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Jacot D, Gizha S, Orny C, Fernandes M, Tricoli C, Marcelpoil R, Prod'hom G, Volle JM, Greub G, Croxatto A. Development and evaluation of an artificial intelligence for bacterial growth monitoring in clinical bacteriology. J Clin Microbiol 2024; 62:e0165123. [PMID: 38572970 PMCID: PMC11077979 DOI: 10.1128/jcm.01651-23] [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: 12/08/2023] [Accepted: 03/11/2024] [Indexed: 04/05/2024] Open
Abstract
In clinical bacteriology laboratories, reading and processing of sterile plates remain a significant part of the routine workload (30%-40% of the plates). Here, an algorithm was developed for bacterial growth detection starting with any type of specimens and using the most common media in bacteriology. The growth prediction performance of the algorithm for automatic processing of sterile plates was evaluated not only at 18-24 h and 48 h but also at earlier timepoints toward the development of an early growth monitoring system. A total of 3,844 plates inoculated with representative clinical specimens were used. The plates were imaged 15 times, and two different microbiologists read the images randomly and independently, creating 99,944 human ground truths. The algorithm was able, at 48 h, to discriminate growth from no growth with a sensitivity of 99.80% (five false-negative [FN] plates out of 3,844) and a specificity of 91.97%. At 24 h, sensitivity and specificity reached 99.08% and 93.37%, respectively. Interestingly, during human truth reading, growth was reported as early as 4 h, while at 6 h, half of the positive plates were already showing some growth. In this context, automated early growth monitoring in case of normally sterile samples is envisioned to provide added value to the microbiologists, enabling them to prioritize reading and to communicate early detection of bacterial growth to the clinicians.
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Affiliation(s)
- Damien Jacot
- Institute of Microbiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Shklqim Gizha
- Institute of Microbiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Cedrick Orny
- Becton Dickinson Kiestra, Le Pont-de-Claix, France
| | | | | | | | - Guy Prod'hom
- Institute of Microbiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | | | - Gilbert Greub
- Institute of Microbiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Infectious Diseases Service, Department of Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Antony Croxatto
- Institute of Microbiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- ADMED, Department of Microbiology, La Chaux-de-Fonds, Switzerland
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Singla N, Kundu R, Dey P. Artificial Intelligence: Exploring utility in detection and typing of fungus with futuristic application in fungal cytology. Cytopathology 2024; 35:226-234. [PMID: 37970960 DOI: 10.1111/cyt.13336] [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: 08/20/2023] [Revised: 10/19/2023] [Accepted: 11/07/2023] [Indexed: 11/19/2023]
Abstract
Artificial Intelligence (AI) is an emerging, transforming and revolutionary technology that has captured attention worldwide. It is translating research into precision oncology treatments. AI can analyse large or big data sets requiring high-speed specialized computing solutions. The data are big in terms of volume and multimodal with the amalgamation of images, text and structure. Machine learning has identified antifungal drug targets, and taxonomic and phylogenetic classification of fungi based on sequence analysis is now available. Real-time identification tools and user-friendly mobile applications for identifying fungi have been discovered. Akin to histopathology, AI can be applied to fungal cytology. AI has been fruitful in cytopathology of the thyroid gland, breast, urine and uterine cervical lesions. AI has a huge scope in fungal cytology and would certainly bear fruit with its accuracy, reproducibility and capacity for handling big data. The purpose of this systematic review was to highlight the AI's utility in detecting fungus and its typing with a special focus on future application in fungal cytology. We also touch upon the basics of AI in brief.
