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Aasem M, Javed Iqbal M. Toward explainable AI in radiology: Ensemble-CAM for effective thoracic disease localization in chest X-ray images using weak supervised learning. Front Big Data 2024; 7:1366415. [PMID: 38756502 PMCID: PMC11096460 DOI: 10.3389/fdata.2024.1366415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Accepted: 04/08/2024] [Indexed: 05/18/2024] Open
Abstract
Chest X-ray (CXR) imaging is widely employed by radiologists to diagnose thoracic diseases. Recently, many deep learning techniques have been proposed as computer-aided diagnostic (CAD) tools to assist radiologists in minimizing the risk of incorrect diagnosis. From an application perspective, these models have exhibited two major challenges: (1) They require large volumes of annotated data at the training stage and (2) They lack explainable factors to justify their outcomes at the prediction stage. In the present study, we developed a class activation mapping (CAM)-based ensemble model, called Ensemble-CAM, to address both of these challenges via weakly supervised learning by employing explainable AI (XAI) functions. Ensemble-CAM utilizes class labels to predict the location of disease in association with interpretable features. The proposed work leverages ensemble and transfer learning with class activation functions to achieve three objectives: (1) minimizing the dependency on strongly annotated data when locating thoracic diseases, (2) enhancing confidence in predicted outcomes by visualizing their interpretable features, and (3) optimizing cumulative performance via fusion functions. Ensemble-CAM was trained on three CXR image datasets and evaluated through qualitative and quantitative measures via heatmaps and Jaccard indices. The results reflect the enhanced performance and reliability in comparison to existing standalone and ensembled models.
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Affiliation(s)
- Muhammad Aasem
- Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan
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2
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Zhong F, He K, Ji M, Chen J, Gao T, Li S, Zhang J, Li C. Optimizing vitiligo diagnosis with ResNet and Swin transformer deep learning models: a study on performance and interpretability. Sci Rep 2024; 14:9127. [PMID: 38644396 PMCID: PMC11033269 DOI: 10.1038/s41598-024-59436-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 04/10/2024] [Indexed: 04/23/2024] Open
Abstract
Vitiligo is a hypopigmented skin disease characterized by the loss of melanin. The progressive nature and widespread incidence of vitiligo necessitate timely and accurate detection. Usually, a single diagnostic test often falls short of providing definitive confirmation of the condition, necessitating the assessment by dermatologists who specialize in vitiligo. However, the current scarcity of such specialized medical professionals presents a significant challenge. To mitigate this issue and enhance diagnostic accuracy, it is essential to build deep learning models that can support and expedite the detection process. This study endeavors to establish a deep learning framework to enhance the diagnostic accuracy of vitiligo. To this end, a comparative analysis of five models including ResNet (ResNet34, ResNet50, and ResNet101 models) and Swin Transformer series (Swin Transformer Base, and Swin Transformer Large models), were conducted under the uniform condition to identify the model with superior classification capabilities. Moreover, the study sought to augment the interpretability of these models by selecting one that not only provides accurate diagnostic outcomes but also offers visual cues highlighting the regions pertinent to vitiligo. The empirical findings reveal that the Swin Transformer Large model achieved the best performance in classification, whose AUC, accuracy, sensitivity, and specificity are 0.94, 93.82%, 94.02%, and 93.5%, respectively. In terms of interpretability, the highlighted regions in the class activation map correspond to the lesion regions of the vitiligo images, which shows that it effectively indicates the specific category regions associated with the decision-making of dermatological diagnosis. Additionally, the visualization of feature maps generated in the middle layer of the deep learning model provides insights into the internal mechanisms of the model, which is valuable for improving the interpretability of the model, tuning performance, and enhancing clinical applicability. The outcomes of this study underscore the significant potential of deep learning models to revolutionize medical diagnosis by improving diagnostic accuracy and operational efficiency. The research highlights the necessity for ongoing exploration in this domain to fully leverage the capabilities of deep learning technologies in medical diagnostics.
