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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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Courtenay LA, Barbero-García I, Martínez-Lastras S, Del Pozo S, Corral M, González-Aguilera D. Using computational learning for non-melanoma skin cancer and actinic keratosis near-infrared hyperspectral signature classification. Photodiagnosis Photodyn Ther 2024; 49:104269. [PMID: 39002835 DOI: 10.1016/j.pdpdt.2024.104269] [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: 06/07/2024] [Revised: 06/29/2024] [Accepted: 07/08/2024] [Indexed: 07/15/2024]
Abstract
BACKGROUND The early detection of Non-Melanoma Skin Cancer (NMSC) is essential to ensure patients receive the most effective treatment. Diagnostic screening tools for NMSC are crucial due to high confusion rates with other types of skin lesions, such as Actinic Keratosis. Nevertheless, current means of diagnosing and screening patients rely on either visual criteria, that are often conditioned by subjectivity and experience, or highly invasive, slow, and costly methods, such as histological diagnoses. From this, the objectives of the present study are to test if classification accuracies improve in the Near-Infrared region of the electromagnetic spectrum, as opposed to previous research in shorter wavelengths. METHODS This study utilizes near-infrared hyperspectral imaging, within the range of 900.6 and 1454.8 nm. Images were captured for a total of 125 patients, including 66 patients with Basal Cell Carcinoma, 42 with cutaneous Squamous Cell Carcinoma, and 17 with Actinic Keratosis, to differentiate between healthy and unhealthy skin lesions. A combination of hybrid convolutional neural networks (for feature extraction) and support vector machine algorithms (as a final activation layer) was employed for analysis. In addition, we test whether transfer learning is feasible from networks trained on shorter wavelengths of the electromagnetic spectrum. RESULTS The implemented method achieved a general accuracy of over 80 %, with some tasks reaching over 90 %. F1 scores were also found to generally be over the optimal threshold of 0.8. The best results were obtained when detecting Actinic Keratosis, however differentiation between the two types of malignant lesions was often noted to be more difficult. These results demonstrate the potential of near-infrared hyperspectral imaging combined with advanced machine learning techniques in distinguishing NMSC from other skin lesions. Transfer learning was unsuccessful in improving the training of these algorithms. CONCLUSIONS We have shown that the Near-Infrared region of the electromagnetic spectrum is highly useful for the identification and study of non-melanoma type skin lesions. While the results are promising, further research is required to develop more robust algorithms that can minimize the impact of noise in these datasets before clinical application is feasible.
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Affiliation(s)
- Lloyd A Courtenay
- CNRS, PACEA UMR 5199, Université de Bordeaux, Bât B2, Allée Geoffroy Saint Hilaire, CS50023, Pessac, 33600, France.
| | - Inés Barbero-García
- Department of Cartographic and Terrain Engineering, Higher Polytechnic School of Ávila, Universidad de Salamanca, Calle Hornos Caleros 50, 05003 Ávila, Spain
| | - Saray Martínez-Lastras
- Department of Cartographic and Terrain Engineering, Higher Polytechnic School of Ávila, Universidad de Salamanca, Calle Hornos Caleros 50, 05003 Ávila, Spain
| | - Susana Del Pozo
- Department of Cartographic and Terrain Engineering, Higher Polytechnic School of Ávila, Universidad de Salamanca, Calle Hornos Caleros 50, 05003 Ávila, Spain
| | - Miriam Corral
- Dermatology Service, Ávila Healthcare Complex, Calle Jesús del Gran Poder 42, 05003, Ávila, Spain
| | - Diego González-Aguilera
- Department of Cartographic and Terrain Engineering, Higher Polytechnic School of Ávila, Universidad de Salamanca, Calle Hornos Caleros 50, 05003 Ávila, Spain
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Edalati S, Vasan V, Cheng CP, Patel Z, Govindaraj S, Iloreta AM. Can GPT-4 revolutionize otolaryngology? Navigating opportunities and ethical considerations. Am J Otolaryngol 2024; 45:104303. [PMID: 38678799 DOI: 10.1016/j.amjoto.2024.104303] [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: 04/10/2024] [Accepted: 04/14/2024] [Indexed: 05/01/2024]
Abstract
Otolaryngologists can enhance workflow efficiency, provide better patient care, and advance medical research and education by integrating artificial intelligence (AI) into their practices. GPT-4 technology is a revolutionary and contemporary example of AI that may apply to otolaryngology. The knowledge of otolaryngologists should be supplemented, not replaced when using GPT-4 to make critical medical decisions and provide individualized patient care. In our thorough examination, we explore the potential uses of the groundbreaking GPT-4 technology in the field of otolaryngology, covering aspects such as potential outcomes and technical boundaries. Additionally, we delve into the intricate and intellectually challenging dilemmas that emerge when incorporating GPT-4 into otolaryngology, considering the ethical considerations inherent in its implementation. Our stance is that GPT-4 has the potential to be very helpful. Its capabilities, which include aid in clinical decision-making, patient care, and administrative job automation, present exciting possibilities for enhancing patient outcomes, boosting the efficiency of healthcare delivery, and enhancing patient experiences. Even though there are still certain obstacles and limitations, the progress made so far shows that GPT-4 can be a valuable tool for modern medicine. GPT-4 may play a more significant role in clinical practice as technology develops, helping medical professionals deliver high-quality care tailored to every patient's unique needs.