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Affiliation(s)
- Nidhi Singla
- Department of Microbiology, Government Medical College and Hospital, Chandigarh, India
| | - Reetu Kundu
- Department of Cytology and Gynaecological Pathology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Pranab Dey
- Department of Cytology and Gynaecological Pathology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
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Vidal-García M, Urrutikoetxea-Gutiérrez M, Forero Niampira JC, Basaras M, Cisterna R, Díaz de Tuesta Del Arco JL. Ultrafast detection of β-lactamase resistance in Klebsiella pneumoniae from blood culture by nanopore sequencing. Future Microbiol 2023; 18:1309-1317. [PMID: 37850345 DOI: 10.2217/fmb-2023-0057] [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: 03/10/2023] [Accepted: 07/25/2023] [Indexed: 10/19/2023] Open
Abstract
Aim: This study aimed to assess the ultra-fast method using MinION™ sequencing for rapid identification of β-lactamase-producing Klebsiella pneumoniae clinical isolates from positive blood cultures. Methods: Spiked-blood positive blood cultures were extracted using the ultra-fast method and automated DNA extraction for MinION sequencing. Raw reads were analyzed for β-lactamase resistance genes. Multilocus sequence typing and β-lactamase variant characterization were performed after assembly. Results: The ultra-fast method identified clinically relevant β-lactamase resistance genes in less than 1 h. Multilocus sequence typing and β-lactamase variant characterization required 3-6 h. Sequencing quality showed no direct correlation with pore number or DNA concentration. Conclusion: Nanopore sequencing, specifically the ultra-fast method, is promising for the rapid diagnosis of bloodstream infections, facilitating timely identification of multidrug-resistant bacteria in clinical samples.
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Affiliation(s)
- Matxalen Vidal-García
- Clinical Microbiology Department, Basurto University Hospital, 480132
- Clinical Microbiology & Infection Control, ISS Biocruces Bizkaia, 489033
| | - Mikel Urrutikoetxea-Gutiérrez
- Clinical Microbiology Department, Basurto University Hospital, 480132
- Clinical Microbiology & Infection Control, ISS Biocruces Bizkaia, 489033
| | - Juan C Forero Niampira
- Inmunology, Microbiology & Parasitology Department, University of the Basque Country, 48940
| | - Miren Basaras
- Inmunology, Microbiology & Parasitology Department, University of the Basque Country, 48940
| | - Ramón Cisterna
- Inmunology, Microbiology & Parasitology Department, University of the Basque Country, 48940
| | - José L Díaz de Tuesta Del Arco
- Clinical Microbiology Department, Basurto University Hospital, 480132
- Clinical Microbiology & Infection Control, ISS Biocruces Bizkaia, 489033
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Giacobbe DR, Zhang Y, de la Fuente J. Explainable artificial intelligence and machine learning: novel approaches to face infectious diseases challenges. Ann Med 2023; 55:2286336. [PMID: 38010090 PMCID: PMC10836268 DOI: 10.1080/07853890.2023.2286336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 11/16/2023] [Indexed: 11/29/2023] Open
Abstract
Artificial intelligence (AI) and machine learning (ML) are revolutionizing human activities in various fields, with medicine and infectious diseases being not exempt from their rapid and exponential growth. Furthermore, the field of explainable AI and ML has gained particular relevance and is attracting increasing interest. Infectious diseases have already started to benefit from explainable AI/ML models. For example, they have been employed or proposed to better understand complex models aimed at improving the diagnosis and management of coronavirus disease 2019, in the field of antimicrobial resistance prediction and in quantum vaccine algorithms. Although some issues concerning the dichotomy between explainability and interpretability still require careful attention, an in-depth understanding of how complex AI/ML models arrive at their predictions or recommendations is becoming increasingly essential to properly face the growing challenges of infectious diseases in the present century.
