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Matboli M, Abdelbaky I, Khaled A, Khaled R, Hamady S, Farid LM, Abouelkhair MB, El-Attar NE, Farag Fathallah M, Abd El Hamid MS, Elmakromy GM, Ali M. Machine learning based identification potential feature genes for prediction of drug efficacy in nonalcoholic steatohepatitis animal model. Lipids Health Dis 2024; 23:266. [PMID: 39182075 PMCID: PMC11344433 DOI: 10.1186/s12944-024-02231-9] [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: 05/05/2024] [Accepted: 07/30/2024] [Indexed: 08/27/2024] Open
Abstract
BACKGROUND Nonalcoholic Steatohepatitis (NASH) results from complex liver conditions involving metabolic, inflammatory, and fibrogenic processes. Despite its burden, there has been a lack of any approved food-and-drug administration therapy up till now. PURPOSE Utilizing machine learning (ML) algorithms, the study aims to identify reliable potential genes to accurately predict the treatment response in the NASH animal model using biochemical and molecular markers retrieved using bioinformatics techniques. METHODS The NASH-induced rat models were administered various microbiome-targeted therapies and herbal drugs for 12 weeks, these drugs resulted in reducing hepatic lipid accumulation, liver inflammation, and histopathological changes. The ML model was trained and tested based on the Histopathological NASH score (HPS); while (0-4) HPS considered Improved NASH and (5-8) considered non-improved, confirmed through rats' liver histopathological examination, incorporates 34 features comprising 20 molecular markers (mRNAs-microRNAs-Long non-coding-RNAs) and 14 biochemical markers that are highly enriched in NASH pathogenesis. Six different ML models were used in the proposed model for the prediction of NASH improvement, with Gradient Boosting demonstrating the highest accuracy of 98% in predicting NASH drug response. FINDINGS Following a gradual reduction in features, the outcomes demonstrated superior performance when employing the Random Forest classifier, yielding an accuracy of 98.4%. The principal selected molecular features included YAP1, LATS1, NF2, SRD5A3-AS1, FOXA2, TEAD2, miR-650, MMP14, ITGB1, and miR-6881-5P, while the biochemical markers comprised triglycerides (TG), ALT, ALP, total bilirubin (T. Bilirubin), alpha-fetoprotein (AFP), and low-density lipoprotein cholesterol (LDL-C). CONCLUSION This study introduced an ML model incorporating 16 noninvasive features, including molecular and biochemical signatures, which achieved high performance and accuracy in detecting NASH improvement. This model could potentially be used as diagnostic tools and to identify target therapies.
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Affiliation(s)
- Marwa Matboli
- Medical Biochemistry and Molecular Biology Department, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
| | - Ibrahim Abdelbaky
- Artificial Intelligence Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha City, Egypt
| | - Abdelrahman Khaled
- Bioinformatics Group, Center of Informatics Sciences (CIS), School of Information Technology and Computer Sciences, Nile University, Giza, Egypt
| | - Radwa Khaled
- Biotechnology/Biomolecular Chemistry Department, Faculty of Science, Cairo University, Cairo, Egypt
- Basic Sciences Department, Modern University for Technology and Information, Cairo, Egypt
| | | | - Laila M Farid
- Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | | | - Noha E El-Attar
- Information System Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha City, Egypt
- Faculty of Artificial Intelligence, Delta University for Science and Technology, Gamasa, 35712, Egypt
| | - Mohamed Farag Fathallah
- Medical Pathology Department, Faculty of Medicine, Cairo University, Cairo, Egypt
- Medical Physiology Department, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Manal S Abd El Hamid
- Medical Physiology Department, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Gena M Elmakromy
- Endocrinology & Diabetes Mellitus Unit, Department of Internal Medicine, Badr University in Cairo, Badr City, Egypt
| | - Marwa Ali
- Medical Biochemistry and Molecular Biology Department, Faculty of Medicine, Ain Shams University, Cairo, Egypt
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Lu F, Meng Y, Song X, Li X, Liu Z, Gu C, Zheng X, Jing Y, Cai W, Pinyopornpanish K, Mancuso A, Romeiro FG, Méndez-Sánchez N, Qi X. Artificial Intelligence in Liver Diseases: Recent Advances. Adv Ther 2024; 41:967-990. [PMID: 38286960 DOI: 10.1007/s12325-024-02781-5] [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: 09/12/2023] [Accepted: 01/03/2024] [Indexed: 01/31/2024]
Abstract
Liver diseases cause a significant burden on public health worldwide. In spite of great advances during recent years, there are still many challenges in the diagnosis and treatment of liver diseases. During recent years, artificial intelligence (AI) has been widely used for the diagnosis, risk stratification, and prognostic prediction of various diseases based on clinical datasets and medical images. Accumulative studies have shown its performance for diagnosing patients with nonalcoholic fatty liver disease and liver fibrosis and assessing their severity, and for predicting treatment response and recurrence of hepatocellular carcinoma, outcomes of liver transplantation recipients, and risk of drug-induced liver injury. Herein, we aim to comprehensively summarize the current evidence regarding diagnostic, prognostic, and/or therapeutic role of AI in these common liver diseases.
