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Lyakhova UA, Lyakhov PA. Systematic review of approaches to detection and classification of skin cancer using artificial intelligence: Development and prospects. Comput Biol Med 2024; 178:108742. [PMID: 38875908 DOI: 10.1016/j.compbiomed.2024.108742] [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: 01/10/2024] [Revised: 06/03/2024] [Accepted: 06/08/2024] [Indexed: 06/16/2024]
Abstract
In recent years, there has been a significant improvement in the accuracy of the classification of pigmented skin lesions using artificial intelligence algorithms. Intelligent analysis and classification systems are significantly superior to visual diagnostic methods used by dermatologists and oncologists. However, the application of such systems in clinical practice is severely limited due to a lack of generalizability and risks of potential misclassification. Successful implementation of artificial intelligence-based tools into clinicopathological practice requires a comprehensive study of the effectiveness and performance of existing models, as well as further promising areas for potential research development. The purpose of this systematic review is to investigate and evaluate the accuracy of artificial intelligence technologies for detecting malignant forms of pigmented skin lesions. For the study, 10,589 scientific research and review articles were selected from electronic scientific publishers, of which 171 articles were included in the presented systematic review. All selected scientific articles are distributed according to the proposed neural network algorithms from machine learning to multimodal intelligent architectures and are described in the corresponding sections of the manuscript. This research aims to explore automated skin cancer recognition systems, from simple machine learning algorithms to multimodal ensemble systems based on advanced encoder-decoder models, visual transformers (ViT), and generative and spiking neural networks. In addition, as a result of the analysis, future directions of research, prospects, and potential for further development of automated neural network systems for classifying pigmented skin lesions are discussed.
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Affiliation(s)
- U A Lyakhova
- Department of Mathematical Modeling, North-Caucasus Federal University, 355017, Stavropol, Russia.
| | - P A Lyakhov
- Department of Mathematical Modeling, North-Caucasus Federal University, 355017, Stavropol, Russia; North-Caucasus Center for Mathematical Research, North-Caucasus Federal University, 355017, Stavropol, Russia.
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2
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Li Pomi F, Papa V, Borgia F, Vaccaro M, Pioggia G, Gangemi S. Artificial Intelligence: A Snapshot of Its Application in Chronic Inflammatory and Autoimmune Skin Diseases. Life (Basel) 2024; 14:516. [PMID: 38672786 PMCID: PMC11051135 DOI: 10.3390/life14040516] [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: 03/29/2024] [Revised: 04/10/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024] Open
Abstract
Immuno-correlated dermatological pathologies refer to skin disorders that are closely associated with immune system dysfunction or abnormal immune responses. Advancements in the field of artificial intelligence (AI) have shown promise in enhancing the diagnosis, management, and assessment of immuno-correlated dermatological pathologies. This intersection of dermatology and immunology plays a pivotal role in comprehending and addressing complex skin disorders with immune system involvement. The paper explores the knowledge known so far and the evolution and achievements of AI in diagnosis; discusses segmentation and the classification of medical images; and reviews existing challenges, in immunological-related skin diseases. From our review, the role of AI has emerged, especially in the analysis of images for both diagnostic and severity assessment purposes. Furthermore, the possibility of predicting patients' response to therapies is emerging, in order to create tailored therapies.
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Affiliation(s)
- Federica Li Pomi
- Department of Precision Medicine in Medical, Surgical and Critical Care (Me.Pre.C.C.), University of Palermo, 90127 Palermo, Italy;
| | - Vincenzo Papa
- Department of Clinical and Experimental Medicine, School and Operative Unit of Allergy and Clinical Immunology, University of Messina, 98125 Messina, Italy; (V.P.); (S.G.)
| | - Francesco Borgia
- Department of Clinical and Experimental Medicine, Section of Dermatology, University of Messina, 98125 Messina, Italy;
| | - Mario Vaccaro
- Department of Clinical and Experimental Medicine, Section of Dermatology, University of Messina, 98125 Messina, Italy;
| | - Giovanni Pioggia
- Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy (CNR), 98164 Messina, Italy;
| | - Sebastiano Gangemi
- Department of Clinical and Experimental Medicine, School and Operative Unit of Allergy and Clinical Immunology, University of Messina, 98125 Messina, Italy; (V.P.); (S.G.)
