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Li J, Fan X, Tang T, Wu E, Wang D, Zong H, Zhou X, Li Y, Zhang C, Zhang Y, Wu R, Wu C, Yang L, Shen B. An artificial intelligence method for predicting postoperative urinary incontinence based on multiple anatomic parameters of MRI. Heliyon 2023; 9:e20337. [PMID: 37767466 PMCID: PMC10520312 DOI: 10.1016/j.heliyon.2023.e20337] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 09/12/2023] [Accepted: 09/19/2023] [Indexed: 09/29/2023] Open
Abstract
Background Deep learning methods are increasingly applied in the medical field; however, their lack of interpretability remains a challenge. Captum is a tool that can be used to interpret neural network models by computing feature importance weights. Although Captum is an interpretable model, it is rarely used to study medical problems, and there is a scarcity of data regarding MRI anatomical measurements for patients with prostate cancer after undergoing Robotic-Assisted Radical Prostatectomy (RARP). Consequently, predictive models for continence that use multiple types of anatomical MRI measurements are limited. Methods We explored the energy efficiency of deep learning models for predicting continence by analyzing MRI measurements. We analyzed and compared various statistical models and provided reference examples for the clinical application of interpretable deep-learning models. Patients who underwent RARP at our institution between July 2019 and December 2020 were included in this study. A series of clinical MRI anatomical measurements from these patients was used to discover continence features, and their impact on continence was primarily evaluated using a series of statistical methods and computational models. Results Age and six other anatomical measurements were identified as the top seven features of continence by the proposed model UINet7 with an accuracy of 0.97, and the first four of these features were also found by primary statistical analysis. Conclusions This study fills the gaps in the in-depth investigation of continence features after RARP due to the limitations of clinical data and applicable models. We provide a pioneering example of the application of deep-learning models to clinical problems. The interpretability analysis of deep learning models has the potential for clinical applications.
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Affiliation(s)
- Jiakun Li
- Department of Urology, West China Hospital, Sichuan University, Chengdu, China
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Xuemeng Fan
- Department of Urology, West China Hospital, Sichuan University, Chengdu, China
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Tong Tang
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- Department of Computer Science and Information Technologies, Elviña Campus, University of A Coruña, A Coruña, Spain
| | - Erman Wu
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Dongyue Wang
- Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu, China
| | - Hui Zong
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Xianghong Zhou
- Department of Urology, West China Hospital, Sichuan University, Chengdu, China
| | - Yifan Li
- Department of Urology, West China Hospital, Sichuan University, Chengdu, China
| | - Chichen Zhang
- Department of Urology, West China Hospital, Sichuan University, Chengdu, China
| | - Yihang Zhang
- Department of Urology, West China Hospital, Sichuan University, Chengdu, China
| | - Rongrong Wu
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Cong Wu
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Lu Yang
- Department of Urology, West China Hospital, Sichuan University, Chengdu, China
| | - Bairong Shen
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
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Hibi M, Katada S, Kawakami A, Bito K, Ohtsuka M, Sugitani K, Muliandi A, Yamanaka N, Hasumura T, Ando Y, Fushimi T, Fujimatsu T, Akatsu T, Kawano S, Kimura R, Tsuchiya S, Yamamoto Y, Haneoka M, Kushida K, Hideshima T, Shimizu E, Suzuki J, Kirino A, Tsujimura H, Nakamura S, Sakamoto T, Tazoe Y, Yabuki M, Nagase S, Hirano T, Fukuda R, Yamashiro Y, Nagashima Y, Ojima N, Sudo M, Oya N, Minegishi Y, Misawa K, Charoenphakdee N, Gao Z, Hayashi K, Oono K, Sugawara Y, Yamaguchi S, Ono T, Maruyama H. Assessment of Multidimensional Health Care Parameters Among Adults in Japan for Developing a Virtual Human Generative Model: Protocol for a Cross-sectional Study. JMIR Res Protoc 2023; 12:e47024. [PMID: 37294611 DOI: 10.2196/47024] [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: 03/08/2023] [Revised: 04/20/2023] [Accepted: 04/20/2023] [Indexed: 06/10/2023] Open
Abstract
