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Li Y, Frederick RA, George D, Cogan SF, Pancrazio JJ, Bleris L, Hernandez-Reynoso AG. NeurostimML: a machine learning model for predicting neurostimulation-induced tissue damage. J Neural Eng 2024; 21:036054. [PMID: 38885676 DOI: 10.1088/1741-2552/ad593e] [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: 10/18/2023] [Accepted: 06/17/2024] [Indexed: 06/20/2024]
Abstract
Objective. The safe delivery of electrical current to neural tissue depends on many factors, yet previous methods for predicting tissue damage rely on only a few stimulation parameters. Here, we report the development of a machine learning approach that could lead to a more reliable method for predicting electrical stimulation-induced tissue damage by incorporating additional stimulation parameters.Approach. A literature search was conducted to build an initial database of tissue response information after electrical stimulation, categorized as either damaging or non-damaging. Subsequently, we used ordinal encoding and random forest for feature selection, and investigated four machine learning models for classification: Logistic Regression, K-nearest Neighbor, Random Forest, and Multilayer Perceptron. Finally, we compared the results of these models against the accuracy of the Shannon equation.Main Results. We compiled a database with 387 unique stimulation parameter combinations collected from 58 independent studies conducted over a period of 47 years, with 195 (51%) categorized as non-damaging and 190 (49%) categorized as damaging. The features selected for building our model with a Random Forest algorithm were: waveform shape, geometric surface area, pulse width, frequency, pulse amplitude, charge per phase, charge density, current density, duty cycle, daily stimulation duration, daily number of pulses delivered, and daily accumulated charge. The Shannon equation yielded an accuracy of 63.9% using akvalue of 1.79. In contrast, the Random Forest algorithm was able to robustly predict whether a set of stimulation parameters was classified as damaging or non-damaging with an accuracy of 88.3%.Significance. This novel Random Forest model can facilitate more informed decision making in the selection of neuromodulation parameters for both research studies and clinical practice. This study represents the first approach to use machine learning in the prediction of stimulation-induced neural tissue damage, and lays the groundwork for neurostimulation driven by machine learning models.
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Affiliation(s)
- Yi Li
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States of America
- Center for Systems Biology, The University of Texas at Dallas, Richardson, TX, United States of America
| | - Rebecca A Frederick
- Phil and Penny Knight Campus for Accelerating Scientific Impact, University of Oregon, Eugene, OR, United States of America
| | - Daniel George
- Department of Computer Science, The University of Texas at Dallas, Richardson, TX, United States of America
| | - Stuart F Cogan
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States of America
| | - Joseph J Pancrazio
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States of America
| | - Leonidas Bleris
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States of America
- Center for Systems Biology, The University of Texas at Dallas, Richardson, TX, United States of America
- Department of Biological Sciences, The University of Texas at Dallas, Richardson, TX, United States of America
| | - Ana G Hernandez-Reynoso
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States of America
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Alsulimani A, Akhter N, Jameela F, Ashgar RI, Jawed A, Hassani MA, Dar SA. The Impact of Artificial Intelligence on Microbial Diagnosis. Microorganisms 2024; 12:1051. [PMID: 38930432 PMCID: PMC11205376 DOI: 10.3390/microorganisms12061051] [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: 05/08/2024] [Revised: 05/19/2024] [Accepted: 05/21/2024] [Indexed: 06/28/2024] Open
Abstract
Traditional microbial diagnostic methods face many obstacles such as sample handling, culture difficulties, misidentification, and delays in determining susceptibility. The advent of artificial intelligence (AI) has markedly transformed microbial diagnostics with rapid and precise analyses. Nonetheless, ethical considerations accompany AI adoption, necessitating measures to uphold patient privacy, mitigate biases, and ensure data integrity. This review examines conventional diagnostic hurdles, stressing the significance of standardized procedures in sample processing. It underscores AI's significant impact, particularly through machine learning (ML), in microbial diagnostics. Recent progressions in AI, particularly ML methodologies, are explored, showcasing their influence on microbial categorization, comprehension of microorganism interactions, and augmentation of microscopy capabilities. This review furnishes a comprehensive evaluation of AI's utility in microbial diagnostics, addressing both advantages and challenges. A few case studies including SARS-CoV-2, malaria, and mycobacteria serve to illustrate AI's potential for swift and precise diagnosis. Utilization of convolutional neural networks (CNNs) in digital pathology, automated bacterial classification, and colony counting further underscores AI's versatility. Additionally, AI improves antimicrobial susceptibility assessment and contributes to disease surveillance, outbreak forecasting, and real-time monitoring. Despite a few limitations, integration of AI in diagnostic microbiology presents robust solutions, user-friendly algorithms, and comprehensive training, promising paradigm-shifting advancements in healthcare.