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Huang HY, Nguyen HT, Lin TL, Saenprasarn P, Liu PH, Wang HC. Identification of Skin Lesions by Snapshot Hyperspectral Imaging. Cancers (Basel) 2024; 16:217. [PMID: 38201644 PMCID: PMC10778186 DOI: 10.3390/cancers16010217] [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: 12/04/2023] [Revised: 12/28/2023] [Accepted: 01/01/2024] [Indexed: 01/12/2024] Open
Abstract
This study pioneers the application of artificial intelligence (AI) and hyperspectral imaging (HSI) in the diagnosis of skin cancer lesions, particularly focusing on Mycosis fungoides (MF) and its differentiation from psoriasis (PsO) and atopic dermatitis (AD). By utilizing a comprehensive dataset of 1659 skin images, including cases of MF, PsO, AD, and normal skin, a novel multi-frame AI algorithm was used for computer-aided diagnosis. The automatic segmentation and classification of skin lesions were further explored using advanced techniques, such as U-Net Attention models and XGBoost algorithms, transforming images from the color space to the spectral domain. The potential of AI and HSI in dermatological diagnostics was underscored, offering a noninvasive, efficient, and accurate alternative to traditional methods. The findings are particularly crucial for early-stage invasive lesion detection in MF, showcasing the model's robust performance in segmenting and classifying lesions and its superior predictive accuracy validated through k-fold cross-validation. The model attained its optimal performance with a k-fold cross-validation value of 7, achieving a sensitivity of 90.72%, a specificity of 96.76%, an F1-score of 90.08%, and an ROC-AUC of 0.9351. This study marks a substantial advancement in dermatological diagnostics, thereby contributing significantly to the early and precise identification of skin malignancies and inflammatory conditions.
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Affiliation(s)
- Hung-Yi Huang
- Department of Dermatology, Ditmanson Medical Foundation Chiayi Christian Hospital, Chia Yi City 60002, Taiwan;
| | - Hong-Thai Nguyen
- Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi City 62102, Taiwan
| | - Teng-Li Lin
- Department of Dermatology, Dalin Tzu Chi General Hospital, No. 2, Min-Sheng Rd., Dalin Town, Chia Yi City 62247, Taiwan;
| | - Penchun Saenprasarn
- School of Nursing, Shinawatra University, 99 Moo 10, Bangtoey, Samkhok, Pathum Thani 12160, Thailand;
| | - Ping-Hung Liu
- Division of General Surgery, Department of Surgery, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st.Rd., Lingya District, Kaohsiung City 80284, Taiwan
| | - Hsiang-Chen Wang
- Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi City 62102, Taiwan
- Director of Technology Development, Hitspectra Intelligent Technology Co., Ltd., Kaohsiung City 80661, Taiwan
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4
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Liu Z, Wang X, Ma Y, Lin Y, Wang G. Artificial intelligence in psoriasis: Where we are and where we are going. Exp Dermatol 2023; 32:1884-1899. [PMID: 37740587 DOI: 10.1111/exd.14938] [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: 06/15/2023] [Revised: 09/05/2023] [Accepted: 09/09/2023] [Indexed: 09/24/2023]
Abstract
Artificial intelligence (AI) is a field of computer science that involves the development of programs designed to replicate human cognitive processes and the analysis of complex data. In dermatology, which is predominantly a visual-based diagnostic field, AI has become increasingly important in improving professional processes, particularly in the diagnosis of psoriasis. In this review, we summarized current AI applications in psoriasis: (i) diagnosis, including identification, classification, lesion segmentation, lesion severity and area scoring; (ii) treatment, including prediction treatment efficiency and prediction candidate drugs; (iii) management, including e-health and preventive medicine. Key challenges and future aspects of AI in psoriasis were also discussed, in hope of providing potential directions for future studies.
