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Alves A, Slim K. Can artificial intelligence help a digestive surgeon in management of rectal cancer? J Visc Surg 2024:S1878-7886(24)00068-7. [PMID: 38871539 DOI: 10.1016/j.jviscsurg.2024.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Affiliation(s)
- Arnaud Alves
- "ANTICIPE" Unit, Digestive Surgery Department, Calvados digestive tumour register, Inserm UMR 1086, CHU Caen, avenue de la Côte de Nacre, 14000 Caen, France.
| | - Karem Slim
- Republic Health Centre, Elsan Group, Clermont-Ferrand, France
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Dougan J, Patel N, Bardarov S. A Comparison of Diagnostic and Immunohistochemical Workup and Literature Review Capabilities of Online Artificial Intelligence Assistance Models in Pathology. Cureus 2024; 16:e61075. [PMID: 38915984 PMCID: PMC11196119 DOI: 10.7759/cureus.61075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/23/2024] [Indexed: 06/26/2024] Open
Abstract
Artificial intelligence (AI) is a suite of technologies that enables computers to learn and interpret information like human cognition. It has found applications across various fields, including healthcare, agriculture, astronomy, navigation, and robotics. Within healthcare, AI has the potential to enhance diagnostic accuracy, facilitate drug research, and automate patient experiences. This comparative study focuses on the proficiency of AI in generating accurate differential diagnoses in the field of pathology. Six medical vignettes were crafted, and each scenario was then input into three different AI platforms. The pathologist reviewed and determined the most accurate AI model.
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Affiliation(s)
- Johnika Dougan
- Pathology and Laboratory Medicine, St. George's University School of Medicine, St. George's, GRD
| | - Netra Patel
- Pathology and Laboratory Medicine, American University of Antigua, Antigua, USA
| | - Svetoslav Bardarov
- Pathology and Laboratory Medicine, Richmond University Medical Center, Staten Island, USA
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Miller I, Rosic N, Stapelberg M, Hudson J, Coxon P, Furness J, Walsh J, Climstein M. Performance of Commercial Dermatoscopic Systems That Incorporate Artificial Intelligence for the Identification of Melanoma in General Practice: A Systematic Review. Cancers (Basel) 2024; 16:1443. [PMID: 38611119 PMCID: PMC11011068 DOI: 10.3390/cancers16071443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/03/2024] [Accepted: 04/04/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND Cutaneous melanoma remains an increasing global public health burden, particularly in fair-skinned populations. Advancing technologies, particularly artificial intelligence (AI), may provide an additional tool for clinicians to help detect malignancies with a more accurate success rate. This systematic review aimed to report the performance metrics of commercially available convolutional neural networks (CNNs) tasked with detecting MM. METHODS A systematic literature search was performed using CINAHL, Medline, Scopus, ScienceDirect and Web of Science databases. RESULTS A total of 16 articles reporting MM were included in this review. The combined number of melanomas detected was 1160, and non-melanoma lesions were 33,010. The performance of market-approved technology and clinician performance for classifying melanoma was highly heterogeneous, with sensitivity ranging from 16.4 to 100.0%, specificity between 40.0 and 98.3% and accuracy between 44.0 and 92.0%. Less heterogeneity was observed when clinicians worked in unison with AI, with sensitivity ranging between 83.3 and 100.0%, specificity between 83.7 and 87.3%, and accuracy between 86.4 and 86.9%. CONCLUSION Instead of focusing on the performance of AI versus clinicians for classifying melanoma, more consistent performance has been obtained when clinicians' work is supported by AI, facilitating management decisions and improving health outcomes.
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Affiliation(s)
- Ian Miller
- Aquatic Based Research, Southern Cross University, Bilinga, QLD 4225, Australia; (I.M.); (N.R.)
- Faculty of Health, Southern Cross University, Bilinga, QLD 4225, Australia (P.C.)
- Specialist Suite, John Flynn Hospital, Tugun, QLD 4224, Australia
| | - Nedeljka Rosic
- Aquatic Based Research, Southern Cross University, Bilinga, QLD 4225, Australia; (I.M.); (N.R.)
- Faculty of Health, Southern Cross University, Bilinga, QLD 4225, Australia (P.C.)
| | - Michael Stapelberg
- Aquatic Based Research, Southern Cross University, Bilinga, QLD 4225, Australia; (I.M.); (N.R.)
- Faculty of Health, Southern Cross University, Bilinga, QLD 4225, Australia (P.C.)
- Specialist Suite, John Flynn Hospital, Tugun, QLD 4224, Australia
| | - Jeremy Hudson
- Faculty of Health, Southern Cross University, Bilinga, QLD 4225, Australia (P.C.)
