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Sufian MA, Hamzi W, Sharifi T, Zaman S, Alsadder L, Lee E, Hakim A, Hamzi B. AI-Driven Thoracic X-ray Diagnostics: Transformative Transfer Learning for Clinical Validation in Pulmonary Radiography. J Pers Med 2024; 14:856. [PMID: 39202047 PMCID: PMC11355475 DOI: 10.3390/jpm14080856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2024] [Revised: 07/23/2024] [Accepted: 08/01/2024] [Indexed: 09/03/2024] Open
Abstract
Our research evaluates advanced artificial (AI) methodologies to enhance diagnostic accuracy in pulmonary radiography. Utilizing DenseNet121 and ResNet50, we analyzed 108,948 chest X-ray images from 32,717 patients and DenseNet121 achieved an area under the curve (AUC) of 94% in identifying the conditions of pneumothorax and oedema. The model's performance surpassed that of expert radiologists, though further improvements are necessary for diagnosing complex conditions such as emphysema, effusion, and hernia. Clinical validation integrating Latent Dirichlet Allocation (LDA) and Named Entity Recognition (NER) demonstrated the potential of natural language processing (NLP) in clinical workflows. The NER system achieved a precision of 92% and a recall of 88%. Sentiment analysis using DistilBERT provided a nuanced understanding of clinical notes, which is essential for refining diagnostic decisions. XGBoost and SHapley Additive exPlanations (SHAP) enhanced feature extraction and model interpretability. Local Interpretable Model-agnostic Explanations (LIME) and occlusion sensitivity analysis further enriched transparency, enabling healthcare providers to trust AI predictions. These AI techniques reduced processing times by 60% and annotation errors by 75%, setting a new benchmark for efficiency in thoracic diagnostics. The research explored the transformative potential of AI in medical imaging, advancing traditional diagnostics and accelerating medical evaluations in clinical settings.
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Affiliation(s)
- Md Abu Sufian
- IVR Low-Carbon Research Institute, Chang’an University, Xi’an 710018, China;
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Wahiba Hamzi
- Laboratoire de Biotechnologie Santé et Environnement, Department of Biology, University of Blida, Blida 09000, Algeria
| | - Tazkera Sharifi
- Data Science Architect-Lead Technologist, Booz Allen Hamilton, Texas City, TX 78226, USA
| | - Sadia Zaman
- Department of Physiology, Queen Mary University, London E1 4NS, UK
| | - Lujain Alsadder
- Department of Physiology, Queen Mary University, London E1 4NS, UK
| | - Esther Lee
- Department of Physiology, Queen Mary University, London E1 4NS, UK
| | - Amir Hakim
- Department of Physiology, Queen Mary University, London E1 4NS, UK
| | - Boumediene Hamzi
- Department of Computing and Mathematical Sciences, California Institute of Technology, Caltech, CA 91125, USA
- The Alan Turing Institute, London NW1 2DB, UK
- Department of Mathematics, Imperial College London, London SW7 2AZ, UK
- Department of Mathematics, Gulf University for Science and Technology (GUST), Mubarak Al-Abdullah 32093, Kuwait
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2
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Quantitative drug susceptibility testing for Mycobacterium tuberculosis using unassembled sequencing data and machine learning. PLoS Comput Biol 2024; 20:e1012260. [PMID: 39102420 PMCID: PMC11326700 DOI: 10.1371/journal.pcbi.1012260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 08/15/2024] [Accepted: 06/19/2024] [Indexed: 08/07/2024] Open
Abstract
There remains a clinical need for better approaches to rapid drug susceptibility testing in view of the increasing burden of multidrug resistant tuberculosis. Binary susceptibility phenotypes only capture changes in minimum inhibitory concentration when these cross the critical concentration, even though other changes may be clinically relevant. We developed a machine learning system to predict minimum inhibitory concentration from unassembled whole-genome sequencing data for 13 anti-tuberculosis drugs. We trained, validated and tested the system on 10,859 isolates from the CRyPTIC dataset. Essential agreement rates (predicted MIC within one doubling dilution of observed MIC) were above 92% for first-line drugs, 91% for fluoroquinolones and aminoglycosides, and 90% for new and repurposed drugs, albeit with a significant drop in performance for the very few phenotypically resistant isolates in the latter group. To further validate the model in the absence of external MIC datasets, we predicted MIC and converted values to binary for an external set of 15,239 isolates with binary phenotypes, and compare their performance against a previously validated mutation catalogue, the expected performance of existing molecular assays, and World Health Organization Target Product Profiles. The sensitivity of the model on the external dataset was greater than 90% for all drugs except ethionamide, clofazimine and linezolid. Specificity was greater than 95% for all drugs except ethambutol, ethionamide, bedaquiline, delamanid and clofazimine. The proposed system can provide quantitative susceptibility phenotyping to help guide antimicrobial therapy, although further data collection and validation are required before machine learning can be used clinically for all drugs.