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Affiliation(s)
- Fan Zhong
- College of Electrical Engineering, Sichuan University, Chengdu, China
| | - Kaiqiao He
- Department of Dermatology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Mengqi Ji
- College of Electrical Engineering, Sichuan University, Chengdu, China
| | - Jianru Chen
- Department of Dermatology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Tianwen Gao
- Department of Dermatology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Shuli Li
- Department of Dermatology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Junpeng Zhang
- College of Electrical Engineering, Sichuan University, Chengdu, China.
| | - Chunying Li
- Department of Dermatology, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
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3
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Ranjbari D, Abbasgholizadeh Rahimi S. Implications of conscious AI in primary healthcare. Fam Med Community Health 2024; 12:e002625. [PMID: 38485268 PMCID: PMC10941173 DOI: 10.1136/fmch-2023-002625] [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: 10/31/2023] [Accepted: 02/29/2024] [Indexed: 03/17/2024] Open
Abstract
The conversation about consciousness of artificial intelligence (AI) is an ongoing topic since 1950s. Despite the numerous applications of AI identified in healthcare and primary healthcare, little is known about how a conscious AI would reshape its use in this domain. While there is a wide range of ideas as to whether AI can or cannot possess consciousness, a prevailing theme in all arguments is uncertainty. Given this uncertainty and the high stakes associated with the use of AI in primary healthcare, it is imperative to be prepared for all scenarios including conscious AI systems being used for medical diagnosis, shared decision-making and resource management in the future. This commentary serves as an overview of some of the pertinent evidence supporting the use of AI in primary healthcare and proposes ideas as to how consciousnesses of AI can support or further complicate these applications. Given the scarcity of evidence on the association between consciousness of AI and its current state of use in primary healthcare, our commentary identifies some directions for future research in this area including assessing patients', healthcare workers' and policy-makers' attitudes towards consciousness of AI systems in primary healthcare settings.
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Affiliation(s)
- Dorsai Ranjbari
- McGill University Faculty of Medicine and Health Sciences, Montreal, Quebec, Canada
| | - Samira Abbasgholizadeh Rahimi
- Family Medicine, Faculty of Medicine and Health Sciences and Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Quebec, Canada
- Mila - Quebec AI Institute, Montreal, Quebec, Canada
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
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4
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Venkatesh KP, Raza MM, Nickel G, Wang S, Kvedar JC. Deep learning models across the range of skin disease. NPJ Digit Med 2024; 7:32. [PMID: 38341516 DOI: 10.1038/s41746-024-01033-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 02/05/2024] [Indexed: 02/12/2024] Open
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5
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Maulana A, Noviandy TR, Suhendra R, Earlia N, Bulqiah M, Idroes GM, Niode NJ, Sofyan H, Subianto M, Idroes R. Evaluation of atopic dermatitis severity using artificial intelligence. NARRA J 2023; 3:e511. [PMID: 38450339 PMCID: PMC10914065 DOI: 10.52225/narra.v3i3.511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 12/18/2023] [Indexed: 03/08/2024]
Abstract
Atopic dermatitis is a prevalent and persistent chronic inflammatory skin disorder that poses significant challenges when it comes to accurately assessing its severity. The aim of this study was to evaluate deep learning models for automated atopic dermatitis severity scoring using a dataset of Aceh ethnicity individuals in Indonesia. The dataset of clinical images was collected from 250 patients at Dr. Zainoel Abidin Hospital, Banda Aceh, Indonesia and labeled by dermatologists as mild, moderate, severe, or none. Five pretrained convolutional neural networks (CNN) architectures were evaluated: ResNet50, VGGNet19, MobileNetV3, MnasNet, and EfficientNetB0. The evaluation metrics, including accuracy, precision, sensitivity, specificity, and F1-score, were employed to assess the models. Among the models, ResNet50 emerged as the most proficient, demonstrating an accuracy of 89.8%, precision of 90.00%, sensitivity of 89.80%, specificity of 96.60%, and an F1-score of 89.85%. These results highlight the potential of incorporating advanced, data-driven models into the field of dermatology. These models can serve as invaluable tools to assist dermatologists in making early and precise assessments of atopic dermatitis severity and therefore improve patient care and outcomes.
