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Chen S, Lobo BC. Regulatory and Implementation Considerations for Artificial Intelligence. Otolaryngol Clin North Am 2024; 57:871-886. [PMID: 38839554 DOI: 10.1016/j.otc.2024.04.007] [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: 06/07/2024]
Abstract
Successful artificial intelligence (AI) implementation is predicated on the trust of clinicians and patients, and is achieved through a culture of responsible use, focusing on regulations, standards, and education. Otolaryngologists can overcome barriers in AI implementation by promoting data standardization through professional societies, engaging in institutional efforts to integrate AI, and developing otolaryngology-specific AI education for both trainees and practitioners.
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Affiliation(s)
- Si Chen
- Department of Otolaryngology - Head and Neck Surgery, University of Florida College of Medicine, 1345 Center Drive, PO Box 100264, Gainesville, FL 32610, USA.
| | - Brian C Lobo
- Department of Otolaryngology - Head and Neck Surgery, University of Florida College of Medicine, 1345 Center Drive, PO Box 100264, Gainesville, FL 32610, USA
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2
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Ye Z, Zhang D, Zhao Y, Chen M, Wang H, Seery S, Qu Y, Xue P, Jiang Y. Deep learning algorithms for melanoma detection using dermoscopic images: A systematic review and meta-analysis. Artif Intell Med 2024; 155:102934. [PMID: 39088883 DOI: 10.1016/j.artmed.2024.102934] [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: 03/10/2023] [Revised: 06/21/2024] [Accepted: 07/22/2024] [Indexed: 08/03/2024]
Abstract
BACKGROUND Melanoma is a serious risk to human health and early identification is vital for treatment success. Deep learning (DL) has the potential to detect cancer using imaging technologies and many studies provide evidence that DL algorithms can achieve high accuracy in melanoma diagnostics. OBJECTIVES To critically assess different DL performances in diagnosing melanoma using dermatoscopic images and discuss the relationship between dermatologists and DL. METHODS Ovid-Medline, Embase, IEEE Xplore, and the Cochrane Library were systematically searched from inception until 7th December 2021. Studies that reported diagnostic DL model performances in detecting melanoma using dermatoscopic images were included if they had specific outcomes and histopathologic confirmation. Binary diagnostic accuracy data and contingency tables were extracted to analyze outcomes of interest, which included sensitivity (SEN), specificity (SPE), and area under the curve (AUC). Subgroup analyses were performed according to human-machine comparison and cooperation. The study was registered in PROSPERO, CRD42022367824. RESULTS 2309 records were initially retrieved, of which 37 studies met our inclusion criteria, and 27 provided sufficient data for meta-analytical synthesis. The pooled SEN was 82 % (range 77-86), SPE was 87 % (range 84-90), with an AUC of 0.92 (range 0.89-0.94). Human-machine comparison had pooled AUCs of 0.87 (0.84-0.90) and 0.83 (0.79-0.86) for DL and dermatologists, respectively. Pooled AUCs were 0.90 (0.87-0.93), 0.80 (0.76-0.83), and 0.88 (0.85-0.91) for DL, and junior and senior dermatologists, respectively. Analyses of human-machine cooperation were 0.88 (0.85-0.91) for DL, 0.76 (0.72-0.79) for unassisted, and 0.87 (0.84-0.90) for DL-assisted dermatologists. CONCLUSIONS Evidence suggests that DL algorithms are as accurate as senior dermatologists in melanoma diagnostics. Therefore, DL could be used to support dermatologists in diagnostic decision-making. Although, further high-quality, large-scale multicenter studies are required to address the specific challenges associated with medical AI-based diagnostics.
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Affiliation(s)
- Zichen Ye
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Daqian Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuankai Zhao
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mingyang Chen
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Huike Wang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Samuel Seery
- Population Health Sciences Institute, School of Pharmacy, Newcastle University, Newcastle NE1 7RU, United Kingdom of Great Britain and Northern Ireland
| | - Yimin Qu
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Peng Xue
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Yu Jiang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
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3
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Cockerell CJ, Goldust M. Ethical concerns related to the use of artificial intelligence in dermatopathology. Int J Dermatol 2024; 63:e128-e129. [PMID: 38702951 DOI: 10.1111/ijd.17223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 05/06/2024]
Affiliation(s)
- Clay J Cockerell
- Lake Granbury Medical Center, Texas College of Osteopathic Medicine, Dallas, TX, USA
| | - Mohamad Goldust
- Department of Dermatology, Yale University School of Medicine, New Haven, CT, USA
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4
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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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5
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Zhou X, Cai F, Li S, Li G, Zhang C, Xie J, Yang Y. Machine learning techniques for prediction in pregnancy complicated by autoimmune rheumatic diseases: Applications and challenges. Int Immunopharmacol 2024; 134:112238. [PMID: 38735259 DOI: 10.1016/j.intimp.2024.112238] [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: 03/05/2024] [Revised: 05/01/2024] [Accepted: 05/08/2024] [Indexed: 05/14/2024]
Abstract
Autoimmune rheumatic diseases are chronic conditions affecting multiple systems and often occurring in young women of childbearing age. The diseases and the physiological characteristics of pregnancy significantly impact maternal-fetal health and pregnancy outcomes. Currently, the integration of big data with healthcare has led to the increasing popularity of using machine learning (ML) to mine clinical data for studying pregnancy complications. In this review, we introduce the basics of ML and the recent advances and trends of ML in different prediction applications for common pregnancy complications by autoimmune rheumatic diseases. Finally, the challenges and future for enhancing the accuracy, reliability, and clinical applicability of ML in prediction have been discussed. This review will provide insights into the utilization of ML in identifying and assisting clinical decision-making for pregnancy complications, while also establishing a foundation for exploring comprehensive management strategies for pregnancy and enhancing maternal and child health.
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Affiliation(s)
- Xiaoshi Zhou
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Feifei Cai
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Shiran Li
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Guolin Li
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Changji Zhang
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Jingxian Xie
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; College of Pharmacy, Southwest Medical University, Luzhou, China
| | - Yong Yang
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
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6
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Gordon ER, Trager MH, Kontos D, Weng C, Geskin LJ, Dugdale LS, Samie FH. Ethical considerations for artificial intelligence in dermatology: a scoping review. Br J Dermatol 2024; 190:789-797. [PMID: 38330217 DOI: 10.1093/bjd/ljae040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/26/2023] [Accepted: 01/23/2024] [Indexed: 02/10/2024]
Abstract
The field of dermatology is experiencing the rapid deployment of artificial intelligence (AI), from mobile applications (apps) for skin cancer detection to large language models like ChatGPT that can answer generalist or specialist questions about skin diagnoses. With these new applications, ethical concerns have emerged. In this scoping review, we aimed to identify the applications of AI to the field of dermatology and to understand their ethical implications. We used a multifaceted search approach, searching PubMed, MEDLINE, Cochrane Library and Google Scholar for primary literature, following the PRISMA Extension for Scoping Reviews guidance. Our advanced query included terms related to dermatology, AI and ethical considerations. Our search yielded 202 papers. After initial screening, 68 studies were included. Thirty-two were related to clinical image analysis and raised ethical concerns for misdiagnosis, data security, privacy violations and replacement of dermatologist jobs. Seventeen discussed limited skin of colour representation in datasets leading to potential misdiagnosis in the general population. Nine articles about teledermatology raised ethical concerns, including the exacerbation of health disparities, lack of standardized regulations, informed consent for AI use and privacy challenges. Seven addressed inaccuracies in the responses of large language models. Seven examined attitudes toward and trust in AI, with most patients requesting supplemental assessment by a physician to ensure reliability and accountability. Benefits of AI integration into clinical practice include increased patient access, improved clinical decision-making, efficiency and many others. However, safeguards must be put in place to ensure the ethical application of AI.
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Affiliation(s)
- Emily R Gordon
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Megan H Trager
- Columbia University Irving Medical Center, Departments of Dermatology
| | - Despina Kontos
- University of Pennsylvania, Perelman School of Medicine, Department of Radiology, Philadelphia, PA, USA
- Radiology
| | | | - Larisa J Geskin
- Columbia University Irving Medical Center, Departments of Dermatology
| | - Lydia S Dugdale
- Columbia University Vagelos College of Physicians and Surgeons, Department of Medicine, Center for Clinical Medical Ethics, New York, NY, USA
| | - Faramarz H Samie
- Columbia University Irving Medical Center, Departments of Dermatology
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7
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Talimi H, Retmi K, Fissoune R, Lemrani M. Artificial Intelligence in Cutaneous Leishmaniasis Diagnosis: Current Developments and Future Perspectives. Diagnostics (Basel) 2024; 14:963. [PMID: 38732377 PMCID: PMC11083257 DOI: 10.3390/diagnostics14090963] [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: 03/25/2024] [Revised: 04/26/2024] [Accepted: 04/30/2024] [Indexed: 05/13/2024] Open
Abstract
Cutaneous Leishmaniasis (CL) is a major global health problem requiring appropriate diagnosis methods. Its diagnosis is challenging, particularly in resource-limited settings. The integration of Artificial Intelligence (AI) into medical diagnostics has shown promising results in various fields, including dermatology. In this systematic review, we aim to highlight the value of using AI for CL diagnosis and the AI-based algorithms that are employed in this process, and to identify gaps that need to be addressed. Our work highlights that only a limited number of studies are related to using AI algorithms for CL diagnosis. Among these studies, seven gaps were identified for future research. Addressing these considerations will pave the way for the development of robust AI systems and encourage more research in CL detection by AI. This could contribute to improving CL diagnosis and, ultimately, healthcare outcomes in CL-endemic regions.