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Affiliation(s)
- Daniele Roberto Giacobbe
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
- Clinica Malattie Infettive, IRCCS Ospedale Policlinico San Martino, Italy
| | - Yudong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, UK
- School of Computer Science and Engineering, Southeast University, Nanjing, China
| | - José de la Fuente
- SaBio (Health and Biotechnology), Instituto de Investigación en Recursos Cinegéticos IREC-CSIC-UCLM-JCCM, Ciudad Real, Spain
- Department of Veterinary Pathobiology, Center for Veterinary Health Sciences, Oklahoma State University, Stillwater, OK, USA
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Datar R, Orenga S, Pogorelcnik R, Rochas O, Simner PJ, van Belkum A. Recent Advances in Rapid Antimicrobial Susceptibility Testing. Clin Chem 2021; 68:91-98. [DOI: 10.1093/clinchem/hvab207] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 09/17/2021] [Indexed: 12/30/2022]
Abstract
Abstract
Background
Antimicrobial susceptibility testing (AST) is classically performed using growth-based techniques that essentially require viable bacterial matter to become visible to the naked eye or a sophisticated densitometer.
Content
Technologies based on the measurement of bacterial density in suspension have evolved marginally in accuracy and rapidity over the 20th century, but assays expanded for new combinations of bacteria and antimicrobials have been automated, and made amenable to high-throughput turn-around. Over the past 25 years, elevated AST rapidity has been provided by nucleic acid-mediated amplification technologies, proteomic and other “omic” methodologies, and the use of next-generation sequencing. In rare cases, AST at the level of single-cell visualization was developed. This has not yet led to major changes in routine high-throughput clinical microbiological detection of antimicrobial resistance.
Summary
We here present a review of the new generation of methods and describe what is still urgently needed for their implementation in day-to-day management of the treatment of infectious diseases.
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Affiliation(s)
- Rucha Datar
- bioMérieux, Microbiology Research, La Balme Les Grottes, France
| | - Sylvain Orenga
- bioMérieux, Microbiology Research, La Balme Les Grottes, France
| | | | - Olivier Rochas
- bioMérieux, Corporate Business Development, Marcy l'Etoile, France
| | - Patricia J Simner
- Department of Pathology, Bacteriology, Division of Medical Microbiology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Alex van Belkum
- bioMérieux, Open Innovation and Partnerships, La Balme Les Grottes, France
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Zhang W, Wu S, Deng J, Liao Q, Liu Y, Xiong L, Shu L, Yuan Y, Xiao Y, Ma Y, Kang M, Li D, Xie Y. Total Laboratory Automation and Three Shifts Reduce Turnaround Time of Cerebrospinal Fluid Culture Results in the Chinese Clinical Microbiology Laboratory. Front Cell Infect Microbiol 2021; 11:765504. [PMID: 34926317 PMCID: PMC8675566 DOI: 10.3389/fcimb.2021.765504] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 11/15/2021] [Indexed: 02/05/2023] Open
Abstract
Background Total laboratory automation (TLA) has the potential to reduce specimen processing time, optimize workflow, and decrease turnaround time (TAT). The purpose of this research is to investigate whether the TAT of our laboratory has changed since the adoption of TLA, as well as to optimize laboratory workflow, improve laboratory testing efficiency, and provide better services of clinical diagnosis and treatment. Materials and Methods Laboratory data was extracted from our laboratory information system in two 6-month periods: pre-TLA (July to December 2019) and post-TLA (July to December 2020), respectively. Results The median TAT for positive cultures decreased significantly from pre-TLA to post-TLA (65.93 vs 63.53, P<0.001). For different types of cultures, The TAT of CSF changed the most (86.76 vs 64.30, P=0.007), followed by sputum (64.38 vs 61.41, P<0.001), urine (52.10 vs 49,57, P<0.001), blood (68.49 vs 66.60, P<0.001). For Ascites and Pleural fluid, there was no significant difference (P>0.05). Further analysis found that the incidence of broth growth only for pre-TLA was 12.4% (14/133), while for post-TLA, it was 3.4% (4/119). The difference was statistically significant (P=0.01). The common isolates from CSF samples were Cryptococcus neoformans, coagulase-negative Staphylococcus, Acinetobacter baumannii, and Klebsiella pneumonia. Conclusion Using TLA and setting up three shifts shortened the TAT of our clinical microbiology laboratory, especially for CSF samples.