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Serajian M, Marini S, Alanko JN, Noyes NR, Prosperi M, Boucher C. Scalable de novo classification of antibiotic resistance of Mycobacterium tuberculosis. Bioinformatics 2024; 40:i39-i47. [PMID: 38940175 PMCID: PMC11211809 DOI: 10.1093/bioinformatics/btae243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024] Open
Abstract
MOTIVATION World Health Organization estimates that there were over 10 million cases of tuberculosis (TB) worldwide in 2019, resulting in over 1.4 million deaths, with a worrisome increasing trend yearly. The disease is caused by Mycobacterium tuberculosis (MTB) through airborne transmission. Treatment of TB is estimated to be 85% successful, however, this drops to 57% if MTB exhibits multiple antimicrobial resistance (AMR), for which fewer treatment options are available. RESULTS We develop a robust machine-learning classifier using both linear and nonlinear models (i.e. LASSO logistic regression (LR) and random forests (RF)) to predict the phenotypic resistance of Mycobacterium tuberculosis (MTB) for a broad range of antibiotic drugs. We use data from the CRyPTIC consortium to train our classifier, which consists of whole genome sequencing and antibiotic susceptibility testing (AST) phenotypic data for 13 different antibiotics. To train our model, we assemble the sequence data into genomic contigs, identify all unique 31-mers in the set of contigs, and build a feature matrix M, where M[i, j] is equal to the number of times the ith 31-mer occurs in the jth genome. Due to the size of this feature matrix (over 350 million unique 31-mers), we build and use a sparse matrix representation. Our method, which we refer to as MTB++, leverages compact data structures and iterative methods to allow for the screening of all the 31-mers in the development of both LASSO LR and RF. MTB++ is able to achieve high discrimination (F-1 >80%) for the first-line antibiotics. Moreover, MTB++ had the highest F-1 score in all but three classes and was the most comprehensive since it had an F-1 score >75% in all but four (rare) antibiotic drugs. We use our feature selection to contextualize the 31-mers that are used for the prediction of phenotypic resistance, leading to some insights about sequence similarity to genes in MEGARes. Lastly, we give an estimate of the amount of data that is needed in order to provide accurate predictions. AVAILABILITY The models and source code are publicly available on Github at https://github.com/M-Serajian/MTB-Pipeline.
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Affiliation(s)
- Mohammadali Serajian
- Department of Computer and Information Science and Engineering, University of Florida, 1889 Museum Road, Gainesville, Florida 32611, United States
| | - Simone Marini
- Department of Epidemiology, University of Florida, PO Box 100231, Gainesville, Florida 32601, United States
| | - Jarno N Alanko
- Department of Computer Science, University of Helsinki, P.O. Box 4, Helsinki 00014, Finland
| | - Noelle R Noyes
- Department of Veterinary Population Medicine, University of Minnesota, 1365 Gortner Avenue, St. Paul, Minnesota 55108, United States
| | - Mattia Prosperi
- Department of Epidemiology, University of Florida, PO Box 100231, Gainesville, Florida 32601, United States
| | - Christina Boucher
- Department of Computer and Information Science and Engineering, University of Florida, 1889 Museum Road, Gainesville, Florida 32611, United States
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Duffey M, Shafer RW, Timm J, Burrows JN, Fotouhi N, Cockett M, Leroy D. Combating antimicrobial resistance in malaria, HIV and tuberculosis. Nat Rev Drug Discov 2024; 23:461-479. [PMID: 38750260 DOI: 10.1038/s41573-024-00933-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/15/2024] [Indexed: 06/07/2024]
Abstract
Antimicrobial resistance poses a significant threat to the sustainability of effective treatments against the three most prevalent infectious diseases: malaria, human immunodeficiency virus (HIV) infection and tuberculosis. Therefore, there is an urgent need to develop novel drugs and treatment protocols capable of reducing the emergence of resistance and combating it when it does occur. In this Review, we present an overview of the status and underlying molecular mechanisms of drug resistance in these three diseases. We also discuss current strategies to address resistance during the research and development of next-generation therapies. These strategies vary depending on the infectious agent and the array of resistance mechanisms involved. Furthermore, we explore the potential for cross-fertilization of knowledge and technology among these diseases to create innovative approaches for minimizing drug resistance and advancing the discovery and development of new anti-infective treatments. In conclusion, we advocate for the implementation of well-defined strategies to effectively mitigate and manage resistance in all interventions against infectious diseases.
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Affiliation(s)
- Maëlle Duffey
- Medicines for Malaria Venture (MMV), R&D Department/Drug Discovery, ICC, Geneva, Switzerland
- The Global Antibiotic Research & Development Partnership, Geneva, Switzerland
| | - Robert W Shafer
- Department of Medicine/Infectious Diseases, Stanford University, Palo Alto, CA, USA
| | | | - Jeremy N Burrows
- Medicines for Malaria Venture (MMV), R&D Department/Drug Discovery, ICC, Geneva, Switzerland
| | | | | | - Didier Leroy
- Medicines for Malaria Venture (MMV), R&D Department/Drug Discovery, ICC, Geneva, Switzerland.
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Rusic D, Kumric M, Seselja Perisin A, Leskur D, Bukic J, Modun D, Vilovic M, Vrdoljak J, Martinovic D, Grahovac M, Bozic J. Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence. Microorganisms 2024; 12:842. [PMID: 38792673 PMCID: PMC11123121 DOI: 10.3390/microorganisms12050842] [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: 03/16/2024] [Revised: 04/16/2024] [Accepted: 04/19/2024] [Indexed: 05/26/2024] Open
Abstract
Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.