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Affiliation(s)
- Zhenhua Liu
- Department of Dermatology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
- Department of Dermatology, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Xinyu Wang
- Department of Economics, Finance and Healthcare Administration, Valdosta State University, Valdosta, Georgia, USA
| | - Yao Ma
- Student Brigade of Basic Medicine School, Fourth Military Medical University, Xi'an, China
| | - Yiting Lin
- Department of Dermatology, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Gang Wang
- Department of Dermatology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
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Ogura Y, Shimano M, Bise R, Yamashita T, Katagiri C, Sato I. Analysis of optical absorption of photoaged human skin using a high-frequency illumination microscopy analysis system. Exp Dermatol 2023; 32:1402-1411. [PMID: 37264684 DOI: 10.1111/exd.14840] [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: 10/16/2022] [Revised: 04/04/2023] [Accepted: 05/07/2023] [Indexed: 06/03/2023]
Abstract
Skin is composed of different layers, including the stratum corneum, epidermal living layer and papillary and reticular dermis. Each has specific optical properties due to differences in their biological components. Alterations in the skin's cutaneous biological components resulting from photoaging caused by chronic exposure to UV light affect the deterioration of appearance associated with the skin's optical properties. Various methods for analysing cutaneous optical properties have been previously proposed, including mathematical models and computer simulations. However, these were insufficient to elucidate changes in each skin layer and comprehensively understand the skin's integrated optical properties. We focused on UV-induced yellowing of the facial skin. We evaluated site-specific optical absorption of human skin tissue sections to investigate the yellowish discoloration, which is suggested to be related to the photodamage process. The method includes our original technique of separating the transmitted and scattered light using high-frequency illumination microscopy, leading to microscopic analysis of the tissue's optical absorption in the regions of interest. In analysing the sun-exposed facial skin tissue sections, we successfully showed that dermal regions of aged skin have increased absorption at 450 nm, where yellowish colours are complemented. Furthermore, we confirmed that elastic fibres with observable histological disorder resulting from photodamage are a prominent source of high optical absorption. We detected changes in the skin's optical absorption associated with dermal degeneration resulting from photodamage using a novel optical microscopy technique. The results provide a base for the evaluation of optical property changes for both yellowing discoloration and other tissue disorders.
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Affiliation(s)
- Yuki Ogura
- Shiseido Global Innovation Center, Yokohama, Japan
| | | | - Ryoma Bise
- Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan
| | | | | | - Imari Sato
- National Institute of Informatics, Tokyo, Japan
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Todorov A, Torah R, Ardern-Jones MR, Beeby SP. Electromagnetic Sensing Techniques for Monitoring Atopic Dermatitis-Current Practices and Possible Advancements: A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:3935. [PMID: 37112275 PMCID: PMC10144024 DOI: 10.3390/s23083935] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 03/29/2023] [Accepted: 04/04/2023] [Indexed: 06/19/2023]
Abstract
Atopic dermatitis (AD) is one of the most common skin disorders, affecting nearly one-fifth of children and adolescents worldwide, and currently, the only method of monitoring the condition is through an in-person visual examination by a clinician. This method of assessment poses an inherent risk of subjectivity and can be restrictive to patients who do not have access to or cannot visit hospitals. Advances in digital sensing technologies can serve as a foundation for the development of a new generation of e-health devices that provide accurate and empirical evaluation of the condition to patients worldwide. The goal of this review is to study the past, present, and future of AD monitoring. First, current medical practices such as biopsy, tape stripping and blood serum are discussed with their merits and demerits. Then, alternative digital methods of medical evaluation are highlighted with the focus on non-invasive monitoring using biomarkers of AD-TEWL, skin permittivity, elasticity, and pruritus. Finally, possible future technologies are showcased such as radio frequency reflectometry and optical spectroscopy along with a short discussion to provoke research into improving the current techniques and employing the new ones to develop an AD monitoring device, which could eventually facilitate medical diagnosis.