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Affiliation(s)
- Shaun Edalati
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Vikram Vasan
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Christopher P Cheng
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Zara Patel
- Department of Otolaryngology-Head & Neck Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Satish Govindaraj
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alfred Marc Iloreta
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Trager MH, Gordon ER, Breneman A, Weng C, Samie FH. Artificial intelligence for nonmelanoma skin cancer. Clin Dermatol 2024:S0738-081X(24)00100-7. [PMID: 38925444 DOI: 10.1016/j.clindermatol.2024.06.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2024]
Abstract
Nonmelanoma skin cancers (NMSCs) are among the top five most common cancers globally. NMSC is an area with great potential for novel application of diagnostic tools including artificial intelligence (AI). In this scoping review, we aimed to describe the applications of AI in the diagnosis and treatment of NMSC. Twenty-nine publications described AI applications to dermatopathology including lesion classification and margin assessment. Twenty-five publications discussed AI use in clinical image analysis, showing that algorithms are not superior to dermatologists and may rely on unbalanced, nonrepresentative, and nontransparent training data sets. Sixteen publications described the use of AI in cutaneous surgery for NMSC including use in margin assessment during excisions and Mohs surgery, as well as predicting procedural complexity. Eleven publications discussed spectroscopy, confocal microscopy, thermography, and the AI algorithms that analyze and interpret their data. Ten publications pertained to AI applications for the discovery and use of NMSC biomarkers. Eight publications discussed the use of smartphones and AI, specifically how they enable clinicians and patients to have increased access to instant dermatologic assessments but with varying accuracies. Five publications discussed large language models and NMSC, including how they may facilitate or hinder patient education and medical decision-making. Three publications pertaining to the skin of color and AI for NMSC discussed concerns regarding limited diverse data sets for the training of convolutional neural networks. AI demonstrates tremendous potential to improve diagnosis, patient and clinician education, and management of NMSC. Despite excitement regarding AI, data sets are often not transparently reported, may include low-quality images, and may not include diverse skin types, limiting generalizability. AI may serve as a tool to increase access to dermatology services for patients in rural areas and save health care dollars. These benefits can only be achieved, however, with consideration of potential ethical costs.
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Affiliation(s)
- Megan H Trager
- Department of Dermatology, Columbia University Irving Medical Center, New York, NY, USA
| | - Emily R Gordon
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Alyssa Breneman
- Department of Dermatology, Columbia University Irving Medical Center, New York, NY, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Faramarz H Samie
- Department of Dermatology, Columbia University Irving Medical Center, New York, NY, USA.
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Riley BK, Dixon A. Emotional and cognitive trust in artificial intelligence: A framework for identifying research opportunities. Curr Opin Psychol 2024; 58:101833. [PMID: 38991423 DOI: 10.1016/j.copsyc.2024.101833] [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: 05/03/2024] [Revised: 06/18/2024] [Accepted: 06/20/2024] [Indexed: 07/13/2024]
Abstract
This article briefly summarizes trust as a multi-dimensional construct, and trust in AI as a unique but related construct. It argues that because trust in AI is couched within an economic landscape, these two frameworks should be combined to understand the dynamics of trust in AI as it is currently implemented. The review focuses on healthcare and law enforcement as two industries that have adopted AI in ways that do and do not engender trust from stakeholders. The framework is applied to both industries to highlight where and why varying trust in AI is observed. Then seven research questions are posed, and researchers are encouraged to test the proposed framework in other AI-reliant contexts, like education and employment.
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Slominski RM, Kim TK, Janjetovic Z, Brożyna AA, Podgorska E, Dixon KM, Mason RS, Tuckey RC, Sharma R, Crossman DK, Elmets C, Raman C, Jetten AM, Indra AK, Slominski AT. Malignant Melanoma: An Overview, New Perspectives, and Vitamin D Signaling. Cancers (Basel) 2024; 16:2262. [PMID: 38927967 PMCID: PMC11201527 DOI: 10.3390/cancers16122262] [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: 05/04/2024] [Revised: 06/09/2024] [Accepted: 06/14/2024] [Indexed: 06/28/2024] Open
Abstract
Melanoma, originating through malignant transformation of melanin-producing melanocytes, is a formidable malignancy, characterized by local invasiveness, recurrence, early metastasis, resistance to therapy, and a high mortality rate. This review discusses etiologic and risk factors for melanoma, diagnostic and prognostic tools, including recent advances in molecular biology, omics, and bioinformatics, and provides an overview of its therapy. Since the incidence of melanoma is rising and mortality remains unacceptably high, we discuss its inherent properties, including melanogenesis, that make this disease resilient to treatment and propose to use AI to solve the above complex and multidimensional problems. We provide an overview on vitamin D and its anticancerogenic properties, and report recent advances in this field that can provide solutions for the prevention and/or therapy of melanoma. Experimental papers and clinicopathological studies on the role of vitamin D status and signaling pathways initiated by its active metabolites in melanoma prognosis and therapy are reviewed. We conclude that vitamin D signaling, defined by specific nuclear receptors and selective activation by specific vitamin D hydroxyderivatives, can provide a benefit for new or existing therapeutic approaches. We propose to target vitamin D signaling with the use of computational biology and AI tools to provide a solution to the melanoma problem.