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Affiliation(s)
- Doris Rusic
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Marko Kumric
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Ana Seselja Perisin
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Dario Leskur
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Josipa Bukic
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Darko Modun
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Marino Vilovic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Josip Vrdoljak
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Dinko Martinovic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Department of Maxillofacial Surgery, University Hospital of Split, Spinciceva 1, 21000 Split, Croatia
| | - Marko Grahovac
- Department of Pharmacology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia;
| | - Josko Bozic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
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6
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Baddal B, Taner F, Uzun Ozsahin D. Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review. Diagnostics (Basel) 2024; 14:484. [PMID: 38472956 DOI: 10.3390/diagnostics14050484] [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: 12/16/2023] [Revised: 01/23/2024] [Accepted: 02/19/2024] [Indexed: 03/14/2024] Open
Abstract
Healthcare-associated infections (HAIs) are the most common adverse events in healthcare and constitute a major global public health concern. Surveillance represents the foundation for the effective prevention and control of HAIs, yet conventional surveillance is costly and labor intensive. Artificial intelligence (AI) and machine learning (ML) have the potential to support the development of HAI surveillance algorithms for the understanding of HAI risk factors, the improvement of patient risk stratification as well as the prediction and timely detection and prevention of infections. AI-supported systems have so far been explored for clinical laboratory testing and imaging diagnosis, antimicrobial resistance profiling, antibiotic discovery and prediction-based clinical decision support tools in terms of HAIs. This review aims to provide a comprehensive summary of the current literature on AI applications in the field of HAIs and discuss the future potentials of this emerging technology in infection practice. Following the PRISMA guidelines, this study examined the articles in databases including PubMed and Scopus until November 2023, which were screened based on the inclusion and exclusion criteria, resulting in 162 included articles. By elucidating the advancements in the field, we aim to highlight the potential applications of AI in the field, report related issues and shortcomings and discuss the future directions.
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Affiliation(s)
- Buket Baddal
- Department of Medical Microbiology and Clinical Microbiology, Faculty of Medicine, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
- DESAM Research Institute, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
| | - Ferdiye Taner
- Department of Medical Microbiology and Clinical Microbiology, Faculty of Medicine, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
- DESAM Research Institute, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
| | - Dilber Uzun Ozsahin
- Department of Medical Diagnostic Imaging, College of Health Science, University of Sharjah, Sharjah 27272, United Arab Emirates
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates
- Operational Research Centre in Healthcare, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
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Rehman A, Mujahid M, Saba T, Jeon G. Optimised stacked machine learning algorithms for genomics and genetics disorder detection in the healthcare industry. Funct Integr Genomics 2024; 24:23. [PMID: 38305949 DOI: 10.1007/s10142-024-01289-z] [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: 11/20/2023] [Revised: 12/22/2023] [Accepted: 01/02/2024] [Indexed: 02/03/2024]
Abstract
With recent advances in precision medicine and healthcare computing, there is an enormous demand for developing machine learning algorithms in genomics to enhance the rapid analysis of disease disorders. Technological advancement in genomics and imaging provides clinicians with enormous amounts of data, but prediction is still mostly subjective, resulting in problematic medical treatment. Machine learning is being employed in several domains of the healthcare sector, encompassing clinical research, early disease identification, and medicinal innovation with a historical perspective. The main objective of this study is to detect patients who, based on several medical standards, are more susceptible to having a genetic disorder. A genetic disease prediction algorithm was employed, leveraging the patient's health history to evaluate the probability of diagnosing a genetic disorder. We developed a computationally efficient machine learning approach to predict the overall lifespan of patients with a genomics disorder and to classify and predict patients with a genetic disease. The SVM, RF, and ETC are stacked using two-layer meta-estimators to develop the proposed model. The first layer comprises all the baseline models employed to predict the outcomes based on the dataset. The second layer comprises a component known as a meta-classifier. Results from the experiment indicate that the model achieved an accuracy of 90.45% and a recall score of 90.19%. The area under the curve (AUC) for mitochondrial diseases is 98.1%; for multifactorial diseases, it is 97.5%; and for single-gene inheritance, it is 98.8%. The proposed approach presents a novel method for predicting patient prognosis in a manner that is unbiased, accurate, and comprehensive. The proposed approach outperforms human professionals using the current clinical standard for genetic disease classification in terms of identification accuracy. The implementation of stacked will significantly improve the field of biomedical research by improving the anticipation of genetic diseases.
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Affiliation(s)
- Amjad Rehman
- Artificial Intelligence & Data Analytics Lab, CCIS, Prince Sultan University, Riyadh, 11586, Saudi Arabia
| | - Muhammad Mujahid
- Artificial Intelligence & Data Analytics Lab, CCIS, Prince Sultan University, Riyadh, 11586, Saudi Arabia
| | - Tanzila Saba
- Artificial Intelligence & Data Analytics Lab, CCIS, Prince Sultan University, Riyadh, 11586, Saudi Arabia
| | - Gwanggil Jeon
- Artificial Intelligence & Data Analytics Lab, CCIS, Prince Sultan University, Riyadh, 11586, Saudi Arabia.
- Department of Embedded Systems Engineering, Incheon National University, Incheon, 610101, Korea.