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Affiliation(s)
- Feifei Lu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
| | - Yao Meng
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, Dalian Medical University, Dalian, China
| | - Xiaoting Song
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, Dalian Medical University, Dalian, China
| | - Xiaotong Li
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Zhuang Liu
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Chunru Gu
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Xiaojie Zheng
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Yi Jing
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, China
| | - Wei Cai
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, China
| | - Kanokwan Pinyopornpanish
- Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Andrea Mancuso
- Medicina Interna 1, Azienda di Rilievo Nazionale Ad Alta Specializzazione Civico-Di Cristina-Benfratelli, Palermo, Italy.
| | | | - Nahum Méndez-Sánchez
- Liver Research Unit, Medica Sur Clinic and Foundation, National Autonomous University of Mexico, Mexico City, Mexico.
| | - Xingshun Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China.
- Postgraduate College, Dalian Medical University, Dalian, China.
- Postgraduate College, China Medical University, Shenyang, China.
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3
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Wang RR, Chen JL, Duan SJ, Lu YX, Chen P, Zhou YC, Yao SK. Noninvasive Diagnostic Technique for Nonalcoholic Fatty Liver Disease Based on Features of Tongue Images. Chin J Integr Med 2024; 30:203-212. [PMID: 38051474 DOI: 10.1007/s11655-023-3616-1] [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] [Accepted: 06/07/2023] [Indexed: 12/07/2023]
Abstract
OBJECTIVE To investigate a new noninvasive diagnostic model for nonalcoholic fatty liver disease (NAFLD) based on features of tongue images. METHODS Healthy controls and volunteers confirmed to have NAFLD by liver ultrasound were recruited from China-Japan Friendship Hospital between September 2018 and May 2019, then the anthropometric indexes and sampled tongue images were measured. The tongue images were labeled by features, based on a brief protocol, without knowing any other clinical data, after a series of corrections and data cleaning. The algorithm was trained on images using labels and several anthropometric indexes for inputs, utilizing machine learning technology. Finally, a logistic regression algorithm and a decision tree model were constructed as 2 diagnostic models for NAFLD. RESULTS A total of 720 subjects were enrolled in this study, including 432 patients with NAFLD and 288 healthy volunteers. Of them, 482 were randomly allocated into the training set and 238 into the validation set. The diagnostic model based on logistic regression exhibited excellent performance: in validation set, it achieved an accuracy of 86.98%, sensitivity of 91.43%, and specificity of 80.61%; with an area under the curve (AUC) of 0.93 [95% confidence interval (CI) 0.68-0.98]. The decision tree model achieved an accuracy of 81.09%, sensitivity of 91.43%, and specificity of 66.33%; with an AUC of 0.89 (95% CI 0.66-0.92) in validation set. CONCLUSIONS The features of tongue images were associated with NAFLD. Both the 2 diagnostic models, which would be convenient, noninvasive, lightweight, rapid, and inexpensive technical references for early screening, can accurately distinguish NAFLD and are worth further study.
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Affiliation(s)
- Rong-Rui Wang
- Graduate School of Beijing University of Chinese Medicine, Beijing, 100029, China
- Department of Gastroenterology, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Jia-Liang Chen
- Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China
| | - Shao-Jie Duan
- Graduate School of Beijing University of Chinese Medicine, Beijing, 100029, China
- Department of Gastroenterology, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Ying-Xi Lu
- Nanjing Linkwah Micro-electronics Institute, Beijing, 100191, China
- Institute of Microelectronics, Tsinghua University, Beijing, 100084, China
| | - Ping Chen
- Institute of Microelectronics, Tsinghua University, Beijing, 100084, China
| | - Yuan-Chen Zhou
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, 100029, China
| | - Shu-Kun Yao
- Graduate School of Beijing University of Chinese Medicine, Beijing, 100029, China.