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Affiliation(s)
- Alexandar Todorov
- Centre of Flexible Electronics and E-Textiles, School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Russel Torah
- Centre of Flexible Electronics and E-Textiles, School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Michael R. Ardern-Jones
- Clinical Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton SO16 1DU, UK
| | - Steve P. Beeby
- Centre of Flexible Electronics and E-Textiles, School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
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Lunge SB, Shetty NS, Sardesai VR, Karagaiah P, Yamauchi PS, Weinberg JM, Kircik L, Giulini M, Goldust M. Therapeutic application of machine learning in psoriasis: A Prisma systematic review. J Cosmet Dermatol 2023; 22:378-382. [PMID: 35621249 DOI: 10.1111/jocd.15122] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 05/15/2022] [Accepted: 05/24/2022] [Indexed: 11/27/2022]
Abstract
Dermatology, being a predominantly visual-based diagnostic field, has found itself to be at the epitome of artificial intelligence (AI)-based advances. Machine learning (ML), a subset of AI, goes a step further by recognizing patterns from data and teaches machines to automatically learn tasks. Although artificial intelligence in dermatology is mostly developed in melanoma and skin cancer diagnosis, advances in AI and ML have gone far ahead and found its application in ulcer assessment, psoriasis, atopic dermatitis, onychomycosis, etc. This article is focused on the application of ML in the therapeutic aspect of psoriasis.
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Affiliation(s)
- Snehal Balvant Lunge
- Department of Dermatology, Venereology and Leprosy, Bharati Vidyapeeth (DTU) Medical College and Hospital, Pune, India
| | - Nandini Sundar Shetty
- Department of Dermatology, Venereology and Leprosy, Bharati Vidyapeeth (DTU) Medical College and Hospital, Pune, India
| | - Vidyadhar R Sardesai
- Department of Dermatology, Venereology and Leprosy, Bharati Vidyapeeth (DTU) Medical College and Hospital, Pune, India
| | - Priyanka Karagaiah
- Department of dermatology, Bangalore Medical College and Research Institute, Bangalore, India
| | - Paul S Yamauchi
- Dermatology Institute and Skin Care Center, Santa Monica, California, USA
- Division of Dermatology, David Geffen School of Medicine at University of California, Los Angeles, California, USA
| | | | - Leon Kircik
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Mario Giulini
- Department of Dermatology, University Medical Center of the Johannes Gutenberg University, Mainz, Germany
| | - Mohamad Goldust
- Department of Dermatology, University Medical Center Mainz, Mainz, Germany
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Classification of Skin Cancer Using Novel Hyperspectral Imaging Engineering via YOLOv5. J Clin Med 2023; 12:jcm12031134. [PMID: 36769781 PMCID: PMC9918106 DOI: 10.3390/jcm12031134] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 01/30/2023] [Accepted: 01/30/2023] [Indexed: 02/04/2023] Open
Abstract
Many studies have recently used several deep learning methods for detecting skin cancer. However, hyperspectral imaging (HSI) is a noninvasive optics system that can obtain wavelength information on the location of skin cancer lesions and requires further investigation. Hyperspectral technology can capture hundreds of narrow bands of the electromagnetic spectrum both within and outside the visible wavelength range as well as bands that enhance the distinction of image features. The dataset from the ISIC library was used in this study to detect and classify skin cancer on the basis of basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and seborrheic keratosis (SK). The dataset was divided into training and test sets, and you only look once (YOLO) version 5 was applied to train the model. The model performance was judged according to the generated confusion matrix and five indicating parameters, including precision, recall, specificity, accuracy, and the F1-score of the trained model. Two models, namely, hyperspectral narrowband image (HSI-NBI) and RGB classification, were built and then compared in this study to understand the performance of HSI with the RGB model. Experimental results showed that the HSI model can learn the SCC feature better than the original RGB image because the feature is more prominent or the model is not captured in other categories. The recall rate of the RGB and HSI models were 0.722 to 0.794, respectively, thereby indicating an overall increase of 7.5% when using the HSI model.