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Affiliation(s)
- Radomir M. Slominski
- Department of Rheumatology and Clinical Immunology, Department of Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA;
| | - Tae-Kang Kim
- Department of Dermatology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA; (T.-K.K.); (Z.J.); (E.P.); (C.E.); (C.R.)
| | - Zorica Janjetovic
- Department of Dermatology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA; (T.-K.K.); (Z.J.); (E.P.); (C.E.); (C.R.)
| | - Anna A. Brożyna
- Department of Human Biology, Institute of Biology, Faculty of Biological and Veterinary Sciences, Nicolaus Copernicus University, 87-100 Torun, Poland;
| | - Ewa Podgorska
- Department of Dermatology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA; (T.-K.K.); (Z.J.); (E.P.); (C.E.); (C.R.)
| | - Katie M. Dixon
- School of Medical Sciences, The University of Sydney, Sydney, NSW 2050, Australia; (K.M.D.); (R.S.M.)
| | - Rebecca S. Mason
- School of Medical Sciences, The University of Sydney, Sydney, NSW 2050, Australia; (K.M.D.); (R.S.M.)
| | - Robert C. Tuckey
- School of Molecular Sciences, University of Western Australia, Perth, WA 6009, Australia;
| | - Rahul Sharma
- Department of Biomedical Informatics and Data Science, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA;
| | - David K. Crossman
- Department of Genetics and Bioinformatics, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA;
| | - Craig Elmets
- Department of Dermatology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA; (T.-K.K.); (Z.J.); (E.P.); (C.E.); (C.R.)
| | - Chander Raman
- Department of Dermatology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA; (T.-K.K.); (Z.J.); (E.P.); (C.E.); (C.R.)
| | - Anton M. Jetten
- Cell Biology Section, NIEHS—National Institutes of Health, Research Triangle Park, NC 27709, USA;
| | - Arup K. Indra
- Department of Pharmaceutical Sciences, College of Pharmacy, Oregon State University, Corvallis, OR 97331, USA
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97239, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Andrzej T. Slominski
- Department of Dermatology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA; (T.-K.K.); (Z.J.); (E.P.); (C.E.); (C.R.)
- Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL 35294, USA
- Pathology and Laboratory Medicine Service, Veteran Administration Medical Center, Birmingham, AL 35233, USA
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Schreidah CM, Gordon ER, Adeuyan O, Chen C, Lapolla BA, Kent JA, Reynolds GB, Fahmy LM, Weng C, Tatonetti NP, Chase HS, Pe’er I, Geskin LJ. Current status of artificial intelligence methods for skin cancer survival analysis: a scoping review. Front Med (Lausanne) 2024; 11:1243659. [PMID: 38711781 PMCID: PMC11070520 DOI: 10.3389/fmed.2024.1243659] [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: 06/21/2023] [Accepted: 02/22/2024] [Indexed: 05/08/2024] Open
Abstract
Skin cancer mortality rates continue to rise, and survival analysis is increasingly needed to understand who is at risk and what interventions improve outcomes. However, current statistical methods are limited by inability to synthesize multiple data types, such as patient genetics, clinical history, demographics, and pathology and reveal significant multimodal relationships through predictive algorithms. Advances in computing power and data science enabled the rise of artificial intelligence (AI), which synthesizes vast amounts of data and applies algorithms that enable personalized diagnostic approaches. Here, we analyze AI methods used in skin cancer survival analysis, focusing on supervised learning, unsupervised learning, deep learning, and natural language processing. We illustrate strengths and weaknesses of these approaches with examples. Our PubMed search yielded 14 publications meeting inclusion criteria for this scoping review. Most publications focused on melanoma, particularly histopathologic interpretation with deep learning. Such concentration on a single type of skin cancer amid increasing focus on deep learning highlight growing areas for innovation; however, it also demonstrates opportunity for additional analysis that addresses other types of cutaneous malignancies and expands the scope of prognostication to combine both genetic, histopathologic, and clinical data. Moreover, researchers may leverage multiple AI methods for enhanced benefit in analyses. Expanding AI to this arena may enable improved survival analysis, targeted treatments, and outcomes.