- Department of Gastroenterology, China-Japan Friendship Hospital, Beijing, 100029, China.
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Zamanian H, Shalbaf A, Zali MR, Khalaj AR, Dehghan P, Tabesh M, Hatami B, Alizadehsani R, Tan RS, Acharya UR. Application of artificial intelligence techniques for non-alcoholic fatty liver disease diagnosis: A systematic review (2005-2023). COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107932. [PMID: 38008040 DOI: 10.1016/j.cmpb.2023.107932] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 11/28/2023]
Abstract
BACKGROUND AND OBJECTIVES Non-alcoholic fatty liver disease (NAFLD) is a common liver disease with a rapidly growing incidence worldwide. For prognostication and therapeutic decisions, it is important to distinguish the pathological stages of NAFLD: steatosis, steatohepatitis, and liver fibrosis, which are definitively diagnosed on invasive biopsy. Non-invasive ultrasound (US) imaging, including US elastography technique, and clinical parameters can be used to diagnose and grade NAFLD and its complications. Artificial intelligence (AI) is increasingly being harnessed for developing NAFLD diagnostic models based on clinical, biomarker, or imaging data. In this work, we systemically reviewed the literature for AI-enabled NAFLD diagnostic models based on US (including elastography) and clinical (including serological) data. METHODS We performed a comprehensive search on Google Scholar, Scopus, and PubMed search engines for articles published between January 2005 and June 2023 related to AI models for NAFLD diagnosis based on US and/or clinical parameters using the following search terms: "non-alcoholic fatty liver disease", "non-alcoholic steatohepatitis", "deep learning", "machine learning", "artificial intelligence", "ultrasound imaging", "sonography", "clinical information". RESULTS We reviewed 64 published models that used either US (including elastography) or clinical data input to detect the presence of NAFLD, non-alcoholic steatohepatitis, and/or fibrosis, and in some cases, the severity of steatosis, inflammation, and/or fibrosis as well. The performances of the published models were summarized, and stratified by data input and algorithms used, which could be broadly divided into machine and deep learning approaches. CONCLUSION AI models based on US imaging and clinical data can reliably detect NAFLD and its complications, thereby reducing diagnostic costs and the need for invasive liver biopsy. The models offer advantages of efficiency, accuracy, and accessibility, and serve as virtual assistants for specialists to accelerate disease diagnosis and reduce treatment costs for patients and healthcare systems.
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Affiliation(s)
- H Zamanian
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - A Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - M R Zali
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - A R Khalaj
- Tehran obesity treatment center, Department of Surgery, Faculty of Medicine, Shahed University, Tehran, Iran
| | - P Dehghan
- Department of Radiology, Imaging Department, Taleghani Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - M Tabesh
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research, Tehran University of Medical Sciences, Tehran, Iran
| | - B Hatami
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - R Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC, Australia
| | - Ru-San Tan
- National Heart Centre Singapore, Singapore 169609, Singapore; Duke-NUS Medical School, Singapore
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, Australia; Centre for Health Research, University of Southern Queensland, Australia
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Saleh SAK, Santos HO, Găman MA, Cerqueira HS, Zaher EA, Alromaih WR, Arafat NS, Adi AR, Adly HM, Alyoubi R, Alyahyawi N, Kord-Varkaneh H. Effects of intermittent fasting regimens on glycemic, hepatic, anthropometric, and clinical markers in patients with non-alcoholic fatty liver disease: Systematic review and meta-analysis of randomized controlled trials. Clin Nutr ESPEN 2024; 59:70-80. [PMID: 38220409 DOI: 10.1016/j.clnesp.2023.11.009] [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: 08/04/2023] [Revised: 11/08/2023] [Accepted: 11/08/2023] [Indexed: 01/16/2024]
Abstract