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Affiliation(s)
- Aga Maulana
- Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia
| | - Teuku R Noviandy
- Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia
| | - Rivansyah Suhendra
- Department of Information Technology, Faculty of Engineering, Universitas Teuku Umar, Meulaboh, Indonesia
| | - Nanda Earlia
- Dermatology Division, Dr. Zainoel Abidin Hospital, Banda Aceh, Indonesia
- Department of Dermatology and Venereology, Faculty of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia
| | - Mikyal Bulqiah
- Dermatology Division, Dr. Zainoel Abidin Hospital, Banda Aceh, Indonesia
| | - Ghazi M Idroes
- Department of Occupational Health and Safety, Faculty of Health Sciences, Universitas Abulyatama, Aceh Besar, Indonesia
| | - Nurdjannah J Niode
- Department of Dermatology and Venereology, Faculty of Medicine, Sam Ratulangi University, Manado, Indonesia
| | - Hizir Sofyan
- Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia
| | - Muhammad Subianto
- Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia
| | - Rinaldi Idroes
- Department of Pharmacy, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia
- Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia
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6
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Shavlokhova V, Vollmer A, Zouboulis CC, Vollmer M, Wollborn J, Lang G, Kübler A, Hartmann S, Stoll C, Roider E, Saravi B. Finetuning of GLIDE stable diffusion model for AI-based text-conditional image synthesis of dermoscopic images. Front Med (Lausanne) 2023; 10:1231436. [PMID: 37928464 PMCID: PMC10623307 DOI: 10.3389/fmed.2023.1231436] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 10/09/2023] [Indexed: 11/07/2023] Open
Abstract
Background The development of artificial intelligence (AI)-based algorithms and advances in medical domains rely on large datasets. A recent advancement in text-to-image generative AI is GLIDE (Guided Language to Image Diffusion for Generation and Editing). There are a number of representations available in the GLIDE model, but it has not been refined for medical applications. Methods For text-conditional image synthesis with classifier-free guidance, we have fine-tuned GLIDE using 10,015 dermoscopic images of seven diagnostic entities, including melanoma and melanocytic nevi. Photorealistic synthetic samples of each diagnostic entity were created by the algorithm. Following this, an experienced dermatologist reviewed 140 images (20 of each entity), with 10 samples originating from artificial intelligence and 10 from original images from the dataset. The dermatologist classified the provided images according to the seven diagnostic entities. Additionally, the dermatologist was asked to indicate whether or not a particular image was created by AI. Further, we trained a deep learning model to compare the diagnostic results of dermatologist versus machine for entity classification. Results The results indicate that the generated images possess varying degrees of quality and realism, with melanocytic nevi and melanoma having higher similarity to real images than other classes. The integration of synthetic images improved the classification performance of the model, resulting in higher accuracy and precision. The AI assessment showed superior classification performance compared to dermatologist. Conclusion Overall, the results highlight the potential of synthetic images for training and improving AI models in dermatology to overcome data scarcity.