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Zahra Q, Gul J, Shah AR, Yasir M, Karim AM. Antibiotic resistance genes prevalence prediction and interpretation in beaches affected by urban wastewater discharge. One Health 2023; 17:100642. [PMID: 38024281 PMCID: PMC10665162 DOI: 10.1016/j.onehlt.2023.100642] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 10/10/2023] [Indexed: 12/01/2023] Open
Abstract
Background The annual death toll of over 1.2 million worldwide is attributed to infections caused by resistant bacteria, driven by the significant impact of antibiotic misuse and overuse in spreading these bacteria and their associated antibiotic resistance genes (ARGs). While limited data suggest the presence of ARGs in beach environments, efficient prediction tools are needed for monitoring and detecting ARGs to ensure public health safety. This study aims to develop interpretable machine learning methods for predicting ARGs in beach waters, addressing the challenge of black-box models and enhancing our understanding of their internal mechanisms. Methods In this study, we systematically collected beach water samples and subsequently isolated bacteria from these samples using various differential and selective media supplemented with different antibiotics. Resistance profiles of bacteria were determined by using Kirby-Bauer disk diffusion method. Further, ARGs were enumerated by using the quantitative polymerase chain reaction (qPCR) to detect and quantify ARGs. The obtained qPCR data and hydro-meteorological were used to create an ML model with high prediction performance and we further used two explainable artificial intelligence (xAI) model-agnostic interpretation methods to describe the internal behavior of ML model. Results Using qPCR, we detected blaCTX-M, blaNDM, blaCMY, blaOXA, blatetX, blasul1, and blaaac(6'-Ib-cr) in the beach waters. Further, we developed ML prediction models for blaaac(6'-Ib-cr), blasul1, and blatetX using the hydro-metrological and qPCR-derived data and the models demonstrated strong performance, with R2 values of 0.957, 0.997, and 0.976, respectively. Conclusions Our findings show that environmental factors, such as water temperature, precipitation, and tide, are among the important predictors of the abundance of resistance genes at beaches.
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Affiliation(s)
- Qandeel Zahra
- Azra Naheed Medical College, Lahore 54000, Punjab, Pakistan
| | - Jawaria Gul
- Al-Nafees Medical College & Hospital, Islamabad 44000, Pakistan
| | - Ali Raza Shah
- Azra Naheed Medical College, Lahore 54000, Punjab, Pakistan
| | - Muhammad Yasir
- Special Infectious Agents Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Asad Mustafa Karim
- Department of Oriental Medicine and Biotechnology, College of Life Sciences, Kyung Hee University, Yongin-si 17104, South Korea
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Shaw B, von Bredow B, Tsan A, Garner O, Yang S. Clinical Whole-Genome Sequencing Assay for Rapid Mycobacterium tuberculosis Complex First-Line Drug Susceptibility Testing and Phylogenetic Relatedness Analysis. Microorganisms 2023; 11:2538. [PMID: 37894195 PMCID: PMC10609454 DOI: 10.3390/microorganisms11102538] [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: 07/26/2023] [Revised: 09/25/2023] [Accepted: 10/10/2023] [Indexed: 10/29/2023] Open
Abstract
The global rise of drug resistant tuberculosis has highlighted the need for improved diagnostic technologies that provide rapid and reliable drug resistance results. Here, we develop and validate a whole genome sequencing (WGS)-based test for identification of mycobacterium tuberculosis complex (MTB) drug resistance to rifampin, isoniazid, pyrazinamide, ethambutol, and streptomycin. Through comparative analysis of drug resistance results from WGS-based testing and phenotypic drug susceptibility testing (DST) of 38 clinical MTB isolates from patients receiving care in Los Angeles, CA, we found an overall concordance between methods of 97.4% with equivalent performance across culture media. Critically, prospective analysis of 11 isolates showed that WGS-based testing provides results an average of 36 days faster than phenotypic culture-based methods. We showcase the additional benefits of WGS data by investigating a suspected laboratory contamination event and using phylogenetic analysis to search for cryptic local transmission, finding no evidence of community spread amongst our patient population in the past six years. WGS-based testing for MTB drug resistance has the potential to greatly improve diagnosis of drug resistant MTB by accelerating turnaround time while maintaining accuracy and providing additional benefits for infection control, lab safety, and public health applications.
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Affiliation(s)
- Bennett Shaw
- Department of Pathology and Laboratory Medicine, UCLA David Geffen School of Medicine, Los Angeles, CA 90095, USA; (B.S.); (B.v.B.); (A.T.); (O.G.)
| | - Benjamin von Bredow
- Department of Pathology and Laboratory Medicine, UCLA David Geffen School of Medicine, Los Angeles, CA 90095, USA; (B.S.); (B.v.B.); (A.T.); (O.G.)
- Department of Pathology, Oakland University William Beaumont School of Medicine, Rochester, MI 48309, USA
| | - Allison Tsan
- Department of Pathology and Laboratory Medicine, UCLA David Geffen School of Medicine, Los Angeles, CA 90095, USA; (B.S.); (B.v.B.); (A.T.); (O.G.)
| | - Omai Garner
- Department of Pathology and Laboratory Medicine, UCLA David Geffen School of Medicine, Los Angeles, CA 90095, USA; (B.S.); (B.v.B.); (A.T.); (O.G.)
| | - Shangxin Yang
- Department of Pathology and Laboratory Medicine, UCLA David Geffen School of Medicine, Los Angeles, CA 90095, USA; (B.S.); (B.v.B.); (A.T.); (O.G.)
