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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] [Grants] [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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He D, Wang R, Xu Z, Wang J, Song P, Wang H, Su J. The use of artificial intelligence in the treatment of rare diseases: A scoping review. Intractable Rare Dis Res 2024; 13:12-22. [PMID: 38404730 PMCID: PMC10883845 DOI: 10.5582/irdr.2023.01111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/28/2023] [Accepted: 12/22/2023] [Indexed: 02/27/2024] Open
Abstract
With the increasing application of artificial intelligence (AI) in medicine and healthcare, AI technologies have the potential to improve the diagnosis, treatment, and prognosis of rare diseases. Presently, existing research predominantly focuses on the areas of diagnosis and prognosis, with relatively fewer studies dedicated to the domain of treatment. The purpose of this review is to systematically analyze the existing literature on the application of AI in the treatment of rare diseases. We searched three databases for related studies, and established criteria for the selection of retrieved articles. From the 407 unique articles identified across the three databases, 13 articles from 8 countries were selected, which investigated 10 different rare diseases. The most frequently studied rare disease group was rare neurologic diseases (n = 5/13, 38.46%). Among the four identified therapeutic domains, 7 articles (53.85%) focused on drug research, with 5 specifically focused on drug discovery (drug repurposing, the discovery of drug targets and small-molecule inhibitors), 1 on pre-clinical studies (drug interactions), and 1 on clinical studies (information strength assessment of clinical parameters). Across the selected 13 articles, we identified total 32 different algorithms, with random forest (RF) being the most commonly used (n = 4/32, 12.50%). The predominant purpose of AI in the treatment of rare diseases in these articles was to enhance the performance of analytical tasks (53.33%). The most common data source was database data (35.29%), with 5 of these studies being in the field of drug research, utilizing classic databases such as RCSB, PDB and NCBI. Additionally, 47.37% of the articles highlighted the existing challenge of data scarcity or small sample sizes.
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Affiliation(s)
- Da He
- Shanghai Health Development Research Center (Shanghai Medical Information Center), Shanghai, China
| | - Ru Wang
- Shanghai Health Development Research Center (Shanghai Medical Information Center), Shanghai, China
| | - Zhilin Xu
- EYE & ENT Hospital of Fudan University, Shanghai, China
| | - Jiangna Wang
- Jiangxi University of Chinese Medicine, Shanghai, China
| | - Peipei Song
- Center for Clinical Sciences, National Center for Global Health and Medicine, Tokyo, Japan
| | - Haiyin Wang
- Shanghai Health Development Research Center (Shanghai Medical Information Center), Shanghai, China
| | - Jinying Su
- Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Iskrov G, Raycheva R, Kostadinov K, Gillner S, Blankart CR, Gross ES, Gumus G, Mitova E, Stefanov S, Stefanov G, Stefanov R. Are the European reference networks for rare diseases ready to embrace machine learning? A mixed-methods study. Orphanet J Rare Dis 2024; 19:25. [PMID: 38273306 PMCID: PMC10809751 DOI: 10.1186/s13023-024-03047-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 01/19/2024] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND The delay in diagnosis for rare disease (RD) patients is often longer than for patients with common diseases. Machine learning (ML) technologies have the potential to speed up and increase the precision of diagnosis in this population group. We aim to explore the expectations and experiences of the members of the European Reference Networks (ERNs) for RDs with those technologies and their potential for application. METHODS We used a mixed-methods approach with an online survey followed by a focus group discussion. Our study targeted primarily medical professionals but also other individuals affiliated with any of the 24 ERNs. RESULTS The online survey yielded 423 responses from ERN members. Participants reported a limited degree of knowledge of and experience with ML technologies. They considered improved diagnostic accuracy the most important potential benefit, closely followed by the synthesis of clinical information, and indicated the lack of training in these new technologies, which hinders adoption and implementation in routine care. Most respondents supported the option that ML should be an optional but recommended part of the diagnostic process for RDs. Most ERN members saw the use of ML limited to specialised units only in the next 5 years, where those technologies should be funded by public sources. Focus group discussions concluded that the potential of ML technologies is substantial and confirmed that the technologies will have an important impact on healthcare and RDs in particular. As ML technologies are not the core competency of health care professionals, participants deemed a close collaboration with developers necessary to ensure that results are valid and reliable. However, based on our results, we call for more research to understand other stakeholders' opinions and expectations, including the views of patient organisations. CONCLUSIONS We found enthusiasm to implement and apply ML technologies, especially diagnostic tools in the field of RDs, despite the perceived lack of experience. Early dialogue and collaboration between health care professionals, developers, industry, policymakers, and patient associations seem to be crucial to building trust, improving performance, and ultimately increasing the willingness to accept diagnostics based on ML technologies.
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Affiliation(s)
- Georgi Iskrov
- Institute for Rare Diseases, 22 Maestro G. Atanasov St., 4017, Plovdiv, Bulgaria.