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Affiliation(s)
- Celine M. Schreidah
- Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States
| | - Emily R. Gordon
- Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States
| | - Oluwaseyi Adeuyan
- Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States
| | - Caroline Chen
- Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States
| | - Brigit A. Lapolla
- Department of Dermatology, Columbia University Irving Medical Center, New York, NY, United States
| | - Joshua A. Kent
- Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, United States
| | | | - Lauren M. Fahmy
- Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States
| | - Chunhua Weng
- The Data Science Institute, Columbia University, New York, NY, United States
- Department of Biomedical Informatics, Columbia University, New York, NY, United States
| | - Nicholas P. Tatonetti
- The Data Science Institute, Columbia University, New York, NY, United States
- Department of Biomedical Informatics, Columbia University, New York, NY, United States
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
- Cedars-Sinai Cancer, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Herbert S. Chase
- Department of Biomedical Informatics, Columbia University, New York, NY, United States
| | - Itsik Pe’er
- The Data Science Institute, Columbia University, New York, NY, United States
- Department of Systems Biology, Columbia University, New York, NY, United States
- Department of Computer Science, Columbia University, New York, NY, United States
| | - Larisa J. Geskin
- Department of Dermatology, Columbia University Irving Medical Center, New York, NY, United States
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Primiero CA, Betz-Stablein B, Ascott N, D’Alessandro B, Gaborit S, Fricker P, Goldsteen A, González-Villà S, Lee K, Nazari S, Nguyen H, Ntouskos V, Pahde F, Pataki BE, Quintana J, Puig S, Rezze GG, Garcia R, Soyer HP, Malvehy J. A protocol for annotation of total body photography for machine learning to analyze skin phenotype and lesion classification. Front Med (Lausanne) 2024; 11:1380984. [PMID: 38654834 PMCID: PMC11035726 DOI: 10.3389/fmed.2024.1380984] [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: 02/02/2024] [Accepted: 03/25/2024] [Indexed: 04/26/2024] Open
Abstract
Introduction Artificial Intelligence (AI) has proven effective in classifying skin cancers using dermoscopy images. In experimental settings, algorithms have outperformed expert dermatologists in classifying melanoma and keratinocyte cancers. However, clinical application is limited when algorithms are presented with 'untrained' or out-of-distribution lesion categories, often misclassifying benign lesions as malignant, or misclassifying malignant lesions as benign. Another limitation often raised is the lack of clinical context (e.g., medical history) used as input for the AI decision process. The increasing use of Total Body Photography (TBP) in clinical examinations presents new opportunities for AI to perform holistic analysis of the whole patient, rather than a single lesion. Currently there is a lack of existing literature or standards for image annotation of TBP, or on preserving patient privacy during the machine learning process. Methods This protocol describes the methods for the acquisition of patient data, including TBP, medical history, and genetic risk factors, to create a comprehensive dataset for machine learning. 500 patients of various risk profiles will be recruited from two clinical sites (Australia and Spain), to undergo temporal total body imaging, complete surveys on sun behaviors and medical history, and provide a DNA sample. This patient-level metadata is applied to image datasets using DICOM labels. Anonymization and masking methods are applied to preserve patient privacy. A two-step annotation process is followed to label skin images for lesion detection and classification using deep learning models. Skin phenotype characteristics are extracted from images, including innate and facultative skin color, nevi distribution, and UV damage. Several algorithms will be developed relating to skin lesion detection, segmentation and classification, 3D mapping, change detection, and risk profiling. Simultaneously, explainable AI (XAI) methods will be incorporated to foster clinician and patient trust. Additionally, a publicly released dataset of anonymized annotated TBP images will be released for an international challenge to advance the development of new algorithms using this type of data. Conclusion The anticipated results from this protocol are validated AI-based tools to provide holistic risk assessment for individual lesions, and risk stratification of patients to assist clinicians in monitoring for skin cancer.