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Perea-Jacobo R, Paredes-Gutiérrez GR, Guerrero-Chevannier MÁ, Flores DL, Muñiz-Salazar R. Machine Learning of the Whole Genome Sequence of Mycobacterium tuberculosis: A Scoping PRISMA-Based Review. Microorganisms 2023; 11:1872. [PMID: 37630431 PMCID: PMC10456961 DOI: 10.3390/microorganisms11081872] [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] [Received: 06/16/2023] [Revised: 07/13/2023] [Accepted: 07/14/2023] [Indexed: 08/27/2023] Open
Abstract
Tuberculosis (TB) remains one of the most significant global health problems, posing a significant challenge to public health systems worldwide. However, diagnosing drug-resistant tuberculosis (DR-TB) has become increasingly challenging due to the rising number of multidrug-resistant (MDR-TB) cases, despite the development of new TB diagnostic tools. Even the World Health Organization-recommended methods such as Xpert MTB/XDR or Truenat are unable to detect all the Mycobacterium tuberculosis genome mutations associated with drug resistance. While Whole Genome Sequencing offers a more precise DR profile, the lack of user-friendly bioinformatics analysis applications hinders its widespread use. This review focuses on exploring various artificial intelligence models for predicting DR-TB profiles, analyzing relevant English-language articles using the PRISMA methodology through the Covidence platform. Our findings indicate that an Artificial Neural Network is the most commonly employed method, with non-statistical dimensionality reduction techniques preferred over traditional statistical approaches such as Principal Component Analysis or t-distributed Stochastic Neighbor Embedding.
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Affiliation(s)
- Ricardo Perea-Jacobo
- Facultad de Ingeniería Arquitectura y Diseño, Universidad Autónoma de Baja California, Campus Ensenada, Ensenada 22860, Mexico; (R.P.-J.); (G.R.P.-G.); (M.Á.G.-C.)
- Escuela de Ciencias de la Salud, Universidad Autónoma de Baja California, Campus Ensenada, Ensenada 22890, Mexico
| | - Guillermo René Paredes-Gutiérrez
- Facultad de Ingeniería Arquitectura y Diseño, Universidad Autónoma de Baja California, Campus Ensenada, Ensenada 22860, Mexico; (R.P.-J.); (G.R.P.-G.); (M.Á.G.-C.)
| | - Miguel Ángel Guerrero-Chevannier
- Facultad de Ingeniería Arquitectura y Diseño, Universidad Autónoma de Baja California, Campus Ensenada, Ensenada 22860, Mexico; (R.P.-J.); (G.R.P.-G.); (M.Á.G.-C.)
| | - Dora-Luz Flores
- Facultad de Ingeniería Arquitectura y Diseño, Universidad Autónoma de Baja California, Campus Ensenada, Ensenada 22860, Mexico; (R.P.-J.); (G.R.P.-G.); (M.Á.G.-C.)
| | - Raquel Muñiz-Salazar
- Escuela de Ciencias de la Salud, Universidad Autónoma de Baja California, Campus Ensenada, Ensenada 22890, Mexico
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Edalatmand A, McArthur AG. CARD*Shark: automated prioritization of literature curation for the Comprehensive Antibiotic Resistance Database. Database (Oxford) 2023; 2023:7133783. [PMID: 37079891 PMCID: PMC10118295 DOI: 10.1093/database/baad023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 03/01/2023] [Accepted: 03/27/2023] [Indexed: 04/22/2023]
Abstract
Scientific literature is published at a rate that makes manual data extraction a highly time-consuming task. The Comprehensive Antibiotic Resistance Database (CARD) utilizes literature to curate information on antimicrobial resistance genes and to enable time-efficient triage of publications we have developed a classification algorithm for identifying publications describing first reports of new resistance genes. Trained on publications contained in the CARD, CARD*Shark downloads, processes and identifies publications recently added to PubMed that should be reviewed by biocurators. With CARD*Shark, we can minimize the monthly scope of articles a biocurator reviews from hundreds of articles to a few dozen, drastically improving the speed of curation while ensuring no relevant publications are overlooked. Database URL http://card.mcmaster.ca.
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Affiliation(s)
- Arman Edalatmand
- David Braley Centre for Antibiotic Discovery, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4L8, Canada
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4L8, Canada
- Department of Biochemistry and Biomedical Sciences, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4L8, Canada
| | - Andrew G McArthur
- David Braley Centre for Antibiotic Discovery, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4L8, Canada
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4L8, Canada
- Department of Biochemistry and Biomedical Sciences, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4L8, Canada
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12
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Noorizhab MNF, Zainal Abidin N, Teh LK, Tang TH, Onyejepu N, Kunle-Ope C, Tochukwu NE, Sheshi MA, Nwafor T, Akinwale OP, Ismail AI, Nor NM, Salleh MZ. Exploration of the diversity of multi-drug resistant Mycobacterium tuberculosis complex in Lagos, Nigeria using WGS: Distribution of lineages, drug resistance patterns and genetic mutations. Tuberculosis (Edinb) 2023; 140:102343. [PMID: 37080082 DOI: 10.1016/j.tube.2023.102343] [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: 12/01/2022] [Revised: 03/19/2023] [Accepted: 04/12/2023] [Indexed: 04/22/2023]
Abstract
Multidrug-resistant (MDR) or extensively drug-resistant (XDR) Tuberculosis (TB) is a major challenge to global TB control. Therefore, accurate tracing of in-country MDR-TB transmission are crucial for the development of optimal TB management strategies. This study aimed to investigate the diversity of MTBC in Nigeria. The lineage and drug-resistance patterns of the clinical MTBC isolates of TB patients in Southwestern region of Nigeria were determined using the WGS approach. The phenotypic DST of the isolates was determined for nine anti-TB drugs. The sequencing achieved average genome coverage of 65.99X. The most represented lineages were L4 (n = 52, 83%), L1 (n = 8, 12%), L2 (n = 2, 3%) and L5 (n = 1, 2%), suggesting a diversified MTB population. In term of detection of M/XDR-TB, while mutations in katG and rpoB genes are the strong predictors for the presence of M/XDR-TB, the current study also found the lack of good genetic markers for drug resistance amongst the MTBC in Nigeria which may pose greater problems on local tuberculosis management efforts. This high-resolution molecular epidemiological data provides valuable insights into the mechanistic for M/XDR TB in Lagos, Nigeria.