BACKGROUND Human health status can be measured on the basis of many different parameters. Statistical relationships among these different health parameters will enable several possible health care applications and an approximation of the current health status of individuals, which will allow for more personalized and preventive health care by informing the potential risks and developing personalized interventions. Furthermore, a better understanding of the modifiable risk factors related to lifestyle, diet, and physical activity will facilitate the design of optimal treatment approaches for individuals. OBJECTIVE This study aims to provide a high-dimensional, cross-sectional data set of comprehensive health care information to construct a combined statistical model as a single joint probability distribution and enable further studies on individual relationships among the multidimensional data obtained. METHODS In this cross-sectional observational study, data were collected from a population of 1000 adult men and women (aged ≥20 years) matching the age ratio of the typical adult Japanese population. Data include biochemical and metabolic profiles from blood, urine, saliva, and oral glucose tolerance tests; bacterial profiles from feces, facial skin, scalp skin, and saliva; messenger RNA, proteome, and metabolite analyses of facial and scalp skin surface lipids; lifestyle surveys and questionnaires; physical, motor, cognitive, and vascular function analyses; alopecia analysis; and comprehensive analyses of body odor components. Statistical analyses will be performed in 2 modes: one to train a joint probability distribution by combining a commercially available health care data set containing large amounts of relatively low-dimensional data with the cross-sectional data set described in this paper and another to individually investigate the relationships among the variables obtained in this study. RESULTS Recruitment for this study started in October 2021 and ended in February 2022, with a total of 997 participants enrolled. The collected data will be used to build a joint probability distribution called a Virtual Human Generative Model. Both the model and the collected data are expected to provide information on the relationships between various health statuses. CONCLUSIONS As different degrees of health status correlations are expected to differentially affect individual health status, this study will contribute to the development of empirically justified interventions based on the population. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/47024.
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Affiliation(s)
- Masanobu Hibi
- Biological Science Research, Kao Corporation, Tokyo, Japan
| | - Shun Katada
- Biological Science Research, Kao Corporation, Tokyo, Japan
| | - Aya Kawakami
- Digital Business Creation, Kao Corporation, Tokyo, Japan
| | - Kotatsu Bito
- Digital Business Creation, Kao Corporation, Tokyo, Japan
| | - Mayumi Ohtsuka
- Biological Science Research, Kao Corporation, Tochigi, Japan
| | - Kei Sugitani
- Biological Science Research, Kao Corporation, Tokyo, Japan
| | | | - Nami Yamanaka
- Biological Science Research, Kao Corporation, Tokyo, Japan
| | | | - Yasutoshi Ando
- Biological Science Research, Kao Corporation, Tokyo, Japan
| | | | | | - Tomoki Akatsu
- Biological Science Research, Kao Corporation, Tochigi, Japan
| | - Sawako Kawano
- Biological Science Research, Kao Corporation, Tochigi, Japan
| | - Ren Kimura
- Analytical Science Research, Kao Corporation, Tochigi, Japan
| | | | - Yuuki Yamamoto
- Analytical Science Research, Kao Corporation, Tochigi, Japan
| | - Mai Haneoka
- Analytical Science Research, Kao Corporation, Tochigi, Japan
| | - Ken Kushida
- Analytical Science Research, Kao Corporation, Tochigi, Japan
| | | | - Eri Shimizu
- Analytical Science Research, Kao Corporation, Tochigi, Japan
| | - Jumpei Suzuki
- Analytical Science Research, Kao Corporation, Tochigi, Japan
| | - Aya Kirino
- Analytical Science Research, Kao Corporation, Tochigi, Japan
| | | | - Shun Nakamura
- Analytical Science Research, Kao Corporation, Tochigi, Japan
| | | | - Yuki Tazoe
- Sensory Science Research, Kao Corporation, Tokyo, Japan
| | | | - Shinobu Nagase
- Hair Care Products Research, Kao Corporation, Tokyo, Japan
| | - Tamaki Hirano
- Hair Care Products Research, Kao Corporation, Tokyo, Japan
| | - Reiko Fukuda
- Hair Care Products Research, Kao Corporation, Tokyo, Japan
| | - Yukari Yamashiro
- Personal Health Care Products Research, Kao Corporation, Tokyo, Japan
| | | | - Nobutoshi Ojima
- Personal Health Care Products Research, Kao Corporation, Tokyo, Japan
| | - Motoki Sudo
- Personal Health Care Products Research, Kao Corporation, Tokyo, Japan
| | - Naoki Oya
- Biological Science Research, Kao Corporation, Tokyo, Japan
| | | | - Koichi Misawa
- Biological Science Research, Kao Corporation, Tokyo, Japan
| | | | | | | | | | | | | | | | - Hiroshi Maruyama
- Preferred Networks, Inc, Tokyo, Japan
- Research into Artifacts, Center for Engineering, The University of Tokyo, Tokyo, Japan
- Kao Corporation, Tokyo, Japan
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Helleckes LM, Hemmerich J, Wiechert W, von Lieres E, Grünberger A. Machine learning in bioprocess development: from promise to practice. Trends Biotechnol 2023; 41:817-835. [PMID: 36456404 DOI: 10.1016/j.tibtech.2022.10.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/20/2022] [Accepted: 10/27/2022] [Indexed: 11/30/2022]
Abstract
Fostered by novel analytical techniques, digitalization, and automation, modern bioprocess development provides large amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have great potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. Herein we demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring, and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges, and point out domains that can potentially benefit from technology transfer and further progress in the field of ML.