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Affiliation(s)
- Clare A. Primiero
- Dermatology Department, Hospital Clinic and Fundació Clínic per la Recerca Biomèdica—IDIBAPS, Barcelona, Spain
- Frazer Institute, The University of Queensland, Dermatology Research Centre, Brisbane, QLD, Australia
| | - Brigid Betz-Stablein
- Frazer Institute, The University of Queensland, Dermatology Research Centre, Brisbane, QLD, Australia
| | | | | | | | - Paul Fricker
- Torus Actions & Belle.ai, Ramonville-Saint-Agne, France
| | | | | | - Katie Lee
- Frazer Institute, The University of Queensland, Dermatology Research Centre, Brisbane, QLD, Australia
| | - Sana Nazari
- Computer Vision and Robotics Group, University of Girona, Girona, Spain
| | - Hang Nguyen
- Torus Actions & Belle.ai, Ramonville-Saint-Agne, France
| | - Valsamis Ntouskos
- Remote Sensing Lab, National Technical University of Athens, Athens, Greece
| | | | - Balázs E. Pataki
- HUN-REN Institute for Computer Science and Control, Budapest, Hungary
| | | | - Susana Puig
- Dermatology Department, Hospital Clinic and Fundació Clínic per la Recerca Biomèdica—IDIBAPS, Barcelona, Spain
- Medicine Department, University of Barcelona, Barcelona, Spain
- CIBER de Enfermedades raras, Instituto de Salud Carlos III, Barcelona, Spain
| | - Gisele G. Rezze
- Dermatology Department, Hospital Clinic and Fundació Clínic per la Recerca Biomèdica—IDIBAPS, Barcelona, Spain
| | - Rafael Garcia
- Computer Vision and Robotics Group, University of Girona, Girona, Spain
| | - H. Peter Soyer
- Frazer Institute, The University of Queensland, Dermatology Research Centre, Brisbane, QLD, Australia
- Dermatology Department, Princess Alexandra Hospital, Brisbane, QLD, Australia
| | - Josep Malvehy
- Dermatology Department, Hospital Clinic and Fundació Clínic per la Recerca Biomèdica—IDIBAPS, Barcelona, Spain
- Medicine Department, University of Barcelona, Barcelona, Spain
- CIBER de Enfermedades raras, Instituto de Salud Carlos III, Barcelona, Spain
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Miller I, Rosic N, Stapelberg M, Hudson J, Coxon P, Furness J, Walsh J, Climstein M. Performance of Commercial Dermatoscopic Systems That Incorporate Artificial Intelligence for the Identification of Melanoma in General Practice: A Systematic Review. Cancers (Basel) 2024; 16:1443. [PMID: 38611119 PMCID: PMC11011068 DOI: 10.3390/cancers16071443] [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: 02/29/2024] [Revised: 04/03/2024] [Accepted: 04/04/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND Cutaneous melanoma remains an increasing global public health burden, particularly in fair-skinned populations. Advancing technologies, particularly artificial intelligence (AI), may provide an additional tool for clinicians to help detect malignancies with a more accurate success rate. This systematic review aimed to report the performance metrics of commercially available convolutional neural networks (CNNs) tasked with detecting MM. METHODS A systematic literature search was performed using CINAHL, Medline, Scopus, ScienceDirect and Web of Science databases. RESULTS A total of 16 articles reporting MM were included in this review. The combined number of melanomas detected was 1160, and non-melanoma lesions were 33,010. The performance of market-approved technology and clinician performance for classifying melanoma was highly heterogeneous, with sensitivity ranging from 16.4 to 100.0%, specificity between 40.0 and 98.3% and accuracy between 44.0 and 92.0%. Less heterogeneity was observed when clinicians worked in unison with AI, with sensitivity ranging between 83.3 and 100.0%, specificity between 83.7 and 87.3%, and accuracy between 86.4 and 86.9%. CONCLUSION Instead of focusing on the performance of AI versus clinicians for classifying melanoma, more consistent performance has been obtained when clinicians' work is supported by AI, facilitating management decisions and improving health outcomes.
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Affiliation(s)
- Ian Miller
- Aquatic Based Research, Southern Cross University, Bilinga, QLD 4225, Australia; (I.M.); (N.R.)
- Faculty of Health, Southern Cross University, Bilinga, QLD 4225, Australia (P.C.)
- Specialist Suite, John Flynn Hospital, Tugun, QLD 4224, Australia
| | - Nedeljka Rosic
- Aquatic Based Research, Southern Cross University, Bilinga, QLD 4225, Australia; (I.M.); (N.R.)
- Faculty of Health, Southern Cross University, Bilinga, QLD 4225, Australia (P.C.)
| | - Michael Stapelberg
- Aquatic Based Research, Southern Cross University, Bilinga, QLD 4225, Australia; (I.M.); (N.R.)
- Faculty of Health, Southern Cross University, Bilinga, QLD 4225, Australia (P.C.)
- Specialist Suite, John Flynn Hospital, Tugun, QLD 4224, Australia
| | - Jeremy Hudson
- Faculty of Health, Southern Cross University, Bilinga, QLD 4225, Australia (P.C.)
- North Queensland Skin Centre, Townsville, QLD 4810, Australia
| | - Paul Coxon
- Faculty of Health, Southern Cross University, Bilinga, QLD 4225, Australia (P.C.)
- North Queensland Skin Centre, Townsville, QLD 4810, Australia
| | - James Furness
- Water Based Research Unit, Bond University, Robina, QLD 4226, Australia;
| | - Joe Walsh
- Sport Science Institute, Sydney, NSW 2000, Australia;
- AI Consulting Group, Sydney, NSW 2000, Australia
| | - Mike Climstein
- Aquatic Based Research, Southern Cross University, Bilinga, QLD 4225, Australia; (I.M.); (N.R.)
- Faculty of Health, Southern Cross University, Bilinga, QLD 4225, Australia (P.C.)