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Affiliation(s)
- Veronika Shavlokhova
- Maxillofacial Surgery University Hospital Ruppin-Fehrbelliner Straße Neuruppin, Neuruppin, Germany
| | - Andreas Vollmer
- Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, Würzburg, Germany
| | - Christos C. Zouboulis
- Departments of Dermatology, Venereology, Allergology and Immunology, Staedtisches Klinikum Dessau, Medical School Theodor Fontane and Faculty of Health Sciences Brandenburg, Dessau, Germany
| | - Michael Vollmer
- Department of Oral and Maxillofacial Surgery, Tuebingen University Hospital, Tuebingen, Germany
| | - Jakob Wollborn
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Gernot Lang
- Department of Orthopedics and Trauma Surgery, Medical Centre-Albert-Ludwigs-University of Freiburg, Faculty of Medicine, Albert-Ludwigs-University of Freiburg, Freiburg, Germany
| | - Alexander Kübler
- Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, Würzburg, Germany
| | - Stefan Hartmann
- Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, Würzburg, Germany
| | - Christian Stoll
- Maxillofacial Surgery University Hospital Ruppin-Fehrbelliner Straße Neuruppin, Neuruppin, Germany
| | - Elisabeth Roider
- Department of Dermatology, University Hospital of Basel, Basel, Switzerland
| | - Babak Saravi
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Department of Orthopedics and Trauma Surgery, Medical Centre-Albert-Ludwigs-University of Freiburg, Faculty of Medicine, Albert-Ludwigs-University of Freiburg, Freiburg, Germany
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7
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Debelee TG. Skin Lesion Classification and Detection Using Machine Learning Techniques: A Systematic Review. Diagnostics (Basel) 2023; 13:3147. [PMID: 37835889 PMCID: PMC10572538 DOI: 10.3390/diagnostics13193147] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 09/22/2023] [Accepted: 09/24/2023] [Indexed: 10/15/2023] Open
Abstract
Skin lesions are essential for the early detection and management of a number of dermatological disorders. Learning-based methods for skin lesion analysis have drawn much attention lately because of improvements in computer vision and machine learning techniques. A review of the most-recent methods for skin lesion classification, segmentation, and detection is presented in this survey paper. The significance of skin lesion analysis in healthcare and the difficulties of physical inspection are discussed in this survey paper. The review of state-of-the-art papers targeting skin lesion classification is then covered in depth with the goal of correctly identifying the type of skin lesion from dermoscopic, macroscopic, and other lesion image formats. The contribution and limitations of various techniques used in the selected study papers, including deep learning architectures and conventional machine learning methods, are examined. The survey then looks into study papers focused on skin lesion segmentation and detection techniques that aimed to identify the precise borders of skin lesions and classify them accordingly. These techniques make it easier to conduct subsequent analyses and allow for precise measurements and quantitative evaluations. The survey paper discusses well-known segmentation algorithms, including deep-learning-based, graph-based, and region-based ones. The difficulties, datasets, and evaluation metrics particular to skin lesion segmentation are also discussed. Throughout the survey, notable datasets, benchmark challenges, and evaluation metrics relevant to skin lesion analysis are highlighted, providing a comprehensive overview of the field. The paper concludes with a summary of the major trends, challenges, and potential future directions in skin lesion classification, segmentation, and detection, aiming to inspire further advancements in this critical domain of dermatological research.
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Affiliation(s)
- Taye Girma Debelee
- Ethiopian Artificial Intelligence Institute, Addis Ababa 40782, Ethiopia;
- Department of Electrical and Computer Engineering, Addis Ababa Science and Technology University, Addis Ababa 16417, Ethiopia
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8
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Derekas P, Spyridonos P, Likas A, Zampeta A, Gaitanis G, Bassukas I. The Promise of Semantic Segmentation in Detecting Actinic Keratosis Using Clinical Photography in the Wild. Cancers (Basel) 2023; 15:4861. [PMID: 37835555 PMCID: PMC10571759 DOI: 10.3390/cancers15194861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 10/01/2023] [Accepted: 10/02/2023] [Indexed: 10/15/2023] Open
Abstract
AK is a common precancerous skin condition that requires effective detection and treatment monitoring. To improve the monitoring of the AK burden in clinical settings with enhanced automation and precision, the present study evaluates the application of semantic segmentation based on the U-Net architecture (i.e., AKU-Net). AKU-Net employs transfer learning to compensate for the relatively small dataset of annotated images and integrates a recurrent process based on convLSTM to exploit contextual information and address the challenges related to the low contrast and ambiguous boundaries of AK-affected skin regions. We used an annotated dataset of 569 clinical photographs from 115 patients with actinic keratosis to train and evaluate the model. From each photograph, patches of 512 × 512 pixels were extracted using translation lesion boxes that encompassed lesions in different positions and captured different contexts of perilesional skin. In total, 16,488 translation-augmented crops were used for training the model, and 403 lesion center crops were used for testing. To demonstrate the improvements in AK detection, AKU-Net was compared with plain U-Net and U-Net++ architectures. The experimental results highlighted the effectiveness of AKU-Net, improving upon both automation and precision over existing approaches, paving the way for more effective and reliable evaluation of actinic keratosis in clinical settings.