OBJECTIVES Intermittent fasting (IF) regimens have been hypothesized to influence several markers of cardiometabolic and liver function. The objective of our meta-analysis was to investigate the impact of IF regimens on cardiometabolic and liver markers in subjects diagnosed with non-alcoholic fatty liver disease (NAFLD). METHODS We searched several online databases (PubMed/Medline, Web of Science, Scopus and Embase) in order to identify suitable publications for inclusion in the meta-analysis. Results were expressed as weighted mean differences (WMD). RESULTS From 12343 articles identified in different databases, a total of 7 RCT arms were entered into the quantitative synthesis. The manuscripts were published between 2019 and 2023. IF regimens (the 5:2 diet, 16/8 time-restricting feeding, and alternate day fasting) varied from 2 months to 3 months. IF regimens reduced steatosis scores (WMD: -33.22 CAP dB/m, 95 % CI: -50.72 to -15.72), anthropometric characteristics of obesity (WMD: -0.77 kg/m2, 95 % CI: -1.38 to -0.17 for body mass index; WMD: -3.16 kg, 95 % CI: -4.71 to -1.61 for body weight; WMD: -1.90 kg, 95 % CI: -3.51 to -0.29 for waist circumference), as well as ALT (WMD: -9.10 U/L, 95 % CI: -12.45 to -5.75), triglyceride (WMD: -20.83 mg/dl, 95 % CI: -39.01 to -2.66), total cholesterol (WMD: -7.80 mg/dl, 95 % CI: -15.18), HbA1c (WMD: -0.14 %, 95 % CI: -0.20 to -0.08) and HOMA-IR (WMD: -1.21, 95 % CI: -2.08 to -0.34) levels versus controls. Nevertheless, no between-group differences were detected for other biomarkers, e.g., fasting blood glucose, insulin, AST, HDL-C or LDL-C values, and fibrosis scores. CONCLUSION IF regimens can improve some markers of cardiometabolic and liver function in patients with NAFLD. However, the available evidence to support the benefits of IF regimens is limited and derived from a small number of studies, thus further research is needed to clarify the impact of IF on the cardiometabolic health of NAFLD patients.
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Affiliation(s)
- Saleh A K Saleh
- Department of Biochemistry, Faculty of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia; Oncology Diagnostic Unit, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Heitor O Santos
- School of Medicine, Federal University of Uberlandia (UFU), Uberlandia 38408-100, Brazil
| | - Mihnea-Alexandru Găman
- Faculty of Medicine, Carol Davila, University of Medicine and Pharmacy, Bucharest, Romania; Center of Hematology and Bone Marrow Transplantation, Fundeni Clinical Institute, Bucharest, Romania
| | - Henrique S Cerqueira
- School of Medicine, University of São Paulo (USP), Ribeirão Preto 14049-900, Brazil
| | - Eman Abbas Zaher
- Department of Family Medicine, King Abdulaziz Medical City, Riyadh, Saudi Arabia
| | - Wafa Romaih Alromaih
- Department of Family Medicine, King Abdulaziz Medical City, Riyadh, Saudi Arabia
| | - Norah Saud Arafat
- Department of Family Medicine, Security Forces Hospital, Riyadh, Saudi Arabia
| | | | - Heba M Adly
- Department of Community Medicine and Pilgrims Healthcare, Faculty of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Reem Alyoubi
- Department of Pediatrics, King Abdulaziz University Hospital, Jeddah, Saudi Arabia
| | - Naseem Alyahyawi
- Department of Pediatrics, King Abdulaziz University Hospital, Jeddah, Saudi Arabia
| | - Hamed Kord-Varkaneh
- Department of Nutrition and Food Hygiene, Nutrition Health Research Center, Hamadan University of Medical Sciences, Hamadan, Iran.
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Baek EB, Lee J, Hwang JH, Park H, Lee BS, Kim YB, Jun SY, Her J, Son HY, Cho JW. Application of multiple-finding segmentation utilizing Mask R-CNN-based deep learning in a rat model of drug-induced liver injury. Sci Rep 2023; 13:17555. [PMID: 37845356 PMCID: PMC10579263 DOI: 10.1038/s41598-023-44897-8] [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: 03/22/2023] [Accepted: 10/13/2023] [Indexed: 10/18/2023] Open
Abstract
Drug-induced liver injury (DILI) presents significant diagnostic challenges, and recently artificial intelligence-based deep learning technology has been used to predict various hepatic findings. In this study, we trained a set of Mask R-CNN-based deep algorithms to learn and quantify typical toxicant induced-histopathological lesions, portal area, and connective tissue in Sprague Dawley rats. We compared a set of single-finding models (SFMs) and a combined multiple-finding model (MFM) for their ability to simultaneously detect, classify, and quantify multiple hepatic findings on rat liver slide images. All of the SFMs yielded mean average precision (mAP) values above 85%, suggesting that the models had been successfully established. The MFM showed better performance than the SFMs, with a total mAP value of 92.46%. We compared the model predictions for slide images with ground-truth annotations generated by an accredited pathologist. For the MFM, the overall and individual finding predictions were highly correlated with the annotated areas, with R-squared values of 0.852, 0.952, 0.999, 0.990, and 0.958 being obtained for portal area, infiltration, necrosis, vacuolation, and connective tissue (including fibrosis), respectively. Our results indicate that the proposed MFM could be a useful tool for detecting and predicting multiple hepatic findings in basic non-clinical study settings.
