li H, Yuan J, Fennell G, Abdulla V, Nistala R, Dandachi D, Ho DKC, Zhang Y. Recent advances in wearable sensors and data analytics for continuous monitoring and analysis of biomarkers and symptoms related to COVID-19. BIOPHYSICS REVIEWS 2023; 4:031302. [PMID: 38510705 PMCID: PMC10903389 DOI: 10.1063/5.0140900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 05/19/2023] [Indexed: 03/22/2024]
Abstract
The COVID-19 pandemic has changed the lives of many people around the world. Based on the available data and published reports, most people diagnosed with COVID-19 exhibit no or mild symptoms and could be discharged home for self-isolation. Considering that a substantial portion of them will progress to a severe disease requiring hospitalization and medical management, including respiratory and circulatory support in the form of supplemental oxygen therapy, mechanical ventilation, vasopressors, etc. The continuous monitoring of patient conditions at home for patients with COVID-19 will allow early determination of disease severity and medical intervention to reduce morbidity and mortality. In addition, this will allow early and safe hospital discharge and free hospital beds for patients who are in need of admission. In this review, we focus on the recent developments in next-generation wearable sensors capable of continuous monitoring of disease symptoms, particularly those associated with COVID-19. These include wearable non/minimally invasive biophysical (temperature, respiratory rate, oxygen saturation, heart rate, and heart rate variability) and biochemical (cytokines, cortisol, and electrolytes) sensors, sensor data analytics, and machine learning-enabled early detection and medical intervention techniques. Together, we aim to inspire the future development of wearable sensors integrated with data analytics, which serve as a foundation for disease diagnostics, health monitoring and predictions, and medical interventions.
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Affiliation(s)
- Huijie Li
- Department of Biomedical Engineering and the Institute of Materials Science, University of Connecticut, Storrs, Connecticut 06269, USA
| | - Jianhe Yuan
- Department of Electrical Engineering and Computer Science, University of Missouri-Columbia, Columbia, Missouri 65211, USA
| | - Gavin Fennell
- Department of Biomedical Engineering and the Institute of Materials Science, University of Connecticut, Storrs, Connecticut 06269, USA
| | - Vagif Abdulla
- Department of Biomedical Engineering and the Institute of Materials Science, University of Connecticut, Storrs, Connecticut 06269, USA
| | - Ravi Nistala
- Division of Nephrology, Department of Medicine, University of Missouri-Columbia, Columbia, Missouri 65212, USA
| | - Dima Dandachi
- Division of Infectious Diseases, Department of Medicine, University of Missouri-Columbia, 1 Hospital Drive, Columbia, Missouri 65212, USA
| | - Dominic K. C. Ho
- Department of Electrical Engineering and Computer Science, University of Missouri-Columbia, Columbia, Missouri 65211, USA
| | - Yi Zhang
- Department of Biomedical Engineering and the Institute of Materials Science, University of Connecticut, Storrs, Connecticut 06269, USA
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Kumar S, Gupta SK, Kumar V, Kumar M, Chaube MK, Naik NS. Ensemble multimodal deep learning for early diagnosis and accurate classification of COVID-19. COMPUTERS & ELECTRICAL ENGINEERING : AN INTERNATIONAL JOURNAL 2022; 103:108396. [PMID: 36160764 PMCID: PMC9485428 DOI: 10.1016/j.compeleceng.2022.108396] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 09/08/2022] [Accepted: 09/12/2022] [Indexed: 05/12/2023]
Abstract
Over the past few years, the awful COVID-19 pandemic effect has become a lethal sickness. The processing of the gathered samples requires extra time due to the use of medical diagnostic equipment, methodologies, and clinical testing procedures for the early diagnosis of infected individuals. An innovative multimodal paradigm for the early diagnosis and precise categorization of COVID-19 is put up as a solution to this issue. To extract distinguishing features from the prepared chest X-ray picture and cough (audio) database, chest X-ray-based and cough-based model are used here. Other public chest X-ray image datasets, and the Coswara cough (audio) dataset containing 92 COVID-19 positive, and 1079 healthy subjects (people) using the deep Uniform-Net, and Convolutional Neural Network (CNN). The weighted sum-rule fusion method and ensemble deep learning algorithms are utilized to further combine the extracted features. For the early diagnosis of patients, the framework offers an accuracy of 98.67%.