- Physical Activity, Lifestyle, Ageing and Wellbeing Faculty Research Group, University of Sydney, Sydney, NSW 2050, Australia
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10
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Ijaz MF, Woźniak M. Editorial: Recent Advances in Deep Learning and Medical Imaging for Cancer Treatment. Cancers (Basel) 2024; 16:700. [PMID: 38398091 PMCID: PMC10887279 DOI: 10.3390/cancers16040700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024] Open
Abstract
In the evolving landscape of medical imaging, the escalating need for deep-learningmethods takes center stage, offering the capability to autonomously acquire abstract datarepresentations crucial for early detection and classification for cancer treatment. Thecomplexities in handling diverse inputs, high-dimensional features, and subtle patternswithin imaging data are acknowledged as significant challenges in this technologicalpursuit. This Special Issue, "Recent Advances in Deep Learning and Medical Imagingfor Cancer Treatment", has attracted 19 high-quality articles that cover state-of-the-artapplications and technical developments of deep learning, medical imaging, automaticdetection, and classification, explainable artificial intelligence-enabled diagnosis for cancertreatment. In the ever-evolving landscape of cancer treatment, five pivotal themes haveemerged as beacons of transformative change. This editorial delves into the realms ofinnovation that are shaping the future of cancer treatment, focusing on five interconnectedthemes: use of artificial intelligence in medical imaging, applications of AI in cancerdiagnosis and treatment, addressing challenges in medical image analysis, advancementsin cancer detection techniques, and innovations in skin cancer classification.
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Affiliation(s)
- Muhammad Fazal Ijaz
- School of Engineering and Information Technology, Melbourne Institute of Technology, Melbourne 3000, Australia
| | - Marcin Woźniak
- Faculty of Applied Mathematics, Silesian University of Technology, Kaszubska 23, 44100 Gliwice, Poland
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11
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Behara K, Bhero E, Agee JT. Grid-Based Structural and Dimensional Skin Cancer Classification with Self-Featured Optimized Explainable Deep Convolutional Neural Networks. Int J Mol Sci 2024; 25:1546. [PMID: 38338828 PMCID: PMC10855492 DOI: 10.3390/ijms25031546] [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: 12/20/2023] [Revised: 01/16/2024] [Accepted: 01/24/2024] [Indexed: 02/12/2024] Open
Abstract
Skin cancer is a severe and potentially lethal disease, and early detection is critical for successful treatment. Traditional procedures for diagnosing skin cancer are expensive, time-intensive, and necessitate the expertise of a medical practitioner. In recent years, many researchers have developed artificial intelligence (AI) tools, including shallow and deep machine learning-based approaches, to diagnose skin cancer. However, AI-based skin cancer diagnosis faces challenges in complexity, low reproducibility, and explainability. To address these problems, we propose a novel Grid-Based Structural and Dimensional Explainable Deep Convolutional Neural Network for accurate and interpretable skin cancer classification. This model employs adaptive thresholding for extracting the region of interest (ROI), using its dynamic capabilities to enhance the accuracy of identifying cancerous regions. The VGG-16 architecture extracts the hierarchical characteristics of skin lesion images, leveraging its recognized capabilities for deep feature extraction. Our proposed model leverages a grid structure to capture spatial relationships within lesions, while the dimensional features extract relevant information from various image channels. An Adaptive Intelligent Coney Optimization (AICO) algorithm is employed for self-feature selected optimization and fine-tuning the hyperparameters, which dynamically adapts the model architecture to optimize feature extraction and classification. The model was trained and tested using the ISIC dataset of 10,015 dermascope images and the MNIST dataset of 2357 images of malignant and benign oncological diseases. The experimental results demonstrated that the model achieved accuracy and CSI values of 0.96 and 0.97 for TP 80 using the ISIC dataset, which is 17.70% and 16.49% more than lightweight CNN, 20.83% and 19.59% more than DenseNet, 18.75% and 17.53% more than CNN, 6.25% and 6.18% more than Efficient Net-B0, 5.21% and 5.15% over ECNN, 2.08% and 2.06% over COA-CAN, and 5.21% and 5.15% more than ARO-ECNN. Additionally, the AICO self-feature selected ECNN model exhibited minimal FPR and FNR of 0.03 and 0.02, respectively. The model attained a loss of 0.09 for ISIC and 0.18 for the MNIST dataset, indicating that the model proposed in this research outperforms existing techniques. The proposed model improves accuracy, interpretability, and robustness for skin cancer classification, ultimately aiding clinicians in early diagnosis and treatment.