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Affiliation(s)
- Eun Bok Baek
- College of Veterinary Medicine, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon, 34134, Republic of Korea
| | - Jaeku Lee
- Research and Development Team, LAC Inc, Seoul, Republic of Korea
| | - Ji-Hee Hwang
- Toxicologic Pathology Research Group, Department of Advanced Toxicology Research, Korea Institute of Toxicology, 141 Gajeong-ro, Yuseong-gu, Daejeon, 34114, Republic of Korea
| | - Heejin Park
- Toxicologic Pathology Research Group, Department of Advanced Toxicology Research, Korea Institute of Toxicology, 141 Gajeong-ro, Yuseong-gu, Daejeon, 34114, Republic of Korea
| | - Byoung-Seok Lee
- Toxicologic Pathology Research Group, Department of Advanced Toxicology Research, Korea Institute of Toxicology, 141 Gajeong-ro, Yuseong-gu, Daejeon, 34114, Republic of Korea
| | - Yong-Bum Kim
- Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon, 34114, Republic of Korea
| | - Sang-Yeop Jun
- Research and Development Team, LAC Inc, Seoul, Republic of Korea
| | - Jun Her
- Research and Development Team, LAC Inc, Seoul, Republic of Korea
| | - Hwa-Young Son
- College of Veterinary Medicine, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon, 34134, Republic of Korea.
| | - Jae-Woo Cho
- Toxicologic Pathology Research Group, Department of Advanced Toxicology Research, Korea Institute of Toxicology, 141 Gajeong-ro, Yuseong-gu, Daejeon, 34114, Republic of Korea.
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Naik SN, Forlano R, Manousou P, Goldin R, Angelini ED. Fibrosis severity scoring on Sirius red histology with multiple-instance deep learning. BIOLOGICAL IMAGING 2023; 3:e17. [PMID: 38510166 PMCID: PMC10951930 DOI: 10.1017/s2633903x23000144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 05/08/2023] [Accepted: 06/23/2023] [Indexed: 03/22/2024]
Abstract
Non-alcoholic fatty liver disease (NAFLD) is now the leading cause of chronic liver disease, affecting approximately 30% of people worldwide. Histopathology reading of fibrosis patterns is crucial to diagnosing NAFLD. In particular, separating mild from severe stages corresponds to a critical transition as it correlates with clinical outcomes. Deep Learning for digitized histopathology whole-slide images (WSIs) can reduce high inter- and intra-rater variability. We demonstrate a novel solution to score fibrosis severity on a retrospective cohort of 152 Sirius-Red WSIs, with fibrosis stage annotated at slide level by an expert pathologist. We exploit multiple instance learning and multiple-inferences to address the sparsity of pathological signs. We achieved an accuracy of , an F1 score of and an AUC of . These results set new state-of-the-art benchmarks for this application.