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Affiliation(s)
- Panagiotis Derekas
- Department of Computer Science & Engineering, School of Engineering, University of Ioannina, 45110 Ioannina, Greece; (P.D.); (A.L.)
| | - Panagiota Spyridonos
- Department of Medical Physics, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
| | - Aristidis Likas
- Department of Computer Science & Engineering, School of Engineering, University of Ioannina, 45110 Ioannina, Greece; (P.D.); (A.L.)
| | - Athanasia Zampeta
- Department of Skin and Venereal Diseases, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece; (A.Z.); (G.G.); (I.B.)
| | - Georgios Gaitanis
- Department of Skin and Venereal Diseases, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece; (A.Z.); (G.G.); (I.B.)
| | - Ioannis Bassukas
- Department of Skin and Venereal Diseases, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece; (A.Z.); (G.G.); (I.B.)
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Choy SP, Kim BJ, Paolino A, Tan WR, Lim SML, Seo J, Tan SP, Francis L, Tsakok T, Simpson M, Barker JNWN, Lynch MD, Corbett MS, Smith CH, Mahil SK. Systematic review of deep learning image analyses for the diagnosis and monitoring of skin disease. NPJ Digit Med 2023; 6:180. [PMID: 37758829 PMCID: PMC10533565 DOI: 10.1038/s41746-023-00914-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 08/22/2023] [Indexed: 09/29/2023] Open
Abstract
Skin diseases affect one-third of the global population, posing a major healthcare burden. Deep learning may optimise healthcare workflows through processing skin images via neural networks to make predictions. A focus of deep learning research is skin lesion triage to detect cancer, but this may not translate to the wider scope of >2000 other skin diseases. We searched for studies applying deep learning to skin images, excluding benign/malignant lesions (1/1/2000-23/6/2022, PROSPERO CRD42022309935). The primary outcome was accuracy of deep learning algorithms in disease diagnosis or severity assessment. We modified QUADAS-2 for quality assessment. Of 13,857 references identified, 64 were included. The most studied diseases were acne, psoriasis, eczema, rosacea, vitiligo, urticaria. Deep learning algorithms had high specificity and variable sensitivity in diagnosing these conditions. Accuracy of algorithms in diagnosing acne (median 94%, IQR 86-98; n = 11), rosacea (94%, 90-97; n = 4), eczema (93%, 90-99; n = 9) and psoriasis (89%, 78-92; n = 8) was high. Accuracy for grading severity was highest for psoriasis (range 93-100%, n = 2), eczema (88%, n = 1), and acne (67-86%, n = 4). However, 59 (92%) studies had high risk-of-bias judgements and 62 (97%) had high-level applicability concerns. Only 12 (19%) reported participant ethnicity/skin type. Twenty-four (37.5%) evaluated the algorithm in an independent dataset, clinical setting or prospectively. These data indicate potential of deep learning image analysis in diagnosing and monitoring common skin diseases. Current research has important methodological/reporting limitations. Real-world, prospectively-acquired image datasets with external validation/testing will advance deep learning beyond the current experimental phase towards clinically-useful tools to mitigate rising health and cost impacts of skin disease.