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Affiliation(s)
- Kavita Behara
- Department of Electrical Engineering, Mangosuthu University of Technology, Durban 4031, South Africa;
| | - Ernest Bhero
- Discipline of Electrical, Electronic and Computer Engineering, University of KwaZulu Natal, Durban 4041, South Africa;
| | - John Terhile Agee
- Discipline of Electrical, Electronic and Computer Engineering, University of KwaZulu Natal, Durban 4041, South Africa;
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Saeed W, Shahbaz E, Maqsood Q, Ali SW, Mahnoor M. Cutaneous Oncology: Strategies for Melanoma Prevention, Diagnosis, and Therapy. Cancer Control 2024; 31:10732748241274978. [PMID: 39133519 PMCID: PMC11320697 DOI: 10.1177/10732748241274978] [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: 04/21/2024] [Revised: 07/11/2024] [Accepted: 07/30/2024] [Indexed: 08/13/2024] Open
Abstract
Skin cancer comprises one-third of all diagnosed cancer cases and remains a major health concern. Genetic and environmental parameters serve as the two main risk factors associated with the development of skin cancer, with ultraviolet radiation being the most common environmental risk factor. Studies have also found fair complexion, arsenic toxicity, indoor tanning, and family history among the prevailing causes of skin cancer. Prevention and early diagnosis play a crucial role in reducing the frequency and ensuring effective management of skin cancer. Recent studies have focused on exploring minimally invasive or non-invasive diagnostic technologies along with artificial intelligence to facilitate rapid and accurate diagnosis. The treatment of skin cancer ranges from traditional surgical excision to various advanced methods such as phototherapy, radiotherapy, immunotherapy, targeted therapy, and combination therapy. Recent studies have focused on immunotherapy, with the introduction of new checkpoint inhibitors and personalized immunotherapy enhancing treatment efficacy. Advancements in multi-omics, nanotechnology, and artificial intelligence have further deepened the understanding of the mechanisms underlying tumoral growth and their interaction with therapeutic effects, which has paved the way for precision oncology. This review aims to highlight the recent advancements in the understanding and management of skin cancer, and provide an overview of existing and emerging diagnostic, prognostic, and therapeutic modalities, while highlighting areas that require further research to bridge the existing knowledge gaps.
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Affiliation(s)
- Wajeeha Saeed
- Department of Food Sciences, Faculty of Agricultural Sciences, University of the Punjab, Lahore, Pakistan
| | - Esha Shahbaz
- Department of Food Sciences, Faculty of Agricultural Sciences, University of the Punjab, Lahore, Pakistan
| | - Quratulain Maqsood
- Centre for Applied Molecular Biology, University of the Punjab, Lahore Pakistan
| | - Shinawar Waseem Ali
- Department of Food Sciences, Faculty of Agricultural Sciences, University of the Punjab, Lahore, Pakistan
| | - Muhammada Mahnoor
- Sehat Medical Complex Lake City, University of Lahore, Lahore Pakistan
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13
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Liu Q, Zhang J, Bai Y. Mapping the landscape of artificial intelligence in skin cancer research: a bibliometric analysis. Front Oncol 2023; 13:1222426. [PMID: 37901316 PMCID: PMC10613074 DOI: 10.3389/fonc.2023.1222426] [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: 05/14/2023] [Accepted: 09/18/2023] [Indexed: 10/31/2023] Open
Abstract
Objective Artificial intelligence (AI), with its potential to diagnose skin cancer, has the potential to revolutionize future medical and dermatological practices. However, the current knowledge regarding the utilization of AI in skin cancer diagnosis remains somewhat limited, necessitating further research. This study employs visual bibliometric analysis to consolidate and present insights into the evolution and deployment of AI in the context of skin cancer. Through this analysis, we aim to shed light on the research developments, focal areas of interest, and emerging trends within AI and its application to skin cancer diagnosis. Methods On July 14, 2023, articles and reviews about the application of AI in skin cancer, spanning the years from 1900 to 2023, were selected from the Web of Science Core Collection. Co-authorship, co-citation, and co-occurrence analyses of countries, institutions, authors, references, and keywords within this field were conducted using a combination of tools, including CiteSpace V (version 6.2. R3), VOSviewer (version 1.6.18), SCImago, Microsoft Excel 2019, and R 4.2.3. Results A total of 512 papers matching the search terms and inclusion/exclusion criteria were published between 1991 and 2023. The United States leads in publications with 149, followed by India with 61. Germany holds eight positions among the top 10 institutions, while the United States has two. The most prevalent journals cited were Cancer, the European Journal of Cancer, and Sensors. The most frequently cited keywords include "skin cancer", "classification", "artificial intelligence", and "deep learning". Conclusions Research into the application of AI in skin cancer is rapidly expanding, and an increasing number of scholars are dedicating their efforts to this field. With the advancement of AI technology, new opportunities have arisen to enhance the accuracy of skin imaging diagnosis, treatment based on big data, and prognosis prediction. However, at present, the majority of AI research in the field of skin cancer diagnosis is still in the feasibility study stage. It has not yet made significant progress toward practical implementation in clinical settings. To make substantial strides in this field, there is a need to enhance collaboration between countries and institutions. Despite the potential benefits of AI in skin cancer research, numerous challenges remain to be addressed, including developing robust algorithms, resolving data quality issues, and enhancing results interpretability. Consequently, sustained efforts are essential to surmount these obstacles and facilitate the practical application of AI in skin cancer research.
