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Zein H, Chantaf S, Fournier R, Nait-Ali A. Generative adversarial networks for anonymous acneic face dataset generation. PLoS One 2024; 19:e0297958. [PMID: 38625866 PMCID: PMC11020863 DOI: 10.1371/journal.pone.0297958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 01/15/2024] [Indexed: 04/18/2024] Open
Abstract
It is well known that the performance of any classification model is effective if the dataset used for the training process and the test process satisfy some specific requirements. In other words, the more the dataset size is large, balanced, and representative, the more one can trust the proposed model's effectiveness and, consequently, the obtained results. Unfortunately, large-size anonymous datasets are generally not publicly available in biomedical applications, especially those dealing with pathological human face images. This concern makes using deep-learning-based approaches challenging to deploy and difficult to reproduce or verify some published results. In this paper, we propose an efficient method to generate a realistic anonymous synthetic dataset of human faces, focusing on attributes related to acne disorders at three distinct levels of severity (Mild, Moderate, and Severe). Notably, our approach initiates from a small dataset of facial acne images, leveraging generative techniques to augment and diversify the dataset, ensuring comprehensive coverage of acne severity levels while maintaining anonymity and realism in the synthetic data. Therefore, a specific hierarchy StyleGAN-based algorithm trained at distinct levels is considered. Moreover, the utilization of generative adversarial networks for augmentation offers a means to circumvent potential privacy or legal concerns associated with acquiring medical datasets. This is attributed to the synthetic nature of the generated data, where no actual subjects are present, thereby ensuring compliance with privacy regulations and legal considerations. To evaluate the performance of the proposed scheme, we consider a CNN-based classification system, trained using the generated synthetic acneic face images and tested using authentic face images. Consequently, we show that an accuracy of 97.6% is achieved using InceptionResNetv2. As a result, this work allows the scientific community to employ the generated synthetic dataset for any data processing application without restrictions on legal or ethical concerns. Moreover, this approach can also be extended to other applications requiring the generation of synthetic medical images.
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Affiliation(s)
- Hazem Zein
- LISSI Laboratory, Université Paris-Est Créteil, Vitry-sur-Seine, France
| | - Samer Chantaf
- Faculty of Technology, Lebanese University, Saida, Lebanon
| | - Régis Fournier
- LISSI Laboratory, Université Paris-Est Créteil, Vitry-sur-Seine, France
| | - Amine Nait-Ali
- LISSI Laboratory, Université Paris-Est Créteil, Vitry-sur-Seine, France
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Foltz EA, Witkowski A, Becker AL, Latour E, Lim JY, Hamilton A, Ludzik J. Artificial Intelligence Applied to Non-Invasive Imaging Modalities in Identification of Nonmelanoma Skin Cancer: A Systematic Review. Cancers (Basel) 2024; 16:629. [PMID: 38339380 PMCID: PMC10854803 DOI: 10.3390/cancers16030629] [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: 12/06/2023] [Revised: 01/28/2024] [Accepted: 01/29/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND The objective of this study is to systematically analyze the current state of the literature regarding novel artificial intelligence (AI) machine learning models utilized in non-invasive imaging for the early detection of nonmelanoma skin cancers. Furthermore, we aimed to assess their potential clinical relevance by evaluating the accuracy, sensitivity, and specificity of each algorithm and assessing for the risk of bias. METHODS Two reviewers screened the MEDLINE, Cochrane, PubMed, and Embase databases for peer-reviewed studies that focused on AI-based skin cancer classification involving nonmelanoma skin cancers and were published between 2018 and 2023. The search terms included skin neoplasms, nonmelanoma, basal-cell carcinoma, squamous-cell carcinoma, diagnostic techniques and procedures, artificial intelligence, algorithms, computer systems, dermoscopy, reflectance confocal microscopy, and optical coherence tomography. Based on the search results, only studies that directly answered the review objectives were included and the efficacy measures for each were recorded. A QUADAS-2 risk assessment for bias in included studies was then conducted. RESULTS A total of 44 studies were included in our review; 40 utilizing dermoscopy, 3 using reflectance confocal microscopy (RCM), and 1 for hyperspectral epidermal imaging (HEI). The average accuracy of AI algorithms applied to all imaging modalities combined was 86.80%, with the same average for dermoscopy. Only one of the three studies applying AI to RCM measured accuracy, with a result of 87%. Accuracy was not measured in regard to AI based HEI interpretation. CONCLUSION AI algorithms exhibited an overall favorable performance in the diagnosis of nonmelanoma skin cancer via noninvasive imaging techniques. Ultimately, further research is needed to isolate pooled diagnostic accuracy for nonmelanoma skin cancers as many testing datasets also include melanoma and other pigmented lesions.