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Affiliation(s)
- Sneha N. Naik
- ITMAT Data Science Group, NIHR Imperial BRC, Imperial College, London, United Kingdom
- Heffner Biomedical Imaging Lab, Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Roberta Forlano
- Faculty of Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College, London, United Kingdom
| | - Pinelopi Manousou
- Department of Hepatology, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Robert Goldin
- Section for Pathology, Imperial College, London, United Kingdom
| | - Elsa D. Angelini
- ITMAT Data Science Group, NIHR Imperial BRC, Imperial College, London, United Kingdom
- Heffner Biomedical Imaging Lab, Department of Biomedical Engineering, Columbia University, New York, NY, USA
- Faculty of Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College, London, United Kingdom
- Telecom Paris, Institut Polytechnique de Paris, LTCI, Palaiseau, France
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8
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Kirk D, Kok E, Tufano M, Tekinerdogan B, Feskens EJM, Camps G. Machine Learning in Nutrition Research. Adv Nutr 2022; 13:2573-2589. [PMID: 36166846 PMCID: PMC9776646 DOI: 10.1093/advances/nmac103] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 08/02/2022] [Accepted: 09/22/2022] [Indexed: 01/29/2023] Open
Abstract
Data currently generated in the field of nutrition are becoming increasingly complex and high-dimensional, bringing with them new methods of data analysis. The characteristics of machine learning (ML) make it suitable for such analysis and thus lend itself as an alternative tool to deal with data of this nature. ML has already been applied in important problem areas in nutrition, such as obesity, metabolic health, and malnutrition. Despite this, experts in nutrition are often without an understanding of ML, which limits its application and therefore potential to solve currently open questions. The current article aims to bridge this knowledge gap by supplying nutrition researchers with a resource to facilitate the use of ML in their research. ML is first explained and distinguished from existing solutions, with key examples of applications in the nutrition literature provided. Two case studies of domains in which ML is particularly applicable, precision nutrition and metabolomics, are then presented. Finally, a framework is outlined to guide interested researchers in integrating ML into their work. By acting as a resource to which researchers can refer, we hope to support the integration of ML in the field of nutrition to facilitate modern research.
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Affiliation(s)
- Daniel Kirk
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands
| | - Esther Kok
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands
| | - Michele Tufano
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands
| | - Bedir Tekinerdogan
- Information Technology Group, Wageningen University and Research, Wageningen, The Netherlands
| | - Edith J M Feskens
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands
| | - Guido Camps
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands.,OnePlanet Research Center, Wageningen, The Netherlands
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9
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Ülger Y, Delik A. Artificial intelligence model with deep learning in nonalcoholic fatty liver disease diagnosis: genetic based artificial neural networks. NUCLEOSIDES, NUCLEOTIDES & NUCLEIC ACIDS 2022; 42:398-406. [PMID: 36448439 DOI: 10.1080/15257770.2022.2152046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Nonalcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver disease in the world. The NAFLD spectrum includes simple steatosis, steatohepatitis, fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). Genetic, nutritional factors, obesity, insulin resistance, gut microbiota are among the risk factors for NAFLD. The genetic variant Patatin-like phospholipase domain-containing protein 3 (PNPLA3) plays an important role in the development of a number of liver diseases ranging from steatosis, chronic hepatitis, cirrhosis and HCC. Due to the increase in the prevalence of NAFLD, new models are being developed with machine learning, deep learning, artificial neural network (ANN) algorithms in the field of artificial intelligence (AI) to determine low-cost, noninvasive diagnostic methods. Models developed with ANN from AI modules are important in order to examine biochemical and genomic information in detail in the diagnosis of NAFLD. The aim of this study is to develop a simple ANN model using biochemical and genotypic parameters in the diagnosis of NAFLD. A total of 300 patients followed up with the diagnosis of NAFLD and 100 controls were included in the study. The data set was divided into two as training and test set. Genotyping of PNPLA3 (CC, CG, GG) as genomic analysis was performed with real time PCR device. The algorithm used for the diagnosis of NAFLD was designed using age, body mass index (BMI), mean platelet volume (MPV), insulin resistance (IR), alanine aminotransferase (ALT), genotype PNPLA3 (CC, CG, GG) parameters. MLP Classifier algorithm from ANN was used in the development of the model. ANN algorithms are used in python programming language. Statistical analyzes were made in SPSS program. Percent accuracy, area under the ROC curve, confusion matrix, Positive (PPV) and Negative Predicted Value (NPV) values, precision, recall, and f1-score results were determined. The accuracy percentage was determined as 0.979 in the train set and 0.970 in the test set. The Log Loss value was set to 0.09. The developed neural network achieved an accuracy percentage of 97.0% during testing, with an area under the ROC curve value of 0.95. We think that the ANN model developed with genomic and biochemical parameters can be used as a cost-effective, noninvasive new predictive diagnostic model in clinical practice in the diagnosis of NAFLD.
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Affiliation(s)
- Yakup Ülger
- Faculty of Medicine, Department of Gastroenterology, Cukurova University, Adana, Turkey
| | - Anıl Delik
- Faculty of Medicine, Department of Gastroenterology, Cukurova University, Adana, Turkey
- Faculty of Science and Literature Department of Biology, Cukurova University, Adana, Turkey
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