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Affiliation(s)
- Mohd Nur Fakhruzzaman Noorizhab
- Integrative Pharmacogenomics Institute, Universiti Teknologi MARA Selangor Branch, Puncak Alam Campus, Selangor, Malaysia; Faculty of Pharmacy, Universiti Teknologi MARA Selangor Branch, Puncak Alam Campus, Selangor, Malaysia
| | - Norzuliana Zainal Abidin
- Integrative Pharmacogenomics Institute, Universiti Teknologi MARA Selangor Branch, Puncak Alam Campus, Selangor, Malaysia
| | - Lay Kek Teh
- Integrative Pharmacogenomics Institute, Universiti Teknologi MARA Selangor Branch, Puncak Alam Campus, Selangor, Malaysia; Faculty of Pharmacy, Universiti Teknologi MARA Selangor Branch, Puncak Alam Campus, Selangor, Malaysia
| | - Thean Hock Tang
- Advance Medical & Dental Institute (AMDI), Universiti Sains Malaysia, Kepala Batas, Pulau Pinang, Malaysia
| | - Nneka Onyejepu
- Microbiology Department, Nigerian Institute of Medical Research (NIMR), Lagos, Nigeria
| | - Chioma Kunle-Ope
- Microbiology Department, Nigerian Institute of Medical Research (NIMR), Lagos, Nigeria
| | - Nwanneka E Tochukwu
- Microbiology Department, Nigerian Institute of Medical Research (NIMR), Lagos, Nigeria
| | | | - Timothy Nwafor
- Public Health and Epidemiology Department, Nigerian Institute of Medical Research (NIMR), Lagos, Nigeria
| | - Olaoluwa P Akinwale
- Public Health and Epidemiology Department, Nigerian Institute of Medical Research (NIMR), Lagos, Nigeria.
| | | | - Norazmi Mohd Nor
- School of Health Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kelantan, Malaysia
| | - Mohd Zaki Salleh
- Integrative Pharmacogenomics Institute, Universiti Teknologi MARA Selangor Branch, Puncak Alam Campus, Selangor, Malaysia.
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13
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Naidu A, Nayak SS, Lulu S S, Sundararajan V. Advances in computational frameworks in the fight against TB: The way forward. Front Pharmacol 2023; 14:1152915. [PMID: 37077815 PMCID: PMC10106641 DOI: 10.3389/fphar.2023.1152915] [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: 01/28/2023] [Accepted: 03/20/2023] [Indexed: 04/05/2023] Open
Abstract
Around 1.6 million people lost their life to Tuberculosis in 2021 according to WHO estimates. Although an intensive treatment plan exists against the causal agent, Mycobacterium Tuberculosis, evolution of multi-drug resistant strains of the pathogen puts a large number of global populations at risk. Vaccine which can induce long-term protection is still in the making with many candidates currently in different phases of clinical trials. The COVID-19 pandemic has further aggravated the adversities by affecting early TB diagnosis and treatment. Yet, WHO remains adamant on its "End TB" strategy and aims to substantially reduce TB incidence and deaths by the year 2035. Such an ambitious goal would require a multi-sectoral approach which would greatly benefit from the latest computational advancements. To highlight the progress of these tools against TB, through this review, we summarize recent studies which have used advanced computational tools and algorithms for-early TB diagnosis, anti-mycobacterium drug discovery and in the designing of the next-generation of TB vaccines. At the end, we give an insight on other computational tools and Machine Learning approaches which have successfully been applied in biomedical research and discuss their prospects and applications against TB.
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Affiliation(s)
| | | | | | - Vino Sundararajan
- Department of Biotechnology, School of Bio Sciences and Technology, VIT University, Vellore, India
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14
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Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation. Antibiotics (Basel) 2023; 12:antibiotics12030523. [PMID: 36978390 PMCID: PMC10044311 DOI: 10.3390/antibiotics12030523] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/01/2023] [Accepted: 03/03/2023] [Indexed: 03/08/2023] Open
Abstract
Antimicrobial resistance (AMR) is emerging as a potential threat to many lives worldwide. It is very important to understand and apply effective strategies to counter the impact of AMR and its mutation from a medical treatment point of view. The intersection of artificial intelligence (AI), especially deep learning/machine learning, has led to a new direction in antimicrobial identification. Furthermore, presently, the availability of huge amounts of data from multiple sources has made it more effective to use these artificial intelligence techniques to identify interesting insights into AMR genes such as new genes, mutations, drug identification, conditions favorable to spread, and so on. Therefore, this paper presents a review of state-of-the-art challenges and opportunities. These include interesting input features posing challenges in use, state-of-the-art deep-learning/machine-learning models for robustness and high accuracy, challenges, and prospects to apply these techniques for practical purposes. The paper concludes with the encouragement to apply AI to the AMR sector with the intention of practical diagnosis and treatment, since presently most studies are at early stages with minimal application in the practice of diagnosis and treatment of disease.