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Affiliation(s)
- Emilie A. Foltz
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97201, USA
- Elson S. Floyd College of Medicine, Washington State University, Spokane, WA 99202, USA
| | - Alexander Witkowski
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97201, USA
| | - Alyssa L. Becker
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97201, USA
- John A. Burns School of Medicine, University of Hawai’i at Manoa, Honolulu, HI 96813, USA
| | - Emile Latour
- Biostatistics Shared Resource, Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97201, USA
| | - Jeong Youn Lim
- Biostatistics Shared Resource, Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97201, USA
| | - Andrew Hamilton
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97201, USA
| | - Joanna Ludzik
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97201, USA
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Gosak L, Pruinelli L, Topaz M, Štiglic G. The ChatGPT effect and transforming nursing education with generative AI: Discussion paper. Nurse Educ Pract 2024; 75:103888. [PMID: 38219503 DOI: 10.1016/j.nepr.2024.103888] [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: 11/05/2023] [Revised: 12/10/2023] [Accepted: 12/23/2023] [Indexed: 01/16/2024]
Abstract
AIM The aim of this study is to present the possibilities of nurse education in the use of the Chat Generative Pre-training Transformer (ChatGPT) tool to support the documentation process. BACKGROUND The success of the nursing process is based on the accuracy of nursing diagnoses, which also determine nursing interventions and nursing outcomes. Educating nurses in the use of artificial intelligence in the nursing process can significantly reduce the time nurses spend on documentation. DESIGN Discussion paper. METHODS We used a case study from Train4Health in the field of preventive care to demonstrate the potential of using Generative Pre-training Transformer (ChatGPT) to educate nurses in documenting the nursing process using generative artificial intelligence. Based on the case study, we entered a description of the patient's condition into Generative Pre-training Transformer (ChatGPT) and asked questions about nursing diagnoses, nursing interventions and nursing outcomes. We further synthesized these results. RESULTS In the process of educating nurses about the nursing process and nursing diagnosis, Generative Pre-training Transformer (ChatGPT) can present potential patient problems to nurses and guide them through the process from taking a medical history, setting nursing diagnoses and planning goals and interventions. Generative Pre-training Transformer (ChatGPT) returned appropriate nursing diagnoses, but these were not in line with the North American Nursing Diagnosis Association - International (NANDA-I) classification as requested. Of all the nursing diagnoses provided, only one was consistent with the most recent version of the North American Nursing Diagnosis Association - International (NANDA-I). Generative Pre-training Transformer (ChatGPT) is still not specific enough for nursing diagnoses, resulting in incorrect answers in several cases. CONCLUSIONS Using Generative Pre-training Transformer (ChatGPT) to educate nurses and support the documentation process is time-efficient, but it still requires a certain level of human critical-thinking and fact-checking.