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Affiliation(s)
- Ahmad Alsulimani
- Medical Laboratory Technology Department, College of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia; (A.A.); (M.A.H.)
| | - Naseem Akhter
- Department of Biology, Arizona State University, Lake Havasu City, AZ 86403, USA;
| | - Fatima Jameela
- Modern American Dental Clinic, West Warren Avenue, Dearborn, MI 48126, USA;
| | - Rnda I. Ashgar
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
| | - Arshad Jawed
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
| | - Mohammed Ahmed Hassani
- Medical Laboratory Technology Department, College of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia; (A.A.); (M.A.H.)
| | - Sajad Ahmad Dar
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
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Desruisseaux C, Broderick C, Lavergne V, Sy K, Garcia DJ, Barot G, Locher K, Porter C, Caza M, Charles MK. Retrospective validation of MetaSystems' deep-learning-based digital microscopy platform with assistance compared to manual fluorescence microscopy for detection of mycobacteria. J Clin Microbiol 2024; 62:e0106923. [PMID: 38299829 PMCID: PMC10935628 DOI: 10.1128/jcm.01069-23] [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: 08/20/2023] [Accepted: 11/25/2023] [Indexed: 02/02/2024] Open
Abstract
This study aimed to validate Metasystems' automated acid-fast bacilli (AFB) smear microscopy scanning and deep-learning-based image analysis module (Neon Metafer) with assistance on respiratory and pleural samples, compared to conventional manual fluorescence microscopy (MM). Analytical parameters were assessed first, followed by a retrospective validation study. In all, 320 archived auramine-O-stained slides selected non-consecutively [85 originally reported as AFB-smear-positive, 235 AFB-smear-negative slides; with an overall mycobacterial culture positivity rate of 24.1% (77/320)] underwent whole-slide imaging and were analyzed by the Metafer Neon AFB Module (version 4.3.130) using a predetermined probability threshold (PT) for AFB detection of 96%. Digital slides were then examined by a trained reviewer blinded to previous AFB smear and culture results, for the final interpretation of assisted digital microscopy (a-DM). Paired results from both microscopic methods were compared to mycobacterial culture. A scanning failure rate of 10.6% (34/320) was observed, leaving 286 slides for analysis. After discrepant analysis, concordance, positive and negative agreements were 95.5% (95%CI, 92.4%-97.6%), 96.2% (95%CI, 89.2%-99.2%), and 95.2% (95%CI, 91.3%-97.7%), respectively. Using mycobacterial culture as reference standard, a-DM and MM had comparable sensitivities: 90.7% (95%CI, 81.7%-96.2%) versus 92.0% (95%CI, 83.4%-97.0%) (P-value = 1.00); while their specificities differed 91.9% (95%CI, 87.4%-95.2%) versus 95.7% (95%CI, 92.1%-98.0%), respectively (P-value = 0.03). Using a PT of 96%, MetaSystems' platform shows acceptable performance. With a national laboratory staff shortage and a local low mycobacterial infection rate, this instrument when combined with culture, can reliably triage-negative AFB-smear respiratory slides and identify positive slides requiring manual confirmation and semi-quantification. IMPORTANCE This manuscript presents a full validation of MetaSystems' automated acid-fast bacilli (AFB) smear microscopy scanning and deep-learning-based image analysis module using a probability threshold of 96% including accuracy, precision studies, and evaluation of limit of AFB detection on respiratory samples when the technology is used with assistance. This study is complementary to the conversation started by Tomasello et al. on the use of image analysis artificial intelligence software in routine mycobacterial diagnostic activities within the context of high-throughput laboratories with low incidence of tuberculosis.