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Affiliation(s)
- Hasnaa Talimi
- Laboratory of Parasitology and Vector-Borne-Diseases, Institut Pasteur du Maroc, Casablanca 20360, Morocco;
- Systems and Data Engineering Team, National School of Applied Sciences, University Abdelmalek Essaadi, Tangier 93000, Morocco;
| | - Kawtar Retmi
- Institute of Biological Sciences (ISSB-P), Mohammed VI Polytechnique University, Ben Guerir 43150, Morocco;
| | - Rachida Fissoune
- Systems and Data Engineering Team, National School of Applied Sciences, University Abdelmalek Essaadi, Tangier 93000, Morocco;
| | - Meryem Lemrani
- Laboratory of Parasitology and Vector-Borne-Diseases, Institut Pasteur du Maroc, Casablanca 20360, Morocco;
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8
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Aminoshariae A, Nosrat A, Nagendrababu V, Dianat O, Mohammad-Rahimi H, O'Keefe AW, Setzer FC. Artificial Intelligence in Endodontic Education. J Endod 2024; 50:562-578. [PMID: 38387793 DOI: 10.1016/j.joen.2024.02.011] [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: 10/23/2023] [Revised: 01/15/2024] [Accepted: 02/12/2024] [Indexed: 02/24/2024]
Abstract
AIMS The future dental and endodontic education must adapt to the current digitalized healthcare system in a hyper-connected world. The purpose of this scoping review was to investigate the ways an endodontic education curriculum could benefit from the implementation of artificial intelligence (AI) and overcome the limitations of this technology in the delivery of healthcare to patients. METHODS An electronic search was carried out up to December 2023 using MEDLINE, Web of Science, Cochrane Library, and a manual search of reference literature. Grey literature, ongoing clinical trials were also searched using ClinicalTrials.gov. RESULTS The search identified 251 records, of which 35 were deemed relevant to artificial intelligence (AI) and Endodontic education. Areas in which AI might aid students with their didactic and clinical endodontic education were identified as follows: 1) radiographic interpretation; 2) differential diagnosis; 3) treatment planning and decision-making; 4) case difficulty assessment; 5) preclinical training; 6) advanced clinical simulation and case-based training, 7) real-time clinical guidance; 8) autonomous systems and robotics; 9) progress evaluation and personalized education; 10) calibration and standardization. CONCLUSIONS AI in endodontic education will support clinical and didactic teaching through individualized feedback; enhanced, augmented, and virtually generated training aids; automated detection and diagnosis; treatment planning and decision support; and AI-based student progress evaluation, and personalized education. Its implementation will inarguably change the current concept of teaching Endodontics. Dental educators would benefit from introducing AI in clinical and didactic pedagogy; however, they must be aware of AI's limitations and challenges to overcome.
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Affiliation(s)
| | - Ali Nosrat
- Division of Endodontics, Department of Advanced Oral Sciences and Therapeutics, School of Dentistry, University of Maryland Baltimore, Baltimore, Maryland; Private Practice, Centreville Endodontics, Centreville, Virginia
| | - Venkateshbabu Nagendrababu
- Department of Preventive and Restorative Dentistry, University of Sharjah, College of Dental Medicine, Sharjah, United Arab Emirates
| | - Omid Dianat
- Division of Endodontics, Department of Advanced Oral Sciences and Therapeutics, School of Dentistry, University of Maryland Baltimore, Baltimore, Maryland; Private Practice, Centreville Endodontics, Centreville, Virginia
| | - Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Federal Republic of Germany
| | | | - Frank C Setzer
- Department of Endodontics, School of Dental Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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9
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Alhatem A, Wong T, Clark Lambert W. Revolutionizing diagnostic pathology: The emergence and impact of artificial intelligence-what doesn't kill you makes you stronger? Clin Dermatol 2024; 42:268-274. [PMID: 38181890 DOI: 10.1016/j.clindermatol.2023.12.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2024]
Abstract
This study explored the integration and impact of artificial intelligence (AI) in diagnostic pathology, particularly dermatopathology, assessing its challenges and potential solutions for global health care enhancement. A comprehensive literature search in PubMed and Google Scholar, conducted on March 30, 2023, and using terms related to AI, pathology, and machine learning, yielded 44 relevant publications. These were analyzed under themes including the evolution of deep learning in pathology, AI's role in replacing pathologists, development challenges of diagnostic algorithms, clinical implementation hurdles, strategies for practical application in dermatopathology, and future prospects of AI in this field. The findings highlight AI's transformative potential in pathology, underscore the need for ongoing research, collaboration, and regulatory dialogue, and emphasize the importance of addressing the ethical and practical challenges in AI implementation for improved global health care outcomes.
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Affiliation(s)
- Albert Alhatem
- Department of Pathology, Immunology and Laboratory Medicine and Department of Dermatology, Rutgers-New Jersey Medical School, Newark, New Jersey, USA
| | - Trish Wong
- Department of Pathology, Immunology and Laboratory Medicine and Department of Dermatology, Rutgers-New Jersey Medical School, Newark, New Jersey, USA
| | - W Clark Lambert
- Department of Pathology, Immunology and Laboratory Medicine and Department of Dermatology, Rutgers-New Jersey Medical School, Newark, New Jersey, USA.
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10
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Joly-Chevrier M, Nguyen AXL, Liang L, Lesko-Krleza M, Lefrançois P. The State of Artificial Intelligence in Skin Cancer Publications. J Cutan Med Surg 2024; 28:146-152. [PMID: 38323537 PMCID: PMC11015717 DOI: 10.1177/12034754241229361] [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] [Indexed: 02/08/2024]
Abstract
BACKGROUND Artificial intelligence (AI) in skin cancer is a promising research field to assist physicians and to provide support to patients remotely. Physicians' awareness to new developments in AI research is important to define the best practices and scope of integrating AI-enabled technologies within a clinical setting. OBJECTIVES To analyze the characteristics and trends of AI skin cancer publications from dermatology journals. METHODS AI skin cancer publications were retrieved in June 2022 from the Web of Science. Publications were screened by title, abstract, and keywords to assess eligibility. Publications were fully reviewed. Publications were divided between nonmelanoma skin cancer (NMSC), melanoma, and skin cancer studies. The primary measured outcome was the number of citations. The secondary measured outcomes were articles' general characteristics and features related to AI. RESULTS A total of 168 articles were included: 25 on NMSC, 77 on melanoma, and 66 on skin cancer. The most common types of skin cancers were melanoma (134, 79.8%), basal cell carcinoma (61, 36.3%), and squamous cell carcinoma (45, 26.9%). All articles were published between 2000 and 2022, with 49 (29.2%) of them being published in 2021. Original studies that developed or assessed an algorithm predominantly used supervised learning (66, 97.0%) and deep neural networks (42, 67.7%). The most used imaging modalities were standard dermoscopy (76, 45.2%) and clinical images (39, 23.2%). CONCLUSIONS Most publications focused on developing or assessing screening technologies with mainly deep neural network algorithms. This indicates the eminent need for dermatologists to label or annotate images used by novel AI systems.
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Affiliation(s)
| | | | - Laurence Liang
- Faculty of Engineering, McGill University, Montreal, QC, Canada
| | - Michael Lesko-Krleza
- Division of Computer Engineering, Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada
| | - Philippe Lefrançois
- Division of Dermatology, Department of Medicine, McGill University, Montreal, QC, Canada
- Division of Dermatology, Department of Medicine, Jewish General Hospital, Montreal, QC, Canada
- Lady Davis Institute for Medical Research, Montreal, QC, Canada
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11
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Shah A, Wahood S, Guermazi D, Brem CE, Saliba E. Skin and Syntax: Large Language Models in Dermatopathology. Dermatopathology (Basel) 2024; 11:101-111. [PMID: 38390851 PMCID: PMC10885095 DOI: 10.3390/dermatopathology11010009] [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: 01/01/2024] [Revised: 02/04/2024] [Accepted: 02/08/2024] [Indexed: 02/24/2024] Open
Abstract
This literature review introduces the integration of Large Language Models (LLMs) in the field of dermatopathology, outlining their potential benefits, challenges, and prospects. It discusses the changing landscape of dermatopathology with the emergence of LLMs. The potential advantages of LLMs include a streamlined generation of pathology reports, the ability to learn and provide up-to-date information, and simplified patient education. Existing instances of LLMs encompass diagnostic support, research acceleration, and trainee education. Challenges involve biases, data privacy and quality, and establishing a balance between AI and dermatopathological expertise. Prospects include the integration of LLMs with other AI technologies to improve diagnostics and the improvement of multimodal LLMs that can handle both text and image input. Our implementation guidelines highlight the importance of model transparency and interpretability, data quality, and continuous oversight. The transformative potential of LLMs in dermatopathology is underscored, with an emphasis on a dynamic collaboration between artificial intelligence (AI) experts (technical specialists) and dermatopathologists (clinicians) for improved patient outcomes.
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Affiliation(s)
- Asghar Shah
- Department of Dermatology, The Warren Alpert Medical School, Brown University, Providence, RI 02903, USA
| | - Samer Wahood
- Department of Dermatology, The Warren Alpert Medical School, Brown University, Providence, RI 02903, USA
| | - Dorra Guermazi
- Department of Dermatology, The Warren Alpert Medical School, Brown University, Providence, RI 02903, USA
| | - Candice E Brem
- Section of Dermatopathology, Department of Dermatology, Chobanian and Avedisian School of Medicine, Boston University, Boston, MA 02118, USA
| | - Elie Saliba
- Department of Dermatology, The Warren Alpert Medical School, Brown University, Providence, RI 02903, USA
- Department of Dermatology, Gilbert and Rose-Marie Chagoury School of Medicine, Lebanese American University, Beirut 13-5053, Lebanon
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12
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Mittal A, Mittal BB. Comparative Analysis of US Guidelines for the Management of Cutaneous Squamous Cell and Basal Cell Carcinoma. J Skin Cancer 2024; 2024:3859066. [PMID: 38370137 PMCID: PMC10872771 DOI: 10.1155/2024/3859066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 01/11/2024] [Accepted: 01/22/2024] [Indexed: 02/20/2024] Open
Abstract
Background This study presents a comparative analysis of recently published guidelines to manage cutaneous squamous cell carcinoma (cSCC) and cutaneous basal cell carcinoma (cBCC) within the United States (US). Methods A PubMed database search was performed for the time period between June 1, 2016, and December 1, 2022. A comprehensive comparison was performed in the following clinical interest areas: staging and risk stratification, management of primary tumor and regional nodes with curative intent, and palliative treatment. Results Guidelines from 3 organizations were analyzed: the American Academy of Dermatology (AAD), the National Comprehensive Cancer Network (NCCN), and the American Society for Radiation Oncology (ASTRO). The guidelines used different methodologies to grade evidence, making comparison difficult. There was agreement that surgery is the preferred treatment for curative cBCC and cSCC. For patients ineligible for surgery, there was a consensus to recommend definitive radiation. AAD and NCCN recommended consideration of other topical modalities in selected low-risk cBCC. Postoperative radiation therapy (PORT) was uniformly recommended in patients with positive margins that could not be cleared with surgery and in patients with nerve invasion. The definition and extent of nerve invasion varied. All guidelines recommended surgery as the primary treatment in patients with lymph node metastases in a curative setting. The criteria used for PORT varied; NCCN and ASTRO used lymph node size, number of nodes, and extracapsular extension for recommending PORT. Both NCCN and ASTRO recommend consideration of systemic treatment along with PORT in patients with extracapsular extension. Conclusion: US guidelines provide contemporary and complementary information on the management of cBCC and cSCC. There are opportunities for research, particularly in the areas of staging, indications for adjuvant treatment in curative settings, extent of nerve invasion and prognosis, and the role of systemic treatments in curative and palliative settings.