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Affiliation(s)
- Lucija Gosak
- Faculty of Health Sciences, University of Maribor, Maribor 2000, Slovenia.
| | - Lisiane Pruinelli
- College of Nursing and College of Medicine, University of Florida, Gainesville, FL, USA.
| | - Maxim Topaz
- Columbia University School of Nursing, New York City, NY, USA.
| | - Gregor Štiglic
- Faculty of Health Sciences, University of Maribor, Maribor 2000, Slovenia; Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor 2000, Slovenia; Usher Institute, University of Edinburgh, Edinburgh EH8 9YL, UK.
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Cho SI, Navarrete-Dechent C, Daneshjou R, Cho HS, Chang SE, Kim SH, Na JI, Han SS. Generation of a Melanoma and Nevus Data Set From Unstandardized Clinical Photographs on the Internet. JAMA Dermatol 2023; 159:1223-1231. [PMID: 37792351 PMCID: PMC10551819 DOI: 10.1001/jamadermatol.2023.3521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 06/16/2023] [Indexed: 10/05/2023]
Abstract
Importance Artificial intelligence (AI) training for diagnosing dermatologic images requires large amounts of clean data. Dermatologic images have different compositions, and many are inaccessible due to privacy concerns, which hinder the development of AI. Objective To build a training data set for discriminative and generative AI from unstandardized internet images of melanoma and nevus. Design, Setting, and Participants In this diagnostic study, a total of 5619 (CAN5600 data set) and 2006 (CAN2000 data set; a manually revised subset of CAN5600) cropped lesion images of either melanoma or nevus were semiautomatically annotated from approximately 500 000 photographs on the internet using convolutional neural networks (CNNs), region-based CNNs, and large mask inpainting. For unsupervised pretraining, 132 673 possible lesions (LESION130k data set) were also created with diversity by collecting images from 18 482 websites in approximately 80 countries. A total of 5000 synthetic images (GAN5000 data set) were generated using the generative adversarial network (StyleGAN2-ADA; training, CAN2000 data set; pretraining, LESION130k data set). Main Outcomes and Measures The area under the receiver operating characteristic curve (AUROC) for determining malignant neoplasms was analyzed. In each test, 1 of the 7 preexisting public data sets (total of 2312 images; including Edinburgh, an SNU subset, Asan test, Waterloo, 7-point criteria evaluation, PAD-UFES-20, and MED-NODE) was used as the test data set. Subsequently, a comparative study was conducted between the performance of the EfficientNet Lite0 CNN on the proposed data set and that trained on the remaining 6 preexisting data sets. Results The EfficientNet Lite0 CNN trained on the annotated or synthetic images achieved higher or equivalent mean (SD) AUROCs to the EfficientNet Lite0 trained using the pathologically confirmed public data sets, including CAN5600 (0.874 [0.042]; P = .02), CAN2000 (0.848 [0.027]; P = .08), and GAN5000 (0.838 [0.040]; P = .31 [Wilcoxon signed rank test]) and the preexisting data sets combined (0.809 [0.063]) by the benefits of increased size of the training data set. Conclusions and Relevance The synthetic data set in this diagnostic study was created using various AI technologies from internet images. A neural network trained on the created data set (CAN5600) performed better than the same network trained on preexisting data sets combined. Both the annotated (CAN5600 and LESION130k) and synthetic (GAN5000) data sets could be shared for AI training and consensus between physicians.