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Affiliation(s)
- Claudine Desruisseaux
- Division of Medical Microbiology and Infection Control, Department of Pathology and Laboratory Medicine, Vancouver General Hospital, Vancouver Coastal Health, Vancouver, British Columbia, Canada
- Faculty of Medicine, Department of Pathology and Laboratory Medicine, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Conor Broderick
- Faculty of Medicine, Department of Pathology and Laboratory Medicine, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Valéry Lavergne
- Division of Medical Microbiology and Infection Control, Department of Pathology and Laboratory Medicine, Vancouver General Hospital, Vancouver Coastal Health, Vancouver, British Columbia, Canada
- Faculty of Medicine, Department of Pathology and Laboratory Medicine, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Kim Sy
- Division of Medical Microbiology and Infection Control, Department of Pathology and Laboratory Medicine, Vancouver General Hospital, Vancouver Coastal Health, Vancouver, British Columbia, Canada
| | - Duang-Jai Garcia
- Division of Medical Microbiology and Infection Control, Department of Pathology and Laboratory Medicine, Vancouver General Hospital, Vancouver Coastal Health, Vancouver, British Columbia, Canada
| | - Gaurav Barot
- Division of Medical Microbiology and Infection Control, Department of Pathology and Laboratory Medicine, Vancouver General Hospital, Vancouver Coastal Health, Vancouver, British Columbia, Canada
| | - Kerstin Locher
- Division of Medical Microbiology and Infection Control, Department of Pathology and Laboratory Medicine, Vancouver General Hospital, Vancouver Coastal Health, Vancouver, British Columbia, Canada
- Faculty of Medicine, Department of Pathology and Laboratory Medicine, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Charlene Porter
- Division of Medical Microbiology and Infection Control, Department of Pathology and Laboratory Medicine, Vancouver General Hospital, Vancouver Coastal Health, Vancouver, British Columbia, Canada
| | - Mélissa Caza
- Faculty of Medicine, Department of Pathology and Laboratory Medicine, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Marthe K. Charles
- Division of Medical Microbiology and Infection Control, Department of Pathology and Laboratory Medicine, Vancouver General Hospital, Vancouver Coastal Health, Vancouver, British Columbia, Canada
- Faculty of Medicine, Department of Pathology and Laboratory Medicine, The University of British Columbia, Vancouver, British Columbia, Canada
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Li Y, Frederick RA, George D, Cogan SF, Pancrazio JJ, Bleris L, Hernandez-Reynoso AG. NeurostimML: A machine learning model for predicting neurostimulation-induced tissue damage. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.18.562980. [PMID: 37905012 PMCID: PMC10614958 DOI: 10.1101/2023.10.18.562980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Objective The safe delivery of electrical current to neural tissue depends on many factors, yet previous methods for predicting tissue damage rely on only a few stimulation parameters. Here, we report the development of a machine learning approach that could lead to a more reliable method for predicting electrical stimulation-induced tissue damage by incorporating additional stimulation parameters. Approach A literature search was conducted to build an initial database of tissue response information after electrical stimulation, categorized as either damaging or non-damaging. Subsequently, we used ordinal encoding and random forest for feature selection, and investigated four machine learning models for classification: Logistic Regression, K-nearest Neighbor, Random Forest, and Multilayer Perceptron. Finally, we compared the results of these models against the accuracy of the Shannon equation. Main Results We compiled a database with 387 unique stimulation parameter combinations collected from 58 independent studies conducted over a period of 47 years, with 195 (51%) categorized as non-damaging and 190 (49%) categorized as damaging. The features selected for building our model with a Random Forest algorithm were: waveform shape, geometric surface area, pulse width, frequency, pulse amplitude, charge per phase, charge density, current density, duty cycle, daily stimulation duration, daily number of pulses delivered, and daily accumulated charge. The Shannon equation yielded an accuracy of 63.9% using a k value of 1.79. In contrast, the Random Forest algorithm was able to robustly predict whether a set of stimulation parameters was classified as damaging or non-damaging with an accuracy of 88.3%. Significance This novel Random Forest model can facilitate more informed decision making in the selection of neuromodulation parameters for both research studies and clinical practice. This study represents the first approach to use machine learning in the prediction of stimulation-induced neural tissue damage, and lays the groundwork for neurostimulation driven by machine learning models.
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Affiliation(s)
- Yi Li
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, USA
- Center for Systems Biology, The University of Texas at Dallas, Richardson, TX, USA
| | - Rebecca A. Frederick
- Phil and Penny Knight Campus for Accelerating Scientific Impact, University of Oregon, Eugene, OR, USA
| | - Daniel George
- Department of Computer Science, The University of Texas at Dallas, Richardson, TX, USA
| | - Stuart F. Cogan
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, USA
| | - Joseph J. Pancrazio
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, USA
| | - Leonidas Bleris
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, USA
- Center for Systems Biology, The University of Texas at Dallas, Richardson, TX, USA
- Department of Biological Sciences, The University of Texas at Dallas, Richardson, TX, USA
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