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Affiliation(s)
- Amit Mittal
- Department of Dermatology, Mayo Clinic College of Medicine & Science, 200 First St SW, Rochester, MN 55905, USA
| | - Bharat B. Mittal
- Department of Radiation Oncology, Northwestern University, 251 E Huron LC-178, Chicago, IL 60611, USA
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13
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Waseh S, Lee JB. Advances in melanoma: epidemiology, diagnosis, and prognosis. Front Med (Lausanne) 2023; 10:1268479. [PMID: 38076247 PMCID: PMC10703395 DOI: 10.3389/fmed.2023.1268479] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 10/13/2023] [Indexed: 06/30/2024] Open
Abstract
Unraveling the multidimensional complexities of melanoma has required concerted efforts by dedicated community of researchers and clinicians battling against this deadly form of skin cancer. Remarkable advances have been made in the realm of epidemiology, classification, diagnosis, and therapy of melanoma. The treatment of advanced melanomas has entered the golden era as targeted personalized therapies have emerged that have significantly altered the mortality rate. A paradigm shift in the approach to melanoma classification, diagnosis, prognosis, and staging is underway, fueled by discoveries of genetic alterations in melanocytic neoplasms. A morphologic clinicopathologic classification of melanoma is expected to be replaced by a more precise molecular based one. As validated, convenient, and cost-effective molecular-based tests emerge, molecular diagnostics will play a greater role in the clinical and histologic diagnosis of melanoma. Artificial intelligence augmented clinical and histologic diagnosis of melanoma is expected to make the process more streamlined and efficient. A more accurate model of prognosis and staging of melanoma is emerging based on molecular understanding melanoma. This contribution summarizes the recent advances in melanoma epidemiology, classification, diagnosis, and prognosis.
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Affiliation(s)
- Shayan Waseh
- Department of Dermatology, Temple University Hospital, Philadelphia, PA, United States
| | - Jason B. Lee
- Department of Dermatology, Thomas Jefferson University, Philadelphia, PA, United States
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Thomas L, Hyde C, Mullarkey D, Greenhalgh J, Kalsi D, Ko J. Real-world post-deployment performance of a novel machine learning-based digital health technology for skin lesion assessment and suggestions for post-market surveillance. Front Med (Lausanne) 2023; 10:1264846. [PMID: 38020164 PMCID: PMC10645139 DOI: 10.3389/fmed.2023.1264846] [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: 07/21/2023] [Accepted: 10/10/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction Deep Ensemble for Recognition of Malignancy (DERM) is an artificial intelligence as a medical device (AIaMD) tool for skin lesion assessment. Methods We report prospective real-world performance from its deployment within skin cancer pathways at two National Health Service hospitals (UK) between July 2021 and October 2022. Results A total of 14,500 cases were seen, including patients 18-100 years old with Fitzpatrick skin types I-VI represented. Based on 8,571 lesions assessed by DERM with confirmed outcomes, versions A and B demonstrated very high sensitivity for detecting melanoma (95.0-100.0%) or malignancy (96.0-100.0%). Benign lesion specificity was 40.7-49.4% (DERM-vA) and 70.1-73.4% (DERM-vB). DERM identified 15.0-31.0% of cases as eligible for discharge. Discussion We show DERM performance in-line with sensitivity targets and pre-marketing authorisation research, and it reduced the caseload for hospital specialists in two pathways. Based on our experience we offer suggestions on key elements of post-market surveillance for AIaMDs.
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Affiliation(s)
- Lucy Thomas
- Chelsea and Westminster Hospital NHS Foundation Trust, London, United Kingdom
| | - Chris Hyde
- Exeter Test Group, Department of Health and Community Sciences, University of Exeter Medical School, Exeter, United Kingdom
| | | | | | | | - Justin Ko
- Department of Dermatology, Stanford Medicine, Stanford, CA, United States
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15
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Luo N, Zhong X, Su L, Cheng Z, Ma W, Hao P. Artificial intelligence-assisted dermatology diagnosis: From unimodal to multimodal. Comput Biol Med 2023; 165:107413. [PMID: 37703714 DOI: 10.1016/j.compbiomed.2023.107413] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/02/2023] [Accepted: 08/28/2023] [Indexed: 09/15/2023]
Abstract
Artificial Intelligence (AI) is progressively permeating medicine, notably in the realm of assisted diagnosis. However, the traditional unimodal AI models, reliant on large volumes of accurately labeled data and single data type usage, prove insufficient to assist dermatological diagnosis. Augmenting these models with text data from patient narratives, laboratory reports, and image data from skin lesions, dermoscopy, and pathologies could significantly enhance their diagnostic capacity. Large-scale pre-training multimodal models offer a promising solution, exploiting the burgeoning reservoir of clinical data and amalgamating various data types. This paper delves into unimodal models' methodologies, applications, and shortcomings while exploring how multimodal models can enhance accuracy and reliability. Furthermore, integrating cutting-edge technologies like federated learning and multi-party privacy computing with AI can substantially mitigate patient privacy concerns in dermatological datasets and further fosters a move towards high-precision self-diagnosis. Diagnostic systems underpinned by large-scale pre-training multimodal models can facilitate dermatology physicians in formulating effective diagnostic and treatment strategies and herald a transformative era in healthcare.
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Affiliation(s)
- Nan Luo
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Xiaojing Zhong
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Luxin Su
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Zilin Cheng
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Wenyi Ma
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Pingsheng Hao
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
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16
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Sauter D, Lodde G, Nensa F, Schadendorf D, Livingstone E, Kukuk M. Deep learning in computational dermatopathology of melanoma: A technical systematic literature review. Comput Biol Med 2023; 163:107083. [PMID: 37315382 DOI: 10.1016/j.compbiomed.2023.107083] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 05/10/2023] [Accepted: 05/27/2023] [Indexed: 06/16/2023]
Abstract
Deep learning (DL) has become one of the major approaches in computational dermatopathology, evidenced by a significant increase in this topic in the current literature. We aim to provide a structured and comprehensive overview of peer-reviewed publications on DL applied to dermatopathology focused on melanoma. In comparison to well-published DL methods on non-medical images (e.g., classification on ImageNet), this field of application comprises a specific set of challenges, such as staining artifacts, large gigapixel images, and various magnification levels. Thus, we are particularly interested in the pathology-specific technical state-of-the-art. We also aim to summarize the best performances achieved thus far with respect to accuracy, along with an overview of self-reported limitations. Accordingly, we conducted a systematic literature review of peer-reviewed journal and conference articles published between 2012 and 2022 in the databases ACM Digital Library, Embase, IEEE Xplore, PubMed, and Scopus, expanded by forward and backward searches to identify 495 potentially eligible studies. After screening for relevance and quality, a total of 54 studies were included. We qualitatively summarized and analyzed these studies from technical, problem-oriented, and task-oriented perspectives. Our findings suggest that the technical aspects of DL for histopathology in melanoma can be further improved. The DL methodology was adopted later in this field, and still lacks the wider adoption of DL methods already shown to be effective for other applications. We also discuss upcoming trends toward ImageNet-based feature extraction and larger models. While DL has achieved human-competitive accuracy in routine pathological tasks, its performance on advanced tasks is still inferior to wet-lab testing (for example). Finally, we discuss the challenges impeding the translation of DL methods to clinical practice and provide insight into future research directions.
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Affiliation(s)
- Daniel Sauter
- Department of Computer Science, Fachhochschule Dortmund, 44227 Dortmund, Germany.
| | - Georg Lodde
- Department of Dermatology, University Hospital Essen, 45147 Essen, Germany
| | - Felix Nensa
- Institute for AI in Medicine (IKIM), University Hospital Essen, 45131 Essen, Germany; Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, Germany
| | - Dirk Schadendorf
- Department of Dermatology, University Hospital Essen, 45147 Essen, Germany
| | | | - Markus Kukuk
- Department of Computer Science, Fachhochschule Dortmund, 44227 Dortmund, Germany
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17
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Kiran N, Sapna F, Kiran F, Kumar D, Raja F, Shiwlani S, Paladini A, Sonam F, Bendari A, Perkash RS, Anjali F, Varrassi G. Digital Pathology: Transforming Diagnosis in the Digital Age. Cureus 2023; 15:e44620. [PMID: 37799211 PMCID: PMC10547926 DOI: 10.7759/cureus.44620] [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] [Accepted: 09/03/2023] [Indexed: 10/07/2023] Open
Abstract
In the context of rapid technological advancements, the narrative review titled "Digital Pathology: Transforming Diagnosis in the Digital Age" explores the significant impact of digital pathology in reshaping diagnostic approaches. This review delves into the various effects of the field, including remote consultations and artificial intelligence (AI)-assisted analysis, revealing the ongoing transformation taking place. The investigation explores the process of digitizing traditional glass slides, which aims to improve accessibility and facilitate sharing. Additionally, it addresses the complexities associated with data security and standardization challenges. Incorporating AI enhances pathologists' diagnostic capabilities and accelerates analytical procedures. Furthermore, the review highlights the growing importance of collaborative networks facilitating global knowledge sharing. It also emphasizes the significant impact of this technology on medical education and patient care. This narrative review aims to provide an overview of digital pathology's transformative and innovative potential, highlighting its disruptive nature in reshaping diagnostic practices.