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15
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Iftikhar S, Karim AM, Karim AM, Karim MA, Aslam M, Rubab F, Malik SK, Kwon JE, Hussain I, Azhar EI, Kang SC, Yasir M. Prediction and interpretation of antibiotic-resistance genes occurrence at recreational beaches using machine learning models. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 328:116969. [PMID: 36495825 DOI: 10.1016/j.jenvman.2022.116969] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 11/22/2022] [Accepted: 12/03/2022] [Indexed: 06/17/2023]
Abstract
Antibiotic-resistant bacteria and antibiotic resistance genes (ARGs) are pollutants of worldwide concern that seriously threaten public health and ecosystems. Machine learning (ML) prediction models have been applied to predict ARGs in beach waters. However, the existing studies were conducted at a single location and had low prediction performance. Moreover, ML models are "black boxes" that do not reveal their predictions' internal nuances and mechanisms. This lack of transparency and trust can result in serious consequences when using these models in high-stakes decisions. In this study, we developed a gradient boosted regression tree based (GBRT) ML model and then described its behavior using six explainable artificial intelligence (XAI) model-agnostic explanation methods. We used hydro-meteorological and qPCR data from the beaches in South Korea and Pakistan and developed ML prediction models for aac (6'-lb-cr), sul1, and tetX with 10-fold time-blocked cross-validation performances of 4.9, 2.06 and 4.4 root mean squared logarithmic error, respectively. We then analyzed the local and global behavior of the developed ML model using four interpretation methods. The developed ML models showed that water temperature, precipitation and tide are the most important predictors for prediction of ARGs at recreational beaches. We show that the model-agnostic interpretation methods not only explain the behavior of the ML model but also provide insights into the behavior of the ML model under new unseen conditions. Moreover, these post-processing techniques can be a debugging tool for ML-based modeling.
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Affiliation(s)
- Sara Iftikhar
- Department of Electrical Engineering and Computer Sciences, National University of Sciences and Technology (NUST), Islamabad 64000, Pakistan
| | - Asad Mustafa Karim
- Department of Biotechnology, College of Life Sciences, Kyung Hee University, Yongin-si 17104, Republic of Korea
| | - Aoun Murtaza Karim
- Institute of Geology and Geophysics, University of Chinese Academy of Sciences, Beijing, China; Institute of Geology, University of the Punjab, Lahore 54590, Pakistan
| | | | - Muhammad Aslam
- Department of Artificial Intelligence, Sejong University, Seoul, 05006, Republic of Korea
| | - Fazila Rubab
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah Campus, Wah Cantt, 47040, Pakistan
| | - Sumera Kausar Malik
- Department of Bioscience and Biotechnology, The University of Suwon, Hwaseong-si, Gyeonggi-do 18323, Republic of Korea
| | - Jeong Eun Kwon
- Department of Biotechnology, College of Life Sciences, Kyung Hee University, Yongin-si 17104, Republic of Korea
| | - Imran Hussain
- Environmental Biotechnology Lab, Department of Biotechnology Comsats University Islamabad, Abbottabad Campus, Pakistan
| | - Esam I Azhar
- Special Infectious Agents Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia; Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Se Chan Kang
- Department of Biotechnology, College of Life Sciences, Kyung Hee University, Yongin-si 17104, Republic of Korea.
| | - Muhammad Yasir
- Special Infectious Agents Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia; Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
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16
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Li S, Wu J, Ma N, Liu W, Shao M, Ying N, Zhu L. Prediction of genome-wide imipenem resistance features in Klebsiella pneumoniae using machine learning. J Med Microbiol 2023; 72. [PMID: 36753438 DOI: 10.1099/jmm.0.001657] [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: 02/09/2023] Open
Abstract
Introduction. The resistance rate of Klebsiella pneumoniae (K. pneumoniae) to imipenem is increasing year by year, and the imipenem resistance mechanism of K. pneumoniae is complex. Therefore, it is urgent to develop new strategies to explore the resistance mechanism of imipenem for its effective and accurate use in clinical practice.Hypothesis/Gap sStatement. Machine learning could identify resistance features and biological process that influence microbial resistance from whole-genome sequencing (WGS) data.Aims. This work aimed to predict imipenem resistance genetic features in K. pneumoniae from whole-genome k-mer features, and analyse their function for understanding its resistance mechanism.Methods. This study analysed WGS data of K. pneumoniae combined with resistance phenotype for imipenem, and established K. pneumoniae to imipenem genotype-phenotype model to predict resistance features using chi-squared test and random forest. An external clinical dataset was used to verify prediction power of resistance features. The potential genes were identified through alignment the resistance features with the K. pneumoniae reference genome using blastn, the functions of potential genes were further analysed to explore its resistance-related signalling pathways with GO and KEGG analysis, the resistance sequence patterns were screened using streme software. Finally, the resistance features were combined and modelled through four machine-learning algorithms (logistic regression, SVM, GBDT and XGBoost) to evaluate their phenotype prediction ability.Results. A total of 16 670 imipenem resistance features were predicted from genotype-phenotype model. The 30 potential genes were identified by annotating the resistance features and corresponded to known antibiotic-related genes (mdtM, dedA, rne, etc.). GO and KEGG pathway analyses indicated the possible association of imipenem resistance with metabolism process and cell membrane. CRYCAGCDN and CGRDAAAN were found from the imipenem resistance features, which were widely presented in the reported β-lactam resistance genes (bla SHV, bla CTX-M, bla TEM, etc.), and YCYAGCMCAST with metabolic functions (organic substance metabolic process, nitrogen compound metabolic process and cellular metabolic process) was identified from the top 50 resistance features. The 25 resistance genes in the training dataset included 19 genes in the external dataset, which verified the accuracy of prediction. The area under curve values of logistics regression, SVM, GBDT and XGBoost were 0.965, 0.966, 0.969 and 0.969, respectively, indicating that the imipenem resistance features have a strong prediction power.Conclusion. Machine-learning methods could effectively predict the imipenem resistance feature in K. pneumoniae, and provide resistance sequence profiles for predicting resistance phenotype and exploring potential resistance mechanisms. It provides an important insight into the potential therapeutic strategies of K. pneumoniae resistance to imipenem, and speed up the application of machine learning in routine diagnosis.