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Affiliation(s)
- Laura M Helleckes
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany; RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany
| | - Johannes Hemmerich
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
| | - Wolfgang Wiechert
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany; RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany
| | - Eric von Lieres
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany; RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany
| | - Alexander Grünberger
- Multiscale Bioengineering, Technical Faculty, Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany; Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany; Institute of Process Engineering in Life Sciences, Section III: Microsystems in Bioprocess Engineering, Karlsruhe Institute of Technology, Fritz-Haber-Weg 2, 76131, Karlsruhe, Germany.
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Zare Harofte S, Soltani M, Siavashy S, Raahemifar K. Recent Advances of Utilizing Artificial Intelligence in Lab on a Chip for Diagnosis and Treatment. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 18:e2203169. [PMID: 36026569 DOI: 10.1002/smll.202203169] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 07/16/2022] [Indexed: 05/14/2023]
Abstract
Nowadays, artificial intelligence (AI) creates numerous promising opportunities in the life sciences. AI methods can be significantly advantageous for analyzing the massive datasets provided by biotechnology systems for biological and biomedical applications. Microfluidics, with the developments in controlled reaction chambers, high-throughput arrays, and positioning systems, generate big data that is not necessarily analyzed successfully. Integrating AI and microfluidics can pave the way for both experimental and analytical throughputs in biotechnology research. Microfluidics enhances the experimental methods and reduces the cost and scale, while AI methods significantly improve the analysis of huge datasets obtained from high-throughput and multiplexed microfluidics. This review briefly presents a survey of the role of AI and microfluidics in biotechnology. Also, the incorporation of AI with microfluidics is comprehensively investigated. Specifically, recent studies that perform flow cytometry cell classification, cell isolation, and a combination of them by gaining from both AI methods and microfluidic techniques are covered. Despite all current challenges, various fields of biotechnology can be remarkably affected by the combination of AI and microfluidic technologies. Some of these fields include point-of-care systems, precision, personalized medicine, regenerative medicine, prognostics, diagnostics, and treatment of oncology and non-oncology-related diseases.
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Affiliation(s)
- Samaneh Zare Harofte
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, 19967-15433, Iran
| | - Madjid Soltani
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, 19967-15433, Iran
- Department of Electrical and Computer Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
- Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON, N2L 3G1, Canada
- Advanced Bioengineering Initiative Center, Multidisciplinary International Complex, K. N. Toosi University of Technology, Tehran, 14176-14411, Iran
- Cancer Biology Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, 14197-33141, Iran
| | - Saeed Siavashy
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, 19967-15433, Iran
| | - Kaamran Raahemifar
- Data Science and Artificial Intelligence Program, College of Information Sciences and Technology (IST), Penn State University, State College, PA, 16801, USA
- School of Optometry and Vision Science, Faculty of Science, University of Waterloo, 200 University Ave. W, Waterloo, ON, N2L 3G1, Canada
- Department of Chemical Engineering, Faculty of Engineering, University of Waterloo, 200 University Ave. W, Waterloo, ON, N2L 3G1, Canada
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