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Affiliation(s)
- Nfn Kiran
- Pathology and Laboratory Medicine, Staten Island University Hospital, New York, USA
| | - Fnu Sapna
- Pathology and Laboratory Medicine, Albert Einstein College of Medicine, New York, USA
| | - Fnu Kiran
- Pathology and Laboratory Medicine, University of Missouri School of Medicine, Columbia, USA
| | - Deepak Kumar
- Pathology and Laboratory Medicine, University of Missouri, Columbia, USA
| | - Fnu Raja
- Pathology and Laboratory Medicine, MetroHealth Medical Center, Cleveland, USA
| | - Sheena Shiwlani
- Pathology and Laboratory Medicine, Isra University, Karachi, PAK
- Pathology, Mount Sinai Hospital, New York, USA
| | - Antonella Paladini
- Clinical Medicine, Public Health and Life Science (MESVA), University of L'Aquila, L'Aquila, ITA
| | - Fnu Sonam
- Pathology and Laboratory Medicine, Liaquat University of Medical and Health Sciences, Sukkur, PAK
- Medicine, Mustafai Trust Central Hospital, Sukkur, PAK
| | - Ahmed Bendari
- Pathology and Laboratory Medicine, Lenox Hill Hospital, New York, USA
| | | | - Fnu Anjali
- Internal Medicine, Sakhi Baba General Hospital, Sukkur, PAK
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18
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Choi S, Cho SI, Jung W, Lee T, Choi SJ, Song S, Park G, Park S, Ma M, Pereira S, Yoo D, Shin S, Ock CY, Kim S. Deep learning model improves tumor-infiltrating lymphocyte evaluation and therapeutic response prediction in breast cancer. NPJ Breast Cancer 2023; 9:71. [PMID: 37648694 PMCID: PMC10469174 DOI: 10.1038/s41523-023-00577-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 08/17/2023] [Indexed: 09/01/2023] Open
Abstract
Tumor-infiltrating lymphocytes (TILs) have been recognized as key players in the tumor microenvironment of breast cancer, but substantial interobserver variability among pathologists has impeded its utility as a biomarker. We developed a deep learning (DL)-based TIL analyzer to evaluate stromal TILs (sTILs) in breast cancer. Three pathologists evaluated 402 whole slide images of breast cancer and interpreted the sTIL scores. A standalone performance of the DL model was evaluated in the 210 cases (52.2%) exhibiting sTIL score differences of less than 10 percentage points, yielding a concordance correlation coefficient of 0.755 (95% confidence interval [CI], 0.693-0.805) in comparison to the pathologists' scores. For the 226 slides (56.2%) showing a 10 percentage points or greater variance between pathologists and the DL model, revisions were made. The number of discordant cases was reduced to 116 (28.9%) with the DL assistance (p < 0.001). The DL assistance also increased the concordance correlation coefficient of the sTIL score among every two pathologists. In triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients who underwent the neoadjuvant chemotherapy, the DL-assisted revision notably accentuated higher sTIL scores in responders (26.8 ± 19.6 vs. 19.0 ± 16.4, p = 0.003). Furthermore, the DL-assistant revision disclosed the correlation of sTIL-high tumors (sTIL ≥ 50) with the chemotherapeutic response (odd ratio 1.28 [95% confidence interval, 1.01-1.63], p = 0.039). Through enhancing inter-pathologist concordance in sTIL interpretation and predicting neoadjuvant chemotherapy response, here we report the utility of the DL-based tool as a reference for sTIL scoring in breast cancer assessment.
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Affiliation(s)
- Sangjoon Choi
- Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | | | | | | | - Su Jin Choi
- Department of Pathology, Ajou University School of Medicine, Suwon, Republic of Korea
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Republic of Korea
| | | | | | | | - Minuk Ma
- Lunit Inc, Seoul, Republic of Korea
| | | | | | | | | | - Seokhwi Kim
- Department of Pathology, Ajou University School of Medicine, Suwon, Republic of Korea.
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Republic of Korea.
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19
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Lee H, Lee Y, Jung SW, Lee S, Oh B, Yang S. Deep Learning-Based Evaluation of Ultrasound Images for Benign Skin Tumors. SENSORS (BASEL, SWITZERLAND) 2023; 23:7374. [PMID: 37687830 PMCID: PMC10490539 DOI: 10.3390/s23177374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 08/07/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023]
Abstract
In this study, a combined convolutional neural network for the diagnosis of three benign skin tumors was designed, and its effectiveness was verified through quantitative and statistical analysis. To this end, 698 sonographic images were taken and diagnosed at the Department of Dermatology at Severance Hospital in Seoul, Korea, between 10 November 2017 and 17 January 2020. Through an empirical process, a convolutional neural network combining two structures, which consist of a residual structure and an attention-gated structure, was designed. Five-fold cross-validation was applied, and the train set for each fold was augmented by the Fast AutoAugment technique. As a result of training, for three benign skin tumors, an average accuracy of 95.87%, an average sensitivity of 90.10%, and an average specificity of 96.23% were derived. Also, through statistical analysis using a class activation map and physicians' findings, it was found that the judgment criteria of physicians and the trained combined convolutional neural network were similar. This study suggests that the model designed and trained in this study can be a diagnostic aid to assist physicians and enable more efficient and accurate diagnoses.
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Affiliation(s)
- Hyunwoo Lee
- Department of Biomedical Engineering, Yonsei University, Wonju 26493, Republic of Korea
| | - Yerin Lee
- Department of Precision Medicine, Yonsei University Wonju College of Medicine, Wonju 26426, Republic of Korea
| | - Seung-Won Jung
- Department of Dermatology, Yonsei University Wonju College of Medicine, Wonju 26426, Republic of Korea
| | - Solam Lee
- Department of Dermatology, Yonsei University Wonju College of Medicine, Wonju 26426, Republic of Korea
| | - Byungho Oh
- Department of Dermatology, Cutaneous Biology Research Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Sejung Yang
- Department of Precision Medicine, Yonsei University Wonju College of Medicine, Wonju 26426, Republic of Korea
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20
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Cazzato G, Massaro A, Colagrande A, Trilli I, Ingravallo G, Casatta N, Lupo C, Ronchi A, Franco R, Maiorano E, Vacca A. Artificial Intelligence Applied to a First Screening of Naevoid Melanoma: A New Use of Fast Random Forest Algorithm in Dermatopathology. Curr Oncol 2023; 30:6066-6078. [PMID: 37504312 PMCID: PMC10378276 DOI: 10.3390/curroncol30070452] [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: 05/22/2023] [Revised: 06/12/2023] [Accepted: 06/22/2023] [Indexed: 07/29/2023] Open
Abstract
Malignant melanoma (MM) is the "great mime" of dermatopathology, and it can present such rare variants that even the most experienced pathologist might miss or misdiagnose them. Naevoid melanoma (NM), which accounts for about 1% of all MM cases, is a constant challenge, and when it is not diagnosed in a timely manner, it can even lead to death. In recent years, artificial intelligence has revolutionised much of what has been achieved in the biomedical field, and what once seemed distant is now almost incorporated into the diagnostic therapeutic flow chart. In this paper, we present the results of a machine learning approach that applies a fast random forest (FRF) algorithm to a cohort of naevoid melanomas in an attempt to understand if and how this approach could be incorporated into the business process modelling and notation (BPMN) approach. The FRF algorithm provides an innovative approach to formulating a clinical protocol oriented toward reducing the risk of NM misdiagnosis. The work provides the methodology to integrate FRF into a mapped clinical process.
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Affiliation(s)
- Gerardo Cazzato
- Section of Pathology, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari "Aldo Moro", 70124 Bari, Italy
| | - Alessandro Massaro
- LUM Enterprise srl, S.S. 100-Km.18, Parco il Baricentro, 70010 Bari, Italy
- Department of Management, Finance and Technology, LUM-Libera Università Mediterranea "Giuseppe Degennaro", S.S. 100-Km.18, Parco il Baricentro, 70010 Bari, Italy
| | - Anna Colagrande
- Section of Pathology, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari "Aldo Moro", 70124 Bari, Italy
| | - Irma Trilli
- Odontomatostologic Clinic, Department of Innovative Technologies in Medicine and Dentistry, University of Chieti "G. D'Annunzio", 66100 Chieti, Italy
| | - Giuseppe Ingravallo
- Section of Pathology, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari "Aldo Moro", 70124 Bari, Italy
| | - Nadia Casatta
- Innovation Department, Diapath S.p.A., Via Savoldini n.71, 24057 Martinengo, Italy
| | - Carmelo Lupo
- Innovation Department, Diapath S.p.A., Via Savoldini n.71, 24057 Martinengo, Italy
| | - Andrea Ronchi
- Pathology Unit, Department of Mental Health and Physic and Preventive Medicine, University of Campania "Luigi Vanvitelli", 80138 Naples, Italy
| | - Renato Franco
- Pathology Unit, Department of Mental Health and Physic and Preventive Medicine, University of Campania "Luigi Vanvitelli", 80138 Naples, Italy
| | - Eugenio Maiorano
- Section of Pathology, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari "Aldo Moro", 70124 Bari, Italy
| | - Angelo Vacca
- Centro Interdisciplinare Ricerca Telemedicina-CITEL, Università degli Studi di Bari "Aldo Moro", 70124 Bari, Italy
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21
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Doeleman T, Hondelink LM, Vermeer MH, van Dijk MR, Schrader AMR. Artificial intelligence in digital pathology of cutaneous lymphomas: a review of the current state and future perspectives. Semin Cancer Biol 2023:S1044-579X(23)00095-0. [PMID: 37331571 DOI: 10.1016/j.semcancer.2023.06.004] [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: 12/09/2022] [Revised: 06/01/2023] [Accepted: 06/12/2023] [Indexed: 06/20/2023]
Abstract
Primary cutaneous lymphomas (CLs) represent a heterogeneous group of T-cell lymphomas and B-cell lymphomas that present in the skin without evidence of extracutaneous involvement at time of diagnosis. CLs are largely distinct from their systemic counterparts in clinical presentation, histopathology, and biological behavior and, therefore, require different therapeutic management. Additional diagnostic burden is added by the fact that several benign inflammatory dermatoses mimic CL subtypes, requiring clinicopathological correlation for definitive diagnosis. Due to the heterogeneity and rarity of CL, adjunct diagnostic tools are welcomed, especially by pathologists without expertise in this field or with limited access to a centralized specialist panel. The transition into digital pathology workflows enables artificial intelligence (AI)-based analysis of patients' whole-slide pathology images (WSIs). AI can be used to automate manual processes in histopathology but, more importantly, can be applied to complex diagnostic tasks, especially suitable for rare disease like CL. To date, AI-based applications for CL have been minimally explored in literature. However, in other skin cancers and systemic lymphomas, disciplines that are recognized here as the building blocks for CLs, several studies demonstrated promising results using AI for disease diagnosis and subclassification, cancer detection, specimen triaging, and outcome prediction. Additionally, AI allows discovery of novel biomarkers or may help to quantify established biomarkers. This review summarizes and blends applications of AI in pathology of skin cancer and lymphoma and proposes how these findings can be applied to diagnostics of CL.