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Affiliation(s)
- Shanshan Li
- College of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, PR China
| | - Jun Wu
- Lin'an Center for Disease Control and Prevention, Lin'an, 311300, PR China
| | - Nan Ma
- College of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, PR China
| | - Wenjia Liu
- College of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, PR China.,College of Electronics and Information Engineering, Hangzhou Dianzi University, Hangzhou 310018, PR China
| | - Mengjie Shao
- College of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, PR China
| | - Nanjiao Ying
- College of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, PR China.,Institute of Biomedical Engineering and Instrument, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, PR China
| | - Lei Zhu
- College of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, PR China.,Institute of Biomedical Engineering and Instrument, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, PR China
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17
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Liu X, Flanagan C, Fang J, Lei Y, McGrath L, Wang J, Guo X, Guo J, McGrath H, Han Y. Comparative analysis of popular predictors for difficult laryngoscopy using hybrid intelligent detection methods. Heliyon 2022; 8:e11761. [DOI: 10.1016/j.heliyon.2022.e11761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 09/27/2022] [Accepted: 11/14/2022] [Indexed: 11/24/2022] Open
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18
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Liang S, Ma J, Wang G, Shao J, Li J, Deng H, Wang C, Li W. The Application of Artificial Intelligence in the Diagnosis and Drug Resistance Prediction of Pulmonary Tuberculosis. Front Med (Lausanne) 2022; 9:935080. [PMID: 35966878 PMCID: PMC9366014 DOI: 10.3389/fmed.2022.935080] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 06/13/2022] [Indexed: 11/30/2022] Open
Abstract
With the increasing incidence and mortality of pulmonary tuberculosis, in addition to tough and controversial disease management, time-wasting and resource-limited conventional approaches to the diagnosis and differential diagnosis of tuberculosis are still awkward issues, especially in countries with high tuberculosis burden and backwardness. In the meantime, the climbing proportion of drug-resistant tuberculosis poses a significant hazard to public health. Thus, auxiliary diagnostic tools with higher efficiency and accuracy are urgently required. Artificial intelligence (AI), which is not new but has recently grown in popularity, provides researchers with opportunities and technical underpinnings to develop novel, precise, rapid, and automated implements for pulmonary tuberculosis care, including but not limited to tuberculosis detection. In this review, we aimed to introduce representative AI methods, focusing on deep learning and radiomics, followed by definite descriptions of the state-of-the-art AI models developed using medical images and genetic data to detect pulmonary tuberculosis, distinguish the infection from other pulmonary diseases, and identify drug resistance of tuberculosis, with the purpose of assisting physicians in deciding the appropriate therapeutic schedule in the early stage of the disease. We also enumerated the challenges in maximizing the impact of AI in this field such as generalization and clinical utility of the deep learning models.
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Affiliation(s)
- Shufan Liang
- Department of Respiratory and Critical Care Medicine, Med-X Center for Manufacturing, Frontiers Science Center for Disease-Related Molecular Network, West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
- Precision Medicine Key Laboratory of Sichuan Province, Precision Medicine Research Center, West China Hospital, Sichuan University, Chengdu, China
| | - Jiechao Ma
- AI Lab, Deepwise Healthcare, Beijing, China
| | - Gang Wang
- Precision Medicine Key Laboratory of Sichuan Province, Precision Medicine Research Center, West China Hospital, Sichuan University, Chengdu, China
| | - Jun Shao
- Department of Respiratory and Critical Care Medicine, Med-X Center for Manufacturing, Frontiers Science Center for Disease-Related Molecular Network, West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Jingwei Li
- Department of Respiratory and Critical Care Medicine, Med-X Center for Manufacturing, Frontiers Science Center for Disease-Related Molecular Network, West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Hui Deng
- Department of Respiratory and Critical Care Medicine, Med-X Center for Manufacturing, Frontiers Science Center for Disease-Related Molecular Network, West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
- Precision Medicine Key Laboratory of Sichuan Province, Precision Medicine Research Center, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Hui Deng,
| | - Chengdi Wang
- Department of Respiratory and Critical Care Medicine, Med-X Center for Manufacturing, Frontiers Science Center for Disease-Related Molecular Network, West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
- Chengdi Wang,
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, Med-X Center for Manufacturing, Frontiers Science Center for Disease-Related Molecular Network, West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
- Weimin Li,
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