- Department of Social Medicine and Public Health, Faculty of Public Health, Medical University of Plovdiv, 15A Vasil Aprilov Blvd., 4002, Plovdiv, Bulgaria.
| | - Ralitsa Raycheva
- Institute for Rare Diseases, 22 Maestro G. Atanasov St., 4017, Plovdiv, Bulgaria
- Department of Social Medicine and Public Health, Faculty of Public Health, Medical University of Plovdiv, 15A Vasil Aprilov Blvd., 4002, Plovdiv, Bulgaria
| | - Kostadin Kostadinov
- Institute for Rare Diseases, 22 Maestro G. Atanasov St., 4017, Plovdiv, Bulgaria
- Department of Social Medicine and Public Health, Faculty of Public Health, Medical University of Plovdiv, 15A Vasil Aprilov Blvd., 4002, Plovdiv, Bulgaria
| | - Sandra Gillner
- KPM Center for Public Management, University of Bern, Freiburgstr. 3, 3010, Bern, Switzerland
- Swiss Institute for Translational and Entrepreneurial Medicine (Sitem-Insel), Freiburgstr. 3, 3010, Bern, Switzerland
| | - Carl Rudolf Blankart
- KPM Center for Public Management, University of Bern, Freiburgstr. 3, 3010, Bern, Switzerland
- Swiss Institute for Translational and Entrepreneurial Medicine (Sitem-Insel), Freiburgstr. 3, 3010, Bern, Switzerland
| | - Edith Sky Gross
- EURORDIS - Rare Diseases Europe, 96 Rue Didot, 75014, Paris, France
| | - Gulcin Gumus
- EURORDIS - Rare Diseases Europe, 96 Rue Didot, 75014, Paris, France
| | - Elena Mitova
- Institute for Rare Diseases, 22 Maestro G. Atanasov St., 4017, Plovdiv, Bulgaria
| | - Stefan Stefanov
- Institute for Rare Diseases, 22 Maestro G. Atanasov St., 4017, Plovdiv, Bulgaria
- Department of Epidemiology and Disaster Medicine, Faculty of Public Health, Medical University, 15A Vasil Aprilov Blvd., 4002, Plovdiv, Bulgaria
| | - Georgi Stefanov
- Institute for Rare Diseases, 22 Maestro G. Atanasov St., 4017, Plovdiv, Bulgaria
| | - Rumen Stefanov
- Institute for Rare Diseases, 22 Maestro G. Atanasov St., 4017, Plovdiv, Bulgaria
- Department of Social Medicine and Public Health, Faculty of Public Health, Medical University of Plovdiv, 15A Vasil Aprilov Blvd., 4002, Plovdiv, Bulgaria
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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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Lu W, Xu J, Chen Y, Huang J, Shen Q, Sun F, Zhang Y, Ji D, Xue B, Li J. Identication and validation of cell senescence biomarkers in idiopathic pulmonary hypertension via integrated transcriptome analyses and machine learning. Exp Gerontol 2023; 182:112303. [PMID: 37776984 DOI: 10.1016/j.exger.2023.112303] [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: 08/03/2023] [Revised: 09/26/2023] [Accepted: 09/27/2023] [Indexed: 10/02/2023]
Abstract
BACKGROUND Idiopathic pulmonary hypertension (IPAH) is a rare and severe disease that affects the pulmonary vasculature. As the diagnosis of IPAH requires invasive right heart catheterization surgery, early detection of this condition is notoriously challenging. Therefore, it is of utmost importance to investigate biomarkers present in peripheral blood that could aid physicians in the early identification and management of IPAH. METHOD We speculate that cellular senescence may be involved in the occurrence and development of IPAH through various pathways. In this study, we utilized integrated transcriptome analyses and machine learning-based approach to develop a diagnostic model for IPAH cell senescence. To select genetic features, we employed two machine learning algorithms: the Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF). Additionally, we validated our findings through both external data sets and qRT-PCR experiments. RESULTS The resulting diagnostic nomogram was able to identify five important biomarkers that can aid in the diagnosis of IPAH, including TNFRSF1B, CCL16, GCLM, IL15, and SOD1. These genes are primarily associated with the immune system, as well as with cell senescence and apoptosis. CONCLUSION Our study demonstrates the utility of machine learning algorithms in making accurate diagnoses of IPAH, providing clinicians with a more directed approach to the diagnosis and treatment of this disease.
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Affiliation(s)
- Wenzhang Lu
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong 226001, China; Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Nantong University, Nantong 226001, China
| | - Jiayi Xu
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong 226001, China; Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Nantong University, Nantong 226001, China
| | - Yanrong Chen
- Department of Operating Room, Affiliated Hospital of Nantong University, Nantong 226001, China
| | - Jinbo Huang
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Nantong University, Nantong 226001, China
| | - Qin Shen
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Nantong University, Nantong 226001, China
| | - Fei Sun
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Nantong University, Nantong 226001, China
| | - Yan Zhang
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Nantong University, Nantong 226001, China
| | - Daojun Ji
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong 226001, China; Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Nantong University, Nantong 226001, China
| | - Bijuan Xue
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong 226001, China; Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Nantong University, Nantong 226001, China
| | - Jun Li
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong 226001, China; Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Nantong University, Nantong 226001, China.
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