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Affiliation(s)
- Thom Doeleman
- Department of Pathology, Leiden University Medical Centre, Leiden, the Netherlands; Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands.
| | - Liesbeth M Hondelink
- Department of Pathology, Leiden University Medical Centre, Leiden, the Netherlands
| | - Maarten H Vermeer
- Department of Dermatology, Leiden University Medical Center, Leiden, the Netherlands
| | - Marijke R van Dijk
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Anne M R Schrader
- Department of Pathology, Leiden University Medical Centre, Leiden, the Netherlands
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22
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Plass M, Kargl M, Kiehl TR, Regitnig P, Geißler C, Evans T, Zerbe N, Carvalho R, Holzinger A, Müller H. Explainability and causability in digital pathology. J Pathol Clin Res 2023. [PMID: 37045794 DOI: 10.1002/cjp2.322] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 02/17/2023] [Accepted: 03/16/2023] [Indexed: 04/14/2023]
Abstract
The current move towards digital pathology enables pathologists to use artificial intelligence (AI)-based computer programmes for the advanced analysis of whole slide images. However, currently, the best-performing AI algorithms for image analysis are deemed black boxes since it remains - even to their developers - often unclear why the algorithm delivered a particular result. Especially in medicine, a better understanding of algorithmic decisions is essential to avoid mistakes and adverse effects on patients. This review article aims to provide medical experts with insights on the issue of explainability in digital pathology. A short introduction to the relevant underlying core concepts of machine learning shall nurture the reader's understanding of why explainability is a specific issue in this field. Addressing this issue of explainability, the rapidly evolving research field of explainable AI (XAI) has developed many techniques and methods to make black-box machine-learning systems more transparent. These XAI methods are a first step towards making black-box AI systems understandable by humans. However, we argue that an explanation interface must complement these explainable models to make their results useful to human stakeholders and achieve a high level of causability, i.e. a high level of causal understanding by the user. This is especially relevant in the medical field since explainability and causability play a crucial role also for compliance with regulatory requirements. We conclude by promoting the need for novel user interfaces for AI applications in pathology, which enable contextual understanding and allow the medical expert to ask interactive 'what-if'-questions. In pathology, such user interfaces will not only be important to achieve a high level of causability. They will also be crucial for keeping the human-in-the-loop and bringing medical experts' experience and conceptual knowledge to AI processes.
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Affiliation(s)
- Markus Plass
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Michaela Kargl
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Tim-Rasmus Kiehl
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Berlin, Germany
| | - Peter Regitnig
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Christian Geißler
- DAI-Labor, Agent Oriented Technologies (AOT), Technische Universität Berlin, Berlin, Germany
| | - Theodore Evans
- DAI-Labor, Agent Oriented Technologies (AOT), Technische Universität Berlin, Berlin, Germany
| | - Norman Zerbe
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Berlin, Germany
| | - Rita Carvalho
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Berlin, Germany
| | - Andreas Holzinger
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria
- Human-Centered AI Lab, University of Natural Resources and Life Sciences Vienna, Vienna, Austria
| | - Heimo Müller
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria
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23
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O'Brien B, Zhao K, Gibson TA, Smith DF, Ryan D, Whitfield J, Smith CD, Bromley M. Artificial intelligence for basal cell carcinoma: diagnosis and distinction from histological mimics. Pathology 2023; 55:342-349. [PMID: 36641379 DOI: 10.1016/j.pathol.2022.10.004] [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: 06/12/2022] [Revised: 10/03/2022] [Accepted: 10/11/2022] [Indexed: 12/24/2022]
Abstract
We trained an artificial intelligence (AI) algorithm to identify basal cell carcinoma (BCC), and to distinguish BCC from histological mimics. A total of 1061 glass slides were collected: 616 containing BCC and 445 without BCC. BCC slides were collected prospectively, reflecting the range of specimen types and morphological variety encountered in routine pathology practice. Benign and malignant histological mimics of BCC were selected prospectively and retrospectively, including cases considered diagnostically challenging for pathologists. Glass slides were digitally scanned to create a whole slide image (WSI), which was divided into patches representing a tissue area of 65,535 μm2. Pathologists annotated the data, yielding 87,205 patches labelled BCC present and 1,688,697 patches labelled BCC absent. The COMPASS model (COntext-aware Multi-scale tool for Pathologists Assessing SlideS) based on Convolutional Neural Networks, was trained to provide a probability of BCC being present at the patch level and the slide level. The test set comprised 246 slides, 147 of which contained BCC. The COMPASS AI model demonstrated high accuracy, classifying WSIs as containing BCC with a sensitivity of 98.0% and a specificity of 97.0%, representing 240 WSIs classified correctly, three false positives, and three false negatives. Using BCC as a proof of concept, we demonstrate how AI can account for morphological variation within an entity, and accurately distinguish from histologically similar entities. Our study highlights the potential for AI in routine pathology practice.
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Affiliation(s)
- Blake O'Brien
- Sullivan Nicolaides Pathology, Bowen Hills, Qld, Australia. Blake_O'
| | - Kun Zhao
- Sullivan Nicolaides Pathology, Bowen Hills, Qld, Australia
| | | | - Daniel F Smith
- Sullivan Nicolaides Pathology, Bowen Hills, Qld, Australia
| | - David Ryan
- Sullivan Nicolaides Pathology, Bowen Hills, Qld, Australia
| | | | | | - Mark Bromley
- Sullivan Nicolaides Pathology, Bowen Hills, Qld, Australia
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24
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Frisinger A, Papachristou P. The voice of healthcare: introducing digital decision support systems into clinical practice - a qualitative study. BMC PRIMARY CARE 2023; 24:67. [PMID: 36907875 PMCID: PMC10008705 DOI: 10.1186/s12875-023-02024-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 03/01/2023] [Indexed: 03/14/2023]
Abstract
BACKGROUND There is a need to accelerate digital transformation in healthcare to meet increasing needs and demands. The accuracy of medical digital diagnosis tools is improving. The introduction of new technology in healthcare can however be challenging and it is unclear how it should be done to reach desired results. The aim of this study was to explore perceptions and experiences of introducing new Information Technology (IT) in a primary healthcare organisation, exemplified with a Clinical Decision Support System (CDSS) for malignant melanoma. METHODS A qualitative interview-based study was performed in Region Stockholm, Sweden, with fifteen medical doctors representing three different organisational levels - primary care physician, primary healthcare centre manager, and regional manager/chief medical officer. In addition, one software provider was included. Interview data were analysed according to content analysis. RESULTS One central theme "Introduction of digital CDSS in primary healthcare requires a multidimensional perspective and handling" along with seven main categories and thirty-three subcategories emerged from the analysis. Digital transformation showed to be key for current healthcare providers to stay relevant and competitive. However, healthcare represents a closed community, very capable but with lack of time, fostered to be sceptical to new why change needs to bring true value and be inspired by people with medical background to motivate the powerful frontline. CONCLUSIONS This qualitative study revealed structured information of what goes wrong and right and what needs to be considered when driving digital change in primary care organisations. The task shows to be complex and the importance of listening to the voice of healthcare is valuable for understanding the conditions that need to be fulfilled when adopting new technology into a healthcare organization. By considering the findings of this study upcoming digital transformations can improve their success-rate. The information may also be used in developing a holistic approach or framework model, adapted to primary health care, that can support and accelerate the needed digitalization in healthcare as such.
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Affiliation(s)
- Ann Frisinger
- Study Programme in Medicine, Karolinska Institutet, Stockholm, Sweden.
| | - Panagiotis Papachristou
- Division of Family Medicine and Primary Care, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, SE-141 83, Stockholm, Sweden.
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Ibraheim MK, Gupta R, Gardner JM, Elsensohn A. Artificial Intelligence in Dermatopathology: An Analysis of Its Practical Application. Dermatopathology (Basel) 2023; 10:93-94. [PMID: 36810572 PMCID: PMC9944453 DOI: 10.3390/dermatopathology10010014] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 01/29/2023] [Indexed: 02/18/2023] Open
Abstract
In recent years, researchers have explored potential uses for artificial intelligence (AI) in medical practice [...].
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Affiliation(s)
- Marina Kristy Ibraheim
- Department of Dermatology, Loma Linda University, Loma Linda, CA 92354, USA
- Correspondence: ; Tel.: +1-909-558-2890
| | - Rohit Gupta
- School of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jerad M. Gardner
- Departments of Pathology and Dermatology, Geisinger Medical Center, Danville, PA 17822, USA
| | - Ashley Elsensohn
- Department of Dermatology, Loma Linda University, Loma Linda, CA 92354, USA
- Department of Pathology and Human Anatomy, Loma Linda University, Loma Linda, CA 92354, USA
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Jartarkar SR, Cockerell CJ, Patil A, Kassir M, Babaei M, Weidenthaler‐Barth B, Grabbe S, Goldust M. Artificial intelligence in Dermatopathology. J Cosmet Dermatol 2022; 22:1163-1167. [PMID: 36548174 DOI: 10.1111/jocd.15565] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 11/14/2022] [Accepted: 12/01/2022] [Indexed: 12/24/2022]
Abstract
INTRODUCTION Ever evolving research in medical field has reached an exciting stage with advent of newer technologies. With the introduction of digital microscopy, pathology has transitioned to become more digitally oriented speciality. The potential of artificial intelligence (AI) in dermatopathology is to aid the diagnosis, and it requires dermatopathologists' guidance for efficient functioning of artificial intelligence. METHOD Comprehensive literature search was performed using electronic online databases "PubMed" and "Google Scholar." Articles published in English language were considered for the review. RESULTS Convolutional neural network, a type of deep neural network, is considered as an ideal tool in image recognition, processing, classification, and segmentation. Implementation of AI in tumor pathology is involved in the diagnosis, grading, staging, and prognostic prediction as well as in identification of genetic or pathological features. In this review, we attempt to discuss the use of AI in dermatopathology, the attitude of patients and clinicians, its challenges, limitation, and potential opportunities in future implementation.