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Affiliation(s)
- Shern Ping Choy
- St John's Institute of Dermatology, Guy's and St Thomas' NHS Foundation Trust and King's College London, London, UK
| | - Byung Jin Kim
- St George's University Hospitals NHS Foundation Trust, London, UK
| | - Alexandra Paolino
- St John's Institute of Dermatology, Guy's and St Thomas' NHS Foundation Trust and King's College London, London, UK
| | - Wei Ren Tan
- St John's Institute of Dermatology, Guy's and St Thomas' NHS Foundation Trust and King's College London, London, UK
| | | | | | - Sze Ping Tan
- Barking, Havering and Redbridge University Hospitals NHS Trust, London, UK
| | - Luc Francis
- St John's Institute of Dermatology, Guy's and St Thomas' NHS Foundation Trust and King's College London, London, UK
| | - Teresa Tsakok
- St John's Institute of Dermatology, Guy's and St Thomas' NHS Foundation Trust and King's College London, London, UK
| | - Michael Simpson
- Department of Medical and Molecular Genetics, King's College London, London, UK
| | - Jonathan N W N Barker
- St John's Institute of Dermatology, Guy's and St Thomas' NHS Foundation Trust and King's College London, London, UK
| | - Magnus D Lynch
- St John's Institute of Dermatology, Guy's and St Thomas' NHS Foundation Trust and King's College London, London, UK
| | - Mark S Corbett
- Center for Reviews and Dissemination, University of York, York, UK
| | - Catherine H Smith
- St John's Institute of Dermatology, Guy's and St Thomas' NHS Foundation Trust and King's College London, London, UK
| | - Satveer K Mahil
- St John's Institute of Dermatology, Guy's and St Thomas' NHS Foundation Trust and King's College London, London, UK.
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Ghaffar A, Xie Y, Antinozzi P, Ryan Wolf J. RISREAC Study: Assessment of Cutaneous Radiation Injury Through Clinical Documentation. Disaster Med Public Health Prep 2023; 17:e486. [PMID: 37680193 DOI: 10.1017/dmp.2023.156] [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] [Indexed: 09/09/2023]
Abstract
OBJECTIVE Radiation dermatitis (RD) occurs in 95% of patients receiving radiation therapy (RT) for cancer treatment, affecting 800 million patients annually. We aimed to demonstrate the feasibility of developing a historical RD cohort, Radiation Induced Skin Reactions (RISREAC) cohort. METHODS This retrospective study evaluated RD-related clinical documentation for 245 breast cancer patients who received RT at the University of Rochester Medical Center, to understand the RD progression, scoring, and management. All statistical analyses were performed at 0.05 level of significance. RESULTS Clinician-documented RD severity was observed for 169 (69%) patients with a mean severity of 1.57 [1.46, 1.68]. The mean descriptor-based severity score of 2.31 [2.18, 2.45] moderately correlated (r = 0.532, P < 0.0001) with documented RD grade. Most patients (91.8%) received skin care treatment during RT, with 66.7% receiving more than 2 modalities. CONCLUSIONS The RISREAC cohort is the first retrospective cohort established from clinical documentation of radiation-induced skin changes for the study of RD and cutaneous radiation injury (CRI). RD symptom descriptors were more reliably documented and suitable for all skin types compared to Radiation Therapy Oncology Group (RTOG) or Common Toxicity Criteria for Adverse Events (CTCAE) grades. A new descriptor-based scoring tool would be useful for RD and CRI.