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Affiliation(s)
| | | | - Roxana Daneshjou
- Department of Dermatology, Stanford University, Stanford, California
| | - Hye Soo Cho
- Department of Dermatology, Asan Medical Center, Ulsan University College of Medicine, Seoul, Korea
| | - Sung Eun Chang
- Department of Dermatology, Asan Medical Center, Ulsan University College of Medicine, Seoul, Korea
| | - Seong Hwan Kim
- Department of Plastic and Reconstructive Surgery, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Jung-Im Na
- Department of Dermatology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seoul, Korea
| | - Seung Seog Han
- Department of Dermatology, I Dermatology Clinic, Seoul, Korea
- IDerma Inc, Seoul, Korea
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Jiang S, Ma D, Tan X, Yang M, Jiao Q, Xu L. Bibliometric analysis of the current status and trends on medical hyperspectral imaging. Front Med (Lausanne) 2023; 10:1235955. [PMID: 37795419 PMCID: PMC10545955 DOI: 10.3389/fmed.2023.1235955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 08/30/2023] [Indexed: 10/06/2023] Open
Abstract
Hyperspectral imaging (HSI) is a promising technology that can provide valuable support for the advancement of the medical field. Bibliometrics can analyze a vast number of publications on both macroscopic and microscopic levels, providing scholars with essential foundations to shape future directions. The purpose of this study is to comprehensively review the existing literature on medical hyperspectral imaging (MHSI). Based on the Web of Science (WOS) database, this study systematically combs through literature using bibliometric methods and visualization software such as VOSviewer and CiteSpace to draw scientific conclusions. The analysis yielded 2,274 articles from 73 countries/regions, involving 7,401 authors, 2,037 institutions, 1,038 journals/conferences, and a total of 7,522 keywords. The field of MHSI is currently in a positive stage of development and has conducted extensive research worldwide. This research encompasses not only HSI technology but also its application to diverse medical research subjects, such as skin, cancer, tumors, etc., covering a wide range of hardware constructions and software algorithms. In addition to advancements in hardware, the future should focus on the development of algorithm standards for specific medical research targets and cultivate medical professionals of managing vast amounts of technical information.
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Affiliation(s)
| | | | | | | | | | - Liang Xu
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin,China
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Jacobs F, D'Amico S, Benvenuti C, Gaudio M, Saltalamacchia G, Miggiano C, De Sanctis R, Della Porta MG, Santoro A, Zambelli A. Opportunities and Challenges of Synthetic Data Generation in Oncology. JCO Clin Cancer Inform 2023; 7:e2300045. [PMID: 37535875 DOI: 10.1200/cci.23.00045] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 05/05/2023] [Accepted: 05/25/2023] [Indexed: 08/05/2023] Open
Abstract
Widespread interest in artificial intelligence (AI) in health care has focused mainly on deductive systems that analyze available real-world data to discover patterns not otherwise visible. Generative adversarial network, a new type of inductive AI, has recently evolved to generate high-fidelity virtual synthetic data (SD) trained on relatively limited real-world information. The AI system is fed with a collection of real data, and it learns to generate new augmented data while maintaining the general characteristics of the original data set. The use of SD to enhance clinical research and protect patient privacy has drawn a lot of interest in medicine and in the complex field of oncology. This article summarizes the main characteristics of this innovative technology and critically discusses how it can be used to accelerate data access for secondary purposes, providing an overview of the opportunities and challenges of SD generation for clinical cancer research and health care.
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Affiliation(s)
- Flavia Jacobs
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IRCCS Istituto Clinico Humanitas, Milan, Italy
| | | | - Chiara Benvenuti
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IRCCS Istituto Clinico Humanitas, Milan, Italy
| | - Mariangela Gaudio
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IRCCS Istituto Clinico Humanitas, Milan, Italy
| | | | - Chiara Miggiano
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IRCCS Istituto Clinico Humanitas, Milan, Italy
| | - Rita De Sanctis
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IRCCS Istituto Clinico Humanitas, Milan, Italy
| | - Matteo Giovanni Della Porta
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IRCCS Istituto Clinico Humanitas, Milan, Italy
| | - Armando Santoro
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IRCCS Istituto Clinico Humanitas, Milan, Italy
| | - Alberto Zambelli
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IRCCS Istituto Clinico Humanitas, Milan, Italy
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Ukharov A, Shlivko I, Klemenova I, Garanina O, Uskova K, Mironycheva A, Stepanova Y. Skin cancer risk self-assessment using AI as a mass screening tool. INFORMATICS IN MEDICINE UNLOCKED 2023. [DOI: 10.1016/j.imu.2023.101223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023] Open
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