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Affiliation(s)
- Shishira R. Jartarkar
- Department of Dermatology Vydehi Institute of Medical Sciences and Research Centre University‐RGUHS Bengaluru India
| | - Clay J. Cockerell
- Departments of Dermatology and Pathology The University of Texas Southwestern Medical Center Dallas Texas USA
| | - Anant Patil
- Department of Pharmacology Dr. DY Patil Medical College Navi Mumbai India
| | | | - Mahsa Babaei
- School of Medicine Stanford University California USA
| | - Beate Weidenthaler‐Barth
- Department of Dermatology University Medical Center of the Johannes Gutenberg University Mainz Germany
| | - Stephan Grabbe
- Department of Dermatology University Medical Center of the Johannes Gutenberg University Mainz Germany
| | - Mohamad Goldust
- Department of Dermatology University Medical Center Mainz Mainz Germany
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Jartarkar SR. Artificial intelligence: Its role in dermatopathology. Indian J Dermatol Venereol Leprol 2022:1-4. [PMID: 36688886 DOI: 10.25259/ijdvl_725_2021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 08/01/2022] [Indexed: 12/15/2022]
Abstract
Artificial intelligence (AI), a major frontier in the field of medical research, can potentially lead to a paradigm shift in clinical practice. A type of artificial intelligence system known as convolutional neural network points to the possible utility of deep learning in dermatopathology. Though pathology has been traditionally restricted to microscopes and glass slides, recent advancement in digital pathological imaging has led to a transition making it a potential branch for the implementation of artificial intelligence. The current application of artificial intelligence in dermatopathology is to complement the diagnosis and requires a well-trained dermatopathologist's guidance for better designing and development of deep learning algorithms. Here we review the recent advances of artificial intelligence in dermatopathology, its applications in disease diagnosis and in research, along with its limitations and future potential.
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Affiliation(s)
- Shishira R Jartarkar
- Department of Dermatology, Venereology and Leprosy, Vydehi Institute of Medical Sciences and Research Centre, Whitefield, Bengaluru, Karnataka, India
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Humble N, Mozelius P. The threat, hype, and promise of artificial intelligence in education. DISCOVER ARTIFICIAL INTELLIGENCE 2022. [DOI: 10.1007/s44163-022-00039-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
AbstractThe idea of building intelligent machines has been around for centuries, with a new wave of promising artificial intelligence (AI) in the twenty-first century. Artificial Intelligence in Education (AIED) is a younger phenomenon that has created hype and promises, but also been seen as a threat by critical voices. There have been rich discussions on over-optimism and hype in contemporary AI research. Less has been written about the hyped expectations on AIED and its potential to transform current education. There is huge potential for efficiency and cost reduction, but there is also aspects of quality education and the teacher role. The aim of the study is to identify potential aspects of threat, hype and promise in artificial intelligence for education. A scoping literature review was conducted to gather relevant state-of-the art research in the field of AIED. Main keywords used in the literature search were: artificial intelligence, artificial intelligence in education, AI, AIED, teacher perspective, education, and teacher. Data were analysed with the SWOT-framework as theoretical lens for a thematic analysis. The study identifies a wide variety of strengths, weaknesses, opportunities, and threats for artificial intelligence in education. Findings suggest that there are several important questions to discuss and address in future research, such as: What should the role of the teacher be in education with AI? How does AI align with pedagogical goals and beliefs? And how to handle the potential leak and misuse of user data when AIED systems are developed by for-profit organisations?
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Escalé-Besa A, Fuster-Casanovas A, Börve A, Yélamos O, Fustà-Novell X, Esquius Rafat M, Marin-Gomez FX, Vidal-Alaball J. Using Artificial Intelligence as a Diagnostic Decision Support Tool in Skin Disease: Protocol for an Observational Prospective Cohort Study. JMIR Res Protoc 2022. [PMID: 36044249 PMCID: PMC9475422 DOI: 10.2196/37531 ] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Dermatological conditions are a relevant health problem. Each person has an average of 1.6 skin diseases per year, and consultations for skin pathology represent 20% of the total annual visits to primary care and around 35% are referred to a dermatology specialist. Machine learning (ML) models can be a good tool to help primary care professionals, as it can analyze and optimize complex sets of data. In addition, ML models are increasingly being applied to dermatology as a diagnostic decision support tool using image analysis, especially for skin cancer detection and classification. OBJECTIVE This study aims to perform a prospective validation of an image analysis ML model as a diagnostic decision support tool for the diagnosis of dermatological conditions. METHODS In this prospective study, 100 consecutive patients who visit a participant general practitioner (GP) with a skin problem in central Catalonia were recruited. Data collection was planned to last 7 months. Anonymized pictures of skin diseases were taken and introduced to the ML model interface (capable of screening for 44 different skin diseases), which returned the top 5 diagnoses by probability. The same image was also sent as a teledermatology consultation following the current stablished workflow. The GP, ML model, and dermatologist's assessments will be compared to calculate the precision, sensitivity, specificity, and accuracy of the ML model. The results will be represented globally and individually for each skin disease class using a confusion matrix and one-versus-all methodology. The time taken to make the diagnosis will also be taken into consideration. RESULTS Patient recruitment began in June 2021 and lasted for 5 months. Currently, all patients have been recruited and the images have been shown to the GPs and dermatologists. The analysis of the results has already started. CONCLUSIONS This study will provide information about ML models' effectiveness and limitations. External testing is essential for regulating these diagnostic systems to deploy ML models in a primary care practice setting.
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Affiliation(s)
- Anna Escalé-Besa
- Centre d'Atenció Primària Navàs-Balsareny, Institut Català de la Salut, Navàs, Spain
| | - Aïna Fuster-Casanovas
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Spain
- Health Promotion in Rural Areas Research Group, Gerència Territorial de la Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Spain
| | - Alexander Börve
- iDoc24 Inc, San Francisco, CA, United States
- Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Oriol Yélamos
- Dermatology Department, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
| | | | | | - Francesc X Marin-Gomez
- Servei d'Atenció Primària Osona, Gerència Territorial de Barcelona, Institut Català de la Salut, Vic, Spain
| | - Josep Vidal-Alaball
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Spain
- Health Promotion in Rural Areas Research Group, Gerència Territorial de la Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Spain
- Faculty of Medicine, University of Vic-Central University of Catalonia, Vic, Spain
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Escalé-Besa A, Fuster-Casanovas A, Börve A, Yélamos O, Fustà-Novell X, Esquius Rafat M, Marin-Gomez FX, Vidal-Alaball J. Using Artificial Intelligence as a Diagnostic Decision Support Tool in Skin Disease: Protocol for an Observational Prospective Cohort Study. JMIR Res Protoc 2022; 11:e37531. [PMID: 36044249 PMCID: PMC9475422 DOI: 10.2196/37531] [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: 02/24/2022] [Revised: 05/11/2022] [Accepted: 05/12/2022] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Dermatological conditions are a relevant health problem. Each person has an average of 1.6 skin diseases per year, and consultations for skin pathology represent 20% of the total annual visits to primary care and around 35% are referred to a dermatology specialist. Machine learning (ML) models can be a good tool to help primary care professionals, as it can analyze and optimize complex sets of data. In addition, ML models are increasingly being applied to dermatology as a diagnostic decision support tool using image analysis, especially for skin cancer detection and classification. OBJECTIVE This study aims to perform a prospective validation of an image analysis ML model as a diagnostic decision support tool for the diagnosis of dermatological conditions. METHODS In this prospective study, 100 consecutive patients who visit a participant general practitioner (GP) with a skin problem in central Catalonia were recruited. Data collection was planned to last 7 months. Anonymized pictures of skin diseases were taken and introduced to the ML model interface (capable of screening for 44 different skin diseases), which returned the top 5 diagnoses by probability. The same image was also sent as a teledermatology consultation following the current stablished workflow. The GP, ML model, and dermatologist's assessments will be compared to calculate the precision, sensitivity, specificity, and accuracy of the ML model. The results will be represented globally and individually for each skin disease class using a confusion matrix and one-versus-all methodology. The time taken to make the diagnosis will also be taken into consideration. RESULTS Patient recruitment began in June 2021 and lasted for 5 months. Currently, all patients have been recruited and the images have been shown to the GPs and dermatologists. The analysis of the results has already started. CONCLUSIONS This study will provide information about ML models' effectiveness and limitations. External testing is essential for regulating these diagnostic systems to deploy ML models in a primary care practice setting.
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Affiliation(s)
- Anna Escalé-Besa
- Centre d'Atenció Primària Navàs-Balsareny, Institut Català de la Salut, Navàs, Spain
| | - Aïna Fuster-Casanovas
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Spain
- Health Promotion in Rural Areas Research Group, Gerència Territorial de la Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Spain
| | - Alexander Börve
- iDoc24 Inc, San Francisco, CA, United States
- Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Oriol Yélamos
- Dermatology Department, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
| | | | | | - Francesc X Marin-Gomez
- Servei d'Atenció Primària Osona, Gerència Territorial de Barcelona, Institut Català de la Salut, Vic, Spain
| | - Josep Vidal-Alaball
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Spain
- Health Promotion in Rural Areas Research Group, Gerència Territorial de la Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Spain
- Faculty of Medicine, University of Vic-Central University of Catalonia, Vic, Spain
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Aldhyani THH, Verma A, Al-Adhaileh MH, Koundal D. Multi-Class Skin Lesion Classification Using a Lightweight Dynamic Kernel Deep-Learning-Based Convolutional Neural Network. Diagnostics (Basel) 2022; 12:diagnostics12092048. [PMID: 36140447 PMCID: PMC9497471 DOI: 10.3390/diagnostics12092048] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/20/2022] [Accepted: 08/22/2022] [Indexed: 11/16/2022] Open
Abstract
Skin is the primary protective layer of the internal organs of the body. Nowadays, due to increasing pollution and multiple other factors, various types of skin diseases are growing globally. With variable shapes and multiple types, the classification of skin lesions is a challenging task. Motivated by this spreading deformity in society, a lightweight and efficient model is proposed for the highly accurate classification of skin lesions. Dynamic-sized kernels are used in layers to obtain the best results, resulting in very few trainable parameters. Further, both ReLU and leakyReLU activation functions are purposefully used in the proposed model. The model accurately classified all of the classes of the HAM10000 dataset. The model achieved an overall accuracy of 97.85%, which is much better than multiple state-of-the-art heavy models. Further, our work is compared with some popular state-of-the-art and recent existing models.