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Affiliation(s)
- Aqsa Ghaffar
- School of Medicine & Dentistry, University of Rochester Medical Center, Rochester, NY, USA
| | - Yunna Xie
- Public Health Sciences, University of Rochester Medical Center, Rochester, NY, USA
| | | | - Julie Ryan Wolf
- Department of Dermatology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY, USA
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11
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Diaz-Ramón JL, Gardeazabal J, Izu RM, Garrote E, Rasero J, Apraiz A, Penas C, Seijo S, Lopez-Saratxaga C, De la Peña PM, Sanchez-Diaz A, Cancho-Galan G, Velasco V, Sevilla A, Fernandez D, Cuenca I, Cortes JM, Alonso S, Asumendi A, Boyano MD. Melanoma Clinical Decision Support System: An Artificial Intelligence-Based Tool to Diagnose and Predict Disease Outcome in Early-Stage Melanoma Patients. Cancers (Basel) 2023; 15:cancers15072174. [PMID: 37046835 PMCID: PMC10093614 DOI: 10.3390/cancers15072174] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 03/17/2023] [Accepted: 04/03/2023] [Indexed: 04/08/2023] Open
Abstract
This study set out to assess the performance of an artificial intelligence (AI) algorithm based on clinical data and dermatoscopic imaging for the early diagnosis of melanoma, and its capacity to define the metastatic progression of melanoma through serological and histopathological biomarkers, enabling dermatologists to make more informed decisions about patient management. Integrated analysis of demographic data, images of the skin lesions, and serum and histopathological markers were analyzed in a group of 196 patients with melanoma. The interleukins (ILs) IL-4, IL-6, IL-10, and IL-17A as well as IFNγ (interferon), GM-CSF (granulocyte and macrophage colony-stimulating factor), TGFβ (transforming growth factor), and the protein DCD (dermcidin) were quantified in the serum of melanoma patients at the time of diagnosis, and the expression of the RKIP, PIRIN, BCL2, BCL3, MITF, and ANXA5 proteins was detected by immunohistochemistry (IHC) in melanoma biopsies. An AI algorithm was used to improve the early diagnosis of melanoma and to predict the risk of metastasis and of disease-free survival. Two models were obtained to predict metastasis (including “all patients” or only patients “at early stages of melanoma”), and a series of attributes were seen to predict the progression of metastasis: Breslow thickness, infiltrating BCL-2 expressing lymphocytes, and IL-4 and IL-6 serum levels. Importantly, a decrease in serum GM-CSF seems to be a marker of poor prognosis in patients with early-stage melanomas.
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Affiliation(s)
- Jose Luis Diaz-Ramón
- Dermatology Service, Cruces University Hospital, 48903 Barakaldo, Spain
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
| | - Jesus Gardeazabal
- Dermatology Service, Cruces University Hospital, 48903 Barakaldo, Spain
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
| | - Rosa Maria Izu
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- Dermatology Service, Basurto University Hospital, 48013 Bilbao, Spain
| | - Estibaliz Garrote
- TECNALIA, Basque Research and Technology Alliance (BRTA), 20850 Gipuzkoa, Spain
- Department of Cell Biology and Histology, University of the Basque Country/EHU, 48940 Leioa, Spain
| | - Javier Rasero
- Department of Psychology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Aintzane Apraiz
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- Department of Cell Biology and Histology, University of the Basque Country/EHU, 48940 Leioa, Spain
| | - Cristina Penas
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- Department of Cell Biology and Histology, University of the Basque Country/EHU, 48940 Leioa, Spain
| | - Sandra Seijo
- Ibermática Innovation Institute, 48170 Zamudio, Spain
| | | | | | - Ana Sanchez-Diaz
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- Dermatology Service, Basurto University Hospital, 48013 Bilbao, Spain
| | - Goikoane Cancho-Galan
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- Pathology Service, Basurto University Hospital, 48013 Bilbao, Spain
| | - Veronica Velasco
- Dermatology Service, Cruces University Hospital, 48903 Barakaldo, Spain
- Pathology Service, Cruces University Hospital, 48903 Barakaldo, Spain
| | - Arrate Sevilla
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country/EHU, 48940 Leioa, Spain
| | | | - Iciar Cuenca
- Ibermática Innovation Institute, 48170 Zamudio, Spain
| | - Jesus María Cortes
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- Department of Cell Biology and Histology, University of the Basque Country/EHU, 48940 Leioa, Spain
- IKERBASQUE, The Basque Foundation for Science, 48009 Bilbao, Spain
| | - Santos Alonso
- Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country/EHU, 48940 Leioa, Spain
| | - Aintzane Asumendi
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- Department of Cell Biology and Histology, University of the Basque Country/EHU, 48940 Leioa, Spain
| | - María Dolores Boyano
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- Department of Cell Biology and Histology, University of the Basque Country/EHU, 48940 Leioa, Spain
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Künstliche Intelligenz in der Therapie chronischer Wunden – Konzepte und Ausblick. GEFÄSSCHIRURGIE 2023. [DOI: 10.1007/s00772-022-00964-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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