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Hodak E, Geskin L, Guenova E, Ortiz-Romero PL, Willemze R, Zheng J, Cowan R, Foss F, Mangas C, Querfeld C. Real-Life Barriers to Diagnosis of Early Mycosis Fungoides: An International Expert Panel Discussion. Am J Clin Dermatol 2023; 24:5-14. [PMID: 36399227 PMCID: PMC9673193 DOI: 10.1007/s40257-022-00732-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/19/2022] [Indexed: 11/19/2022]
Abstract
Mycosis fungoides (MF) is a rare, primary cutaneous T-cell lymphoma that is challenging to diagnose due to its heterogeneous clinical presentation and complex histology. The subtlety of the initial clinical appearance of MF can result in diagnostic delays and hesitancy to refer suspected cases to specialist clinics. An unmet need remains for greater awareness and education. Therefore, an international expert panel of dermatologists, oncologists, hematologists, and dermatopathologists convened to discuss and identify barriers to early and accurate MF diagnosis that could guide clinicians toward making a correct diagnosis. Confirmation of MF requires accurate assessment of symptoms and clinical signs, and subsequent correlation with dermatopathological findings. This review summarizes the expert panel's guidance, based on the literature and real-life experience, for dermatologists to help include MF in their list of differential diagnoses, along with simple clinical and histopathologic checklists that may help clinicians to suspect and identify potential MF lesions and reduce diagnostic delays.
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Affiliation(s)
- Emmilia Hodak
- Division of Dermatology, Rabin Medical Center, Beilinson Hospital, Tel Aviv University, 39 Jabotinsky Street, Petah Tiqva, 49100, Tel Aviv, Israel.
| | - Larisa Geskin
- Columbia University Medical Center, Columbia University, New York, NY, USA
| | - Emmanuella Guenova
- University Hospital Lausanne (CHUV) and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Pablo L Ortiz-Romero
- Department of Dermatology, Hospital 12 de Octubre, Institute i+12, CIBERONC, Medical School, University Complutense, Madrid, Spain
| | - Rein Willemze
- Leiden University Medical Center, Leiden, The Netherlands
| | - Jie Zheng
- Ruijin Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Richard Cowan
- Christie Hospital, The Christie School of Oncology, Manchester, UK
| | - Francine Foss
- Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
| | - Cristina Mangas
- Dermatology Department and Institute of Oncology of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona, Switzerland
| | - Christiane Querfeld
- Division of Dermatology and Department of Pathology, City of Hope National Medical Center and Beckman Research Institute, Duarte, CA, USA
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Tsai CL, Mukundan A, Chung CS, Chen YH, Wang YK, Chen TH, Tseng YS, Huang CW, Wu IC, Wang HC. Hyperspectral Imaging Combined with Artificial Intelligence in the Early Detection of Esophageal Cancer. Cancers (Basel) 2021; 13:cancers13184593. [PMID: 34572819 PMCID: PMC8469506 DOI: 10.3390/cancers13184593] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 09/09/2021] [Accepted: 09/10/2021] [Indexed: 02/08/2023] Open
Abstract
Simple Summary Detection of early esophageal cancer is important to improve patient survival, however, early diagnosis of the cancer cells is difficult, even for experienced endoscopists. This article provides a new method by using hyperspectral imaging and a deep learning diagnosis model to classify and diagnose esophageal cancer using a single-shot multibox detector. The accuracy of the results when using an RGB image in WLI was 83% and while using the spectrum data the accuracy was increased to 88%. There was an increase of 5% in WLI. The accuracy of the results when using an RGB image in NBI was 86% and while using the spectrum data the accuracy was increased to 91%. There was an increase of 5% in NBI. This study proves that the accuracy of prediction when using the spectrum data has been significantly improved and the diagnosis of narrow-band endoscopy data is more sensitive than that of white-light endoscopy. Abstract This study uses hyperspectral imaging (HSI) and a deep learning diagnosis model that can identify the stage of esophageal cancer and mark the locations. This model simulates the spectrum data from the image using an algorithm developed in this study which is combined with deep learning for the classification and diagnosis of esophageal cancer using a single-shot multibox detector (SSD)-based identification system. Some 155 white-light endoscopic images and 153 narrow-band endoscopic images of esophageal cancer were used to evaluate the prediction model. The algorithm took 19 s to predict the results of 308 test images and the accuracy of the test results of the WLI and NBI esophageal cancer was 88 and 91%, respectively, when using the spectral data. Compared with RGB images, the accuracy of the WLI was 83% and the NBI was 86%. In this study, the accuracy of the WLI and NBI was increased by 5%, confirming that the prediction accuracy of the HSI detection method is significantly improved.