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Affiliation(s)
- Qianwei Liu
- Graduate School, Beijing University of Chinese Medicine, Beijing, China
| | - Jie Zhang
- Library, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Yanping Bai
- Graduate School, Beijing University of Chinese Medicine, Beijing, China
- Department of Dermatology, China-Japan Friendship Hospital, Beijing, China
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14
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Sufyan M, Shokat Z, Ashfaq UA. Artificial intelligence in cancer diagnosis and therapy: Current status and future perspective. Comput Biol Med 2023; 165:107356. [PMID: 37688994 DOI: 10.1016/j.compbiomed.2023.107356] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 07/21/2023] [Accepted: 08/12/2023] [Indexed: 09/11/2023]
Abstract
Artificial intelligence (AI) in healthcare plays a pivotal role in combating many fatal diseases, such as skin, breast, and lung cancer. AI is an advanced form of technology that uses mathematical-based algorithmic principles similar to those of the human mind for cognizing complex challenges of the healthcare unit. Cancer is a lethal disease with many etiologies, including numerous genetic and epigenetic mutations. Cancer being a multifactorial disease is difficult to be diagnosed at an early stage. Therefore, genetic variations and other leading factors could be identified in due time through AI and machine learning (ML). AI is the synergetic approach for mining the drug targets, their mechanism of action, and drug-organism interaction from massive raw data. This synergetic approach is also facing several challenges in data mining but computational algorithms from different scientific communities for multi-target drug discovery are highly helpful to overcome the bottlenecks in AI for drug-target discovery. AI and ML could be the epicenter in the medical world for the diagnosis, treatment, and evaluation of almost any disease in the near future. In this comprehensive review, we explore the immense potential of AI and ML when integrated with the biological sciences, specifically in the context of cancer research. Our goal is to illuminate the many ways in which AI and ML are being applied to the study of cancer, from diagnosis to individualized treatment. We highlight the prospective role of AI in supporting oncologists and other medical professionals in making informed decisions and improving patient outcomes by examining the intersection of AI and cancer control. Although AI-based medical therapies show great potential, many challenges must be overcome before they can be implemented in clinical practice. We critically assess the current hurdles and provide insights into the future directions of AI-driven approaches, aiming to pave the way for enhanced cancer interventions and improved patient care.
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Affiliation(s)
- Muhammad Sufyan
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Pakistan.
| | - Zeeshan Shokat
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Pakistan.
| | - Usman Ali Ashfaq
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Pakistan.
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15
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Riazi Esfahani P, Mazboudi P, Reddy AJ, Farasat VP, Guirgus ME, Tak N, Min M, Arakji GH, Patel R. Leveraging Machine Learning for Accurate Detection and Diagnosis of Melanoma and Nevi: An Interdisciplinary Study in Dermatology. Cureus 2023; 15:e44120. [PMID: 37750114 PMCID: PMC10518209 DOI: 10.7759/cureus.44120] [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] [Accepted: 08/24/2023] [Indexed: 09/27/2023] Open
Abstract
This study explores the application of machine learning and deep learning algorithms to facilitate the accurate diagnosis of melanoma, a type of malignant skin cancer, and benign nevi. Leveraging a dataset of 793 dermatological images from the Kaggle online platform (Google LLC, Mountain View, California, United States), we developed a model that can accurately differentiate between these lesions based on their distinctive features. The dataset was divided into training (80%), validation (10%), and testing (10%) sets to optimize model performance and ensure its generalizability. Our findings demonstrate the potential of machine learning algorithms in enhancing the efficiency and accuracy of melanoma and nevi detection, with the developed model exhibiting robust performance metrics. Nonetheless, limitations exist due to the potential lack of comprehensive representation of melanoma and nevi cases in the dataset, and variations in image quality and acquisition methods, which may influence the model's performance in real-world clinical settings. Therefore, further research, validation studies, and integration into clinical practice are necessary to ensure the reliability and generalizability of these models. This study underscores the promise of artificial intelligence in advancing dermatologic diagnostics, aiming to improve patient outcomes by supporting early detection and treatment initiation for melanoma.
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Affiliation(s)
| | - Pasha Mazboudi
- Medicine, California University of Science and Medicine, Colton, USA
| | - Akshay J Reddy
- Medicine, California University of Science and Medicine, Colton, USA
| | | | - Monica E Guirgus
- Medicine, California University of Science and Medicine, Colton, USA
| | - Nathaniel Tak
- Medicine, Midwestern University Arizona College of Osteopathic Medicine, Glendale, USA
| | - Mildred Min
- Dermatology, California Northstate University College of Medicine, Elk Grove, USA
| | - Gordon H Arakji
- Health Sciences, California Northstate University, Rancho Cordova, USA
| | - Rakesh Patel
- Internal Medicine, East Tennessee State University Quillen College of Medicine, Johnson City, USA
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