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Affiliation(s)
- Theyazn H. H. Aldhyani
- Applied College in Abqaiq, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia
- Correspondence:
| | - Amit Verma
- School of Computer Science, University of Petroleum & Energy Studies, Dehradun 248007, India
| | - Mosleh Hmoud Al-Adhaileh
- Deanship of E-Learning and Distance Education, King Faisal University, P.O. Box 4000, Al-Ahsa 31982, Saudi Arabia
| | - Deepika Koundal
- School of Computer Science, University of Petroleum & Energy Studies, Dehradun 248007, India
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32
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Improving the Diagnosis of Skin Biopsies Using Tissue Segmentation. Diagnostics (Basel) 2022; 12:diagnostics12071713. [PMID: 35885617 PMCID: PMC9316584 DOI: 10.3390/diagnostics12071713] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/04/2022] [Accepted: 07/12/2022] [Indexed: 11/16/2022] Open
Abstract
Invasive melanoma, a common type of skin cancer, is considered one of the deadliest. Pathologists routinely evaluate melanocytic lesions to determine the amount of atypia, and if the lesion represents an invasive melanoma, its stage. However, due to the complicated nature of these assessments, inter- and intra-observer variability among pathologists in their interpretation are very common. Machine-learning techniques have shown impressive and robust performance on various tasks including healthcare. In this work, we study the potential of including semantic segmentation of clinically important tissue structure in improving the diagnosis of skin biopsy images. Our experimental results show a 6% improvement in F-score when using whole slide images along with epidermal nests and cancerous dermal nest segmentation masks compared to using whole-slide images alone in training and testing the diagnosis pipeline.
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Non-Melanoma Skin Cancer: A Genetic Update and Future Perspectives. Cancers (Basel) 2022; 14:cancers14102371. [PMID: 35625975 PMCID: PMC9139429 DOI: 10.3390/cancers14102371] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/04/2022] [Accepted: 05/09/2022] [Indexed: 02/06/2023] Open
Abstract
Simple Summary Non-melanoma skin cancer (NMSC) is the main type of cancer in the Caucasian population, and the number of cases continues to rise. Research mostly focuses on clinical characteristics analysis, but genetic features are crucial to malignancies’ establishment and advance. We aim to explore the genetic basics of skin cancer, surrounding microenvironment interactions, and regulation mechanisms to provide a broader perspective for new therapies’ development. Abstract Skin cancer is one of the main types of cancer worldwide, and non-melanoma skin cancer (NMSC) is the most frequent within this group. Basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) are the most common types. Multifactorial features are well-known for cancer development, and new hallmarks are gaining relevance. Genetics and epigenetic regulation play an essential role in cancer susceptibility and progression, as well as the variety of cells and molecules that interact in the tumor microenvironment. In this review, we provide an update on the genetic features of NMSC, candidate genes, and new therapies, considering diverse perspectives of skin carcinogenesis. The global health situation and the pandemic have been challenging for health care systems, especially in the diagnosis and treatment of patients with cancer. We provide innovative approaches to overcome the difficulties in the current clinical dynamics.
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Mahalingam M. Reforms, Errors, and Dermatopathology Malpractice: Then and Now: A Comprehensive Retrospective. Adv Anat Pathol 2022; 29:81-96. [PMID: 34561375 DOI: 10.1097/pap.0000000000000319] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Medical malpractice occurs when a hospital, doctor, or other health care professional, through a negligent act or omission, causes an injury to a patient. The negligence might be the result of errors in diagnosis, treatment, aftercare, or health management. To be considered medical malpractice under the law, the claim must violate the standard of care, the injury must be caused by the negligence and, last but most certainly not least, the injury must result in significant damages. This review is an overview of medicolegal issues specific to the practice of Dermatopathology with the caveat that most are likely pertinent to other specialties of pathology as well. The safety of patients remains the priority in pathology as it does in any medical undertaking, and this is no different in the practice of Dermatopathology. The review is broadly divided in 2 parts-we begin with an overview of tort reforms, advocated by physicians to reduce costs associated with malpractice defense. In the second part we address practical issues specific to the practice of pathology and dermatopathology. These include among others, errors-related to the biopsy type, inadequacy of clinical information regarding the lesion that is biopsied, role of interstate dermatopathology as well as examples of select entities commonly misdiagnosed in dermatopathology. In the last decade, artificial intelligence (AI) has moved to the forefront of technology. While research into the uses of AI in pathology is promising, the use of AI in diagnostic practice is still somewhat uncommon. Given that AI is not fully integrated routinely as a diagnostic adjunct, its' impact on pathology-specific medicolegal issues cannot, as yet at least, be defined. Restriction of medical malpractice is of particular relevance in the COVID-19 era, a period that is anything but normal. The response of states with specific pandemic-related guidelines is addressed with the caveat that this particular issue is only covered in select states. Furthermore, given that the COVID pandemic is only a year old, while it does not appear to have had an immediate impact on pathology-specific medicolegal matters, it is possible that the role of COVID on this issue, if any at all, will and can only be fully defined a few years down the line.
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Affiliation(s)
- Meera Mahalingam
- *Department of Dermatology, Tufts University School of Medicine, Boston
- †Dermatopathology Section, VA Consolidated Laboratories, Department of Pathology and Laboratory Medicine, West Roxbury, MA
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Malciu AM, Lupu M, Voiculescu VM. Artificial Intelligence-Based Approaches to Reflectance Confocal Microscopy Image Analysis in Dermatology. J Clin Med 2022; 11:jcm11020429. [PMID: 35054123 PMCID: PMC8780225 DOI: 10.3390/jcm11020429] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 01/11/2022] [Accepted: 01/12/2022] [Indexed: 12/22/2022] Open
Abstract
Reflectance confocal microscopy (RCM) is a non-invasive imaging method designed to identify various skin diseases. Confocal based diagnosis may be subjective due to the learning curve of the method, the scarcity of training programs available for RCM, and the lack of clearly defined diagnostic criteria for all skin conditions. Given that in vivo RCM is becoming more widely used in dermatology, numerous deep learning technologies have been developed in recent years to provide a more objective approach to RCM image analysis. Machine learning-based algorithms are used in RCM image quality assessment to reduce the number of artifacts the operator has to view, shorten evaluation times, and decrease the number of patient visits to the clinic. However, the current visual method for identifying the dermal-epidermal junction (DEJ) in RCM images is subjective, and there is a lot of variation. The delineation of DEJ on RCM images could be automated through artificial intelligence, saving time and assisting novice RCM users in studying the key DEJ morphological structure. The purpose of this paper is to supply a current summary of machine learning and artificial intelligence’s impact on the quality control of RCM images, key morphological structures identification, and detection of different skin lesion types on static RCM images.
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Affiliation(s)
- Ana Maria Malciu
- Department of Dermatology, Elias University Emergency Hospital, 011461 Bucharest, Romania;
| | - Mihai Lupu
- Department of Dermatology, Carol Davila University of Medicine and Pharmacy, 050474 Bucharest, Romania
- Correspondence: (M.L.); (V.M.V.)
| | - Vlad Mihai Voiculescu
- Department of Dermatology, Elias University Emergency Hospital, 011461 Bucharest, Romania;
- Department of Dermatology, Carol Davila University of Medicine and Pharmacy, 050474 Bucharest, Romania
- Correspondence: (M.L.); (V.M.V.)
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Martorell A, Martin-Gorgojo A, Ríos-Viñuela E, Rueda-Carnero J, Alfageme F, Taberner R. [Translated article] Artificial intelligence in dermatology: A threat or an opportunity? ACTAS DERMO-SIFILIOGRAFICAS 2022. [DOI: 10.1016/j.ad.2021.07.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022] Open
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Martorell A, Martin-Gorgojo A, Ríos-Viñuela E, Rueda-Carnero J, Alfageme F, Taberner R. Inteligencia artificial en dermatología: ¿amenaza u oportunidad? ACTAS DERMO-SIFILIOGRAFICAS 2022; 113:30-46. [DOI: 10.1016/j.ad.2021.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 07/18/2021] [Indexed: 11/25/2022] Open
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Chen SB, Novoa RA. Artificial intelligence for dermatopathology: Current trends and the road ahead. Semin Diagn Pathol 2022; 39:298-304. [DOI: 10.1053/j.semdp.2022.01.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 01/06/2022] [Accepted: 01/12/2022] [Indexed: 02/07/2023]
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39
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Bozsányi S, Varga NN, Farkas K, Bánvölgyi A, Lőrincz K, Lihacova I, Lihachev A, Plorina EV, Bartha Á, Jobbágy A, Kuroli E, Paragh G, Holló P, Medvecz M, Kiss N, Wikonkál NM. Multispectral Imaging Algorithm Predicts Breslow Thickness of Melanoma. J Clin Med 2021; 11:jcm11010189. [PMID: 35011930 PMCID: PMC8745435 DOI: 10.3390/jcm11010189] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 12/21/2021] [Accepted: 12/26/2021] [Indexed: 12/20/2022] Open
Abstract
Breslow thickness is a major prognostic factor for melanoma. It is based on histopathological evaluation, and thus it is not available to aid clinical decision making at the time of the initial melanoma diagnosis. In this work, we assessed the efficacy of multispectral imaging (MSI) to predict Breslow thickness and developed a classification algorithm to determine optimal safety margins of the melanoma excision. First, we excluded nevi from the analysis with a novel quantitative parameter. Parameter s’ could differentiate nevi from melanomas with a sensitivity of 89.60% and specificity of 88.11%. Following this step, we have categorized melanomas into three different subgroups based on Breslow thickness (≤1 mm, 1–2 mm and >2 mm) with a sensitivity of 78.00% and specificity of 89.00% and a substantial agreement (κ = 0.67; 95% CI, 0.58–0.76). We compared our results to the performance of dermatologists and dermatology residents who assessed dermoscopic and clinical images of these melanomas, and reached a sensitivity of 60.38% and specificity of 80.86% with a moderate agreement (κ = 0.41; 95% CI, 0.39–0.43). Based on our findings, this novel method may help predict the appropriate safety margins for curative melanoma excision.