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Affiliation(s)
- Weili Zhang
- Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Siying Wu
- Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Jin Deng
- Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Quanfeng Liao
- Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Ya Liu
- Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Li Xiong
- Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Ling Shu
- Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Yu Yuan
- Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Yuling Xiao
- Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Ying Ma
- Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Mei Kang
- Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Dongdong Li
- Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Yi Xie
- Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu, China
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Tuft S, Somerville TF, Li JPO, Neal T, De S, Horsburgh MJ, Fothergill JL, Foulkes D, Kaye S. Bacterial keratitis: identifying the areas of clinical uncertainty. Prog Retin Eye Res 2021; 89:101031. [PMID: 34915112 DOI: 10.1016/j.preteyeres.2021.101031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 11/24/2021] [Accepted: 11/29/2021] [Indexed: 12/12/2022]
Abstract
Bacterial keratitis is a common corneal infection that is treated with topical antimicrobials. By the time of presentation there may already be severe visual loss from corneal ulceration and opacity, which may persist despite treatment. There are significant differences in the associated risk factors and the bacterial isolates between high income and low- or middle-income countries, so that general management guidelines may not be appropriate. Although the diagnosis of bacterial keratitis may seem intuitive there are multiple uncertainties about the criteria that are used, which impacts the interpretation of investigations and recruitment to clinical studies. Importantly, the concept that bacterial keratitis can only be confirmed by culture ignores the approximately 50% of cases clinically consistent with bacterial keratitis in which investigations are negative. The aetiology of these culture-negative cases is unknown. Currently, the estimation of bacterial susceptibility to antimicrobials is based on data from systemic administration and achievable serum or tissue concentrations, rather than relevant corneal concentrations and biological activity in the cornea. The provision to the clinician of minimum inhibitory concentrations of the antimicrobials for the isolated bacteria would be an important step forward. An increase in the prevalence of antimicrobial resistance is a concern, but the effect this has on disease outcomes is yet unclear. Virulence factors are not routinely assessed although they may affect the pathogenicity of bacteria within species and affect outcomes. New technologies have been developed to detect and kill bacteria, and their application to bacterial keratitis is discussed. In this review we present the multiple areas of clinical uncertainty that hamper research and the clinical management of bacterial keratitis, and we address some of the assumptions and dogma that have become established in the literature.
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Affiliation(s)
- Stephen Tuft
- Moorfields Eye Hospital NHS Foundation Trust, 162 City Road, London, EC1V 2PD, UK.
| | - Tobi F Somerville
- Department of Eye and Vision Sciences, University of Liverpool, 6 West Derby Street, Liverpool, L7 8TX, UK.
| | - Ji-Peng Olivia Li
- Moorfields Eye Hospital NHS Foundation Trust, 162 City Road, London, EC1V 2PD, UK.
| | - Timothy Neal
- Department of Clinical Microbiology, Liverpool Clinical Laboratories, Liverpool University Hospital NHS Foundation Trust, Prescot Street, Liverpool, L7 8XP, UK.
| | - Surjo De
- Department of Clinical Microbiology, University College London Hospitals NHS Foundation Trust, 250 Euston Road, London, NW1 2PG, UK.
| | - Malcolm J Horsburgh
- Department of Infection and Microbiomes, University of Liverpool, Crown Street, Liverpool, L69 7BX, UK.
| | - Joanne L Fothergill
- Department of Eye and Vision Sciences, University of Liverpool, 6 West Derby Street, Liverpool, L7 8TX, UK.
| | - Daniel Foulkes
- Department of Eye and Vision Sciences, University of Liverpool, 6 West Derby Street, Liverpool, L7 8TX, UK.
| | - Stephen Kaye
- Department of Eye and Vision Sciences, University of Liverpool, 6 West Derby Street, Liverpool, L7 8TX, UK.
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