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Affiliation(s)
- Cho-Lun Tsai
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Ditmanson Medical Foundation Chiayi Christian Hospital, Chia Yi City 60002, Taiwan; (C.-L.T.); (Y.-H.C.); (T.-H.C.)
| | - Arvind Mukundan
- Department of Mechanical Engineering, Advanced Institute of Manufacturing with High tech Innovations (AIM-HI) and Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi County 62102, Taiwan; (A.M.); (Y.-S.T.)
| | - Chen-Shuan Chung
- Department of Internal Medicine, Far Eastern Memorial Hospital, No.21, Sec. 2, Nanya S. Rd., Banciao Dist., New Taipei City 22060, Taiwan;
| | - Yi-Hsun Chen
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Ditmanson Medical Foundation Chiayi Christian Hospital, Chia Yi City 60002, Taiwan; (C.-L.T.); (Y.-H.C.); (T.-H.C.)
| | - Yao-Kuang Wang
- Division of Gastroenterology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, No.100, Tzyou 1st Rd., Sanmin Dist., Kaohsiung City 80756, Taiwan; (Y.-K.W.); (I.-C.W.)
- Department of Medicine, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, No.100, Tzyou 1st Rd., Sanmin Dist., Kaohsiung City 80756, Taiwan
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, No.100, Tzyou 1st Rd., Sanmin Dist., Kaohsiung City 80756, Taiwan
| | - Tsung-Hsien Chen
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Ditmanson Medical Foundation Chiayi Christian Hospital, Chia Yi City 60002, Taiwan; (C.-L.T.); (Y.-H.C.); (T.-H.C.)
| | - Yu-Sheng Tseng
- Department of Mechanical Engineering, Advanced Institute of Manufacturing with High tech Innovations (AIM-HI) and Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi County 62102, Taiwan; (A.M.); (Y.-S.T.)
| | - Chien-Wei Huang
- Department of Gastroenterology, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st. Rd., Lingya District, Kaohsiung City 80284, Taiwan
- Department of Nursing, Tajen University, 20, Weixin Rd., Yanpu Township, Pingtung City 90741, Taiwan
- Correspondence: (C.-W.H.); (H.-C.W.)
| | - I-Chen Wu
- Division of Gastroenterology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, No.100, Tzyou 1st Rd., Sanmin Dist., Kaohsiung City 80756, Taiwan; (Y.-K.W.); (I.-C.W.)
- Department of Medicine, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, No.100, Tzyou 1st Rd., Sanmin Dist., Kaohsiung City 80756, Taiwan
| | - Hsiang-Chen Wang
- Department of Mechanical Engineering, Advanced Institute of Manufacturing with High tech Innovations (AIM-HI) and Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi County 62102, Taiwan; (A.M.); (Y.-S.T.)
- Correspondence: (C.-W.H.); (H.-C.W.)
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