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Affiliation(s)
- Szabolcs Bozsányi
- Department of Dermatology, Venereology and Dermatooncology, Semmelweis University, 1085 Budapest, Hungary; (S.B.); (N.N.V.); (K.F.); (A.B.); (K.L.); (A.J.); (E.K.); (P.H.); (M.M.); (N.K.)
- Selye János Doctoral College for Advanced Studies, Clinical Sciences Research Group, 1085 Budapest, Hungary
| | - Noémi Nóra Varga
- Department of Dermatology, Venereology and Dermatooncology, Semmelweis University, 1085 Budapest, Hungary; (S.B.); (N.N.V.); (K.F.); (A.B.); (K.L.); (A.J.); (E.K.); (P.H.); (M.M.); (N.K.)
| | - Klára Farkas
- Department of Dermatology, Venereology and Dermatooncology, Semmelweis University, 1085 Budapest, Hungary; (S.B.); (N.N.V.); (K.F.); (A.B.); (K.L.); (A.J.); (E.K.); (P.H.); (M.M.); (N.K.)
| | - András Bánvölgyi
- Department of Dermatology, Venereology and Dermatooncology, Semmelweis University, 1085 Budapest, Hungary; (S.B.); (N.N.V.); (K.F.); (A.B.); (K.L.); (A.J.); (E.K.); (P.H.); (M.M.); (N.K.)
| | - Kende Lőrincz
- Department of Dermatology, Venereology and Dermatooncology, Semmelweis University, 1085 Budapest, Hungary; (S.B.); (N.N.V.); (K.F.); (A.B.); (K.L.); (A.J.); (E.K.); (P.H.); (M.M.); (N.K.)
| | - Ilze Lihacova
- Biophotonics Laboratory, Institute of Atomic Physics and Spectroscopy, University of Latvia, 1004 Riga, Latvia; (I.L.); (A.L.); (E.V.P.)
| | - Alexey Lihachev
- Biophotonics Laboratory, Institute of Atomic Physics and Spectroscopy, University of Latvia, 1004 Riga, Latvia; (I.L.); (A.L.); (E.V.P.)
| | - Emilija Vija Plorina
- Biophotonics Laboratory, Institute of Atomic Physics and Spectroscopy, University of Latvia, 1004 Riga, Latvia; (I.L.); (A.L.); (E.V.P.)
| | - Áron Bartha
- Department of Bioinformatics, Semmelweis University, 1085 Budapest, Hungary;
- 2nd Department of Pediatrics, Semmelweis University, 1085 Budapest, Hungary
| | - Antal Jobbágy
- Department of Dermatology, Venereology and Dermatooncology, Semmelweis University, 1085 Budapest, Hungary; (S.B.); (N.N.V.); (K.F.); (A.B.); (K.L.); (A.J.); (E.K.); (P.H.); (M.M.); (N.K.)
| | - Enikő Kuroli
- Department of Dermatology, Venereology and Dermatooncology, Semmelweis University, 1085 Budapest, Hungary; (S.B.); (N.N.V.); (K.F.); (A.B.); (K.L.); (A.J.); (E.K.); (P.H.); (M.M.); (N.K.)
- 1st Department of Pathology and Experimental Cancer Research, Semmelweis University, 1085 Budapest, Hungary
| | - György Paragh
- Department of Dermatology, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203, USA;
- Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203, USA
| | - Péter Holló
- Department of Dermatology, Venereology and Dermatooncology, Semmelweis University, 1085 Budapest, Hungary; (S.B.); (N.N.V.); (K.F.); (A.B.); (K.L.); (A.J.); (E.K.); (P.H.); (M.M.); (N.K.)
| | - Márta Medvecz
- Department of Dermatology, Venereology and Dermatooncology, Semmelweis University, 1085 Budapest, Hungary; (S.B.); (N.N.V.); (K.F.); (A.B.); (K.L.); (A.J.); (E.K.); (P.H.); (M.M.); (N.K.)
| | - Norbert Kiss
- Department of Dermatology, Venereology and Dermatooncology, Semmelweis University, 1085 Budapest, Hungary; (S.B.); (N.N.V.); (K.F.); (A.B.); (K.L.); (A.J.); (E.K.); (P.H.); (M.M.); (N.K.)
| | - Norbert M. Wikonkál
- Department of Dermatology, Venereology and Dermatooncology, Semmelweis University, 1085 Budapest, Hungary; (S.B.); (N.N.V.); (K.F.); (A.B.); (K.L.); (A.J.); (E.K.); (P.H.); (M.M.); (N.K.)
- Correspondence:
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Martorell A, Martin-Gorgojo A, Ríos-Viñuela E, Rueda-Carnero J, Alfageme F, Taberner R. Artificial intelligence in dermatology: A threat or an opportunity? ACTAS DERMO-SIFILIOGRAFICAS 2021. [DOI: 10.1016/j.adengl.2021.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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41
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Cazzato G, Colagrande A, Cimmino A, Arezzo F, Loizzi V, Caporusso C, Marangio M, Foti C, Romita P, Lospalluti L, Mazzotta F, Cicco S, Cormio G, Lettini T, Resta L, Vacca A, Ingravallo G. Artificial Intelligence in Dermatopathology: New Insights and Perspectives. Dermatopathology (Basel) 2021; 8:418-425. [PMID: 34563035 PMCID: PMC8482082 DOI: 10.3390/dermatopathology8030044] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 08/12/2021] [Accepted: 08/17/2021] [Indexed: 02/05/2023] Open
Abstract
In recent years, an increasing enthusiasm has been observed towards artificial intelligence and machine learning, involving different areas of medicine. Among these, although still in the embryonic stage, the dermatopathological field has also been partially involved, with the attempt to develop and train algorithms that could assist the pathologist in the differential diagnosis of complex melanocytic lesions. In this article, we face this new challenge of the modern era, carry out a review of the literature regarding the state of the art and try to determine promising future perspectives.
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Affiliation(s)
- Gerardo Cazzato
- Section of Pathology, Department of Emergency and Organ Transplantation (DETO), University of Bari Aldo Moro, 70124 Bari, Italy; (A.C.); (A.C.); (C.C.); (T.L.); (L.R.)
- Correspondence: (G.C.); (G.I.)
| | - Anna Colagrande
- Section of Pathology, Department of Emergency and Organ Transplantation (DETO), University of Bari Aldo Moro, 70124 Bari, Italy; (A.C.); (A.C.); (C.C.); (T.L.); (L.R.)
| | - Antonietta Cimmino
- Section of Pathology, Department of Emergency and Organ Transplantation (DETO), University of Bari Aldo Moro, 70124 Bari, Italy; (A.C.); (A.C.); (C.C.); (T.L.); (L.R.)
| | - Francesca Arezzo
- Section of Ginecology and Obstetrics, Department of Biomedical Sciences and Human Oncology, University of Bari Aldo Moro, 70124 Bari, Italy; (F.A.); (V.L.); (G.C.)
| | - Vera Loizzi
- Section of Ginecology and Obstetrics, Department of Biomedical Sciences and Human Oncology, University of Bari Aldo Moro, 70124 Bari, Italy; (F.A.); (V.L.); (G.C.)
| | - Concetta Caporusso
- Section of Pathology, Department of Emergency and Organ Transplantation (DETO), University of Bari Aldo Moro, 70124 Bari, Italy; (A.C.); (A.C.); (C.C.); (T.L.); (L.R.)
| | - Marco Marangio
- Section of Informatics, University of Salento, 73100 Lecce, Italy;
| | - Caterina Foti
- Section of Dermatology, Department of Biomedical Science and Human Oncology, University of Bari Aldo Moro, 70124 Bari, Italy; (C.F.); (P.R.); (L.L.)
| | - Paolo Romita
- Section of Dermatology, Department of Biomedical Science and Human Oncology, University of Bari Aldo Moro, 70124 Bari, Italy; (C.F.); (P.R.); (L.L.)
| | - Lucia Lospalluti
- Section of Dermatology, Department of Biomedical Science and Human Oncology, University of Bari Aldo Moro, 70124 Bari, Italy; (C.F.); (P.R.); (L.L.)
| | - Francesco Mazzotta
- Pediatric Dermatology and Surgery Outpatients Department, Azienda Sanitaria Locale Barletta-Andria-Trani, 76123 Andria, Italy;
| | - Sebastiano Cicco
- Section of Internal Medicine, Department of Biomedical Sciences and Human Oncology, University of Bari Aldo Moro, 70124 Bari, Italy; (S.C.); (A.V.)
| | - Gennaro Cormio
- Section of Ginecology and Obstetrics, Department of Biomedical Sciences and Human Oncology, University of Bari Aldo Moro, 70124 Bari, Italy; (F.A.); (V.L.); (G.C.)
| | - Teresa Lettini
- Section of Pathology, Department of Emergency and Organ Transplantation (DETO), University of Bari Aldo Moro, 70124 Bari, Italy; (A.C.); (A.C.); (C.C.); (T.L.); (L.R.)
| | - Leonardo Resta
- Section of Pathology, Department of Emergency and Organ Transplantation (DETO), University of Bari Aldo Moro, 70124 Bari, Italy; (A.C.); (A.C.); (C.C.); (T.L.); (L.R.)
| | - Angelo Vacca
- Section of Internal Medicine, Department of Biomedical Sciences and Human Oncology, University of Bari Aldo Moro, 70124 Bari, Italy; (S.C.); (A.V.)
| | - Giuseppe Ingravallo
- Section of Pathology, Department of Emergency and Organ Transplantation (DETO), University of Bari Aldo Moro, 70124 Bari, Italy; (A.C.); (A.C.); (C.C.); (T.L.); (L.R.)
- Correspondence: (G.C.); (G.I.)
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