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Affiliation(s)
- Santosh Kumar
- Department of Computer Science and Engineering, International Institute of Information Technology (IIIT)-Naya Raipur, Chhattisgarh, 4933661, India
| | - Sachin Kumar Gupta
- School of Electrical and Communication Engineering, Shri Mata Vaishno Devi University, Katra J&K, India
| | - Vinit Kumar
- Galgotias College of Engineering and Technology, Greater Noida, 201306, India
| | - Manoj Kumar
- Faculty of Engineering and Information Sciences, University of Wollongong in Dubai, Dubai Knowledge Park, Dubai, United Arab Emirates
| | - Mithilesh Kumar Chaube
- Department of Mathematical Science, International Institute of Information Technology (IIIT)-Naya Raipur, Chhattisgarh, 4933661, India
| | - Nenavath Srinivas Naik
- Department of Computer Science and Engineering, International Institute of Information Technology (IIIT)-Naya Raipur, Chhattisgarh, 4933661, India
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A Review of Interpretable ML in Healthcare: Taxonomy, Applications, Challenges, and Future Directions. Symmetry (Basel) 2021. [DOI: 10.3390/sym13122439] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
We have witnessed the impact of ML in disease diagnosis, image recognition and classification, and many more related fields. Healthcare is a sensitive field related to people’s lives in which decisions need to be carefully taken based on solid evidence. However, most ML models are complex, i.e., black-box, meaning they do not provide insights into how the problems are solved or why such decisions are proposed. This lack of interpretability is the main reason why some ML models are not widely used yet in real environments such as healthcare. Therefore, it would be beneficial if ML models could provide explanations allowing physicians to make data-driven decisions that lead to higher quality service. Recently, several efforts have been made in proposing interpretable machine learning models to become more convenient and applicable in real environments. This paper aims to provide a comprehensive survey and symmetry phenomena of IML models and their applications in healthcare. The fundamental characteristics, theoretical underpinnings needed to develop IML, and taxonomy for IML are presented. Several examples of how they are applied in healthcare are investigated to encourage and facilitate the use of IML models in healthcare. Furthermore, current limitations, challenges, and future directions that might impact applying ML in healthcare are addressed.
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Shah FM, Joy SKS, Ahmed F, Hossain T, Humaira M, Ami AS, Paul S, Jim MARK, Ahmed S. A Comprehensive Survey of COVID-19 Detection Using Medical Images. SN COMPUTER SCIENCE 2021; 2:434. [PMID: 34485924 PMCID: PMC8401373 DOI: 10.1007/s42979-021-00823-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 08/16/2021] [Indexed: 12/24/2022]
Abstract
The outbreak of the Coronavirus disease 2019 (COVID-19) caused the death of a large number of people and declared as a pandemic by the World Health Organization. Millions of people are infected by this virus and are still getting infected every day. As the cost and required time of conventional Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests to detect COVID-19 is uneconomical and excessive, researchers are trying to use medical images such as X-ray and Computed Tomography (CT) images to detect this disease with the help of Artificial Intelligence (AI)-based systems, to assist in automating the scanning procedure. In this paper, we reviewed some of these newly emerging AI-based models that can detect COVID-19 from X-ray or CT of lung images. We collected information about available research resources and inspected a total of 80 papers till June 20, 2020. We explored and analyzed data sets, preprocessing techniques, segmentation methods, feature extraction, classification, and experimental results which can be helpful for finding future research directions in the domain of automatic diagnosis of COVID-19 disease using AI-based frameworks. It is also reflected that there is a scarcity of annotated medical images/data sets of COVID-19 affected people, which requires enhancing, segmentation in preprocessing, and domain adaptation in transfer learning for a model, producing an optimal result in model performance. This survey can be the starting point for a novice/beginner level researcher to work on COVID-19 classification.
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Affiliation(s)
- Faisal Muhammad Shah
- Department of Computer Science and Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh
| | - Sajib Kumar Saha Joy
- Department of Computer Science and Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh
| | - Farzad Ahmed
- Department of Computer Science and Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh
| | - Tonmoy Hossain
- Department of Computer Science and Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh
| | - Mayeesha Humaira
- Department of Computer Science and Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh
| | - Amit Saha Ami
- Department of Computer Science and Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh
| | - Shimul Paul
- Department of Computer Science and Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh
| | - Md Abidur Rahman Khan Jim
- Department of Computer Science and Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh
| | - Sifat Ahmed
- Department of Computer Science and Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh
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