- North Queensland Skin Centre, Townsville, QLD 4810, Australia
| | - Paul Coxon
- Faculty of Health, Southern Cross University, Bilinga, QLD 4225, Australia (P.C.)
- North Queensland Skin Centre, Townsville, QLD 4810, Australia
| | - James Furness
- Water Based Research Unit, Bond University, Robina, QLD 4226, Australia;
| | - Joe Walsh
- Sport Science Institute, Sydney, NSW 2000, Australia;
- AI Consulting Group, Sydney, NSW 2000, Australia
| | - Mike Climstein
- Aquatic Based Research, Southern Cross University, Bilinga, QLD 4225, Australia; (I.M.); (N.R.)
- Faculty of Health, Southern Cross University, Bilinga, QLD 4225, Australia (P.C.)
- Physical Activity, Lifestyle, Ageing and Wellbeing Faculty Research Group, University of Sydney, Sydney, NSW 2050, Australia
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Crovella S, Suleman M, Tricarico PM, Al-Khuzaei S, Moltrasio C, El Omri A, Marzano AV. Harnessing artificial intelligence for advancing early diagnosis in hidradenitis suppurativa. Ital J Dermatol Venerol 2024; 159:43-49. [PMID: 38345291 DOI: 10.23736/s2784-8671.23.07829-5] [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: 02/15/2024]
Abstract
This perspective delves into the integration of artificial intelligence (AI) to enhance early diagnosis in hidradenitis suppurativa (HS). Despite significantly impacting Quality of Life, HS presents diagnostic challenges leading to treatment delays. We present a viewpoint on AI-powered clinical decision support system designed for HS, emphasizing the transformative potential of AI in dermatology. HS diagnosis, primarily reliant on clinical evaluation and visual inspection, often results in late-stage identification with substantial tissue damage. The incorporation of AI, utilizing machine learning and deep learning algorithms, addresses this challenge by excelling in image analysis. AI adeptly recognizes subtle patterns in skin lesions, providing objective and standardized analyses to mitigate subjectivity in traditional diagnostic approaches. The AI integration encompasses diverse datasets, including clinical records, images, biochemical and immunological data and OMICs data. AI algorithms enable nuanced comprehension, allowing for precise and customized diagnoses. We underscore AI's potential for continuous learning and adaptation, refining recommendations based on evolving data. Challenges in AI integration, such as data privacy, algorithm bias, and interpretability, are addressed, emphasizing the ethical considerations of responsible AI deployment, including transparency, human oversight, and striking a balance between automation and human intervention. From the dermatologists' standpoint, we illustrate how AI enhances diagnostic accuracy, treatment planning, and long-term follow-up in HS management. Dermatologists leverage AI to analyze clinical records, dermatological images, and various data types, facilitating a proactive and personalized approach. AI's dynamic nature supports continuous learning, refining diagnostic and treatment strategies, ultimately reshaping standards of care in dermatology.
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Affiliation(s)
- Sergio Crovella
- Laboratory of Animal Research Center (LARC), Qatar University, Doha, Qatar -
| | - Muhammad Suleman
- Laboratory of Animal Research Center (LARC), Qatar University, Doha, Qatar
| | | | - Safaa Al-Khuzaei
- Department of Dermatology and Venereology, Hamad Medical Corporation, Rumailah Hospital, Doha, Qatar
| | - Chiara Moltrasio
- Unit of Dermatology, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Abdelfatteh El Omri
- Surgical Research Section, Department of Surgery, Hamad Medical Corporation, Doha, Qatar
| | - Angelo V Marzano
- Unit of Dermatology, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
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Gu Y, Wang M, Gong Y, Li X, Wang Z, Wang Y, Jiang S, Zhang D, Li C. Unveiling breast cancer risk profiles: a survival clustering analysis empowered by an online web application. Future Oncol 2023; 19:2651-2667. [PMID: 38095059 DOI: 10.2217/fon-2023-0736] [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: 12/23/2023] Open
Abstract
Aim: To develop a shiny app for doctors to investigate breast cancer treatments through a new approach by incorporating unsupervised clustering and survival information. Materials & methods: Analysis is based on the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, which contains 1726 subjects and 22 variables. Cox regression was used to identify survival risk factors for K-means clustering. Logrank tests and C-statistics were compared across different cluster numbers and Kaplan-Meier plots were presented. Results & conclusion: Our study fills an existing void by introducing a unique combination of unsupervised learning techniques and survival information on the clinician side, demonstrating the potential of survival clustering as a valuable tool in uncovering hidden structures based on distinct risk profiles.
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Affiliation(s)
- Yuan Gu
- Department of Statistics, The George Washington University, Washington, DC 20052, USA
| | - Mingyue Wang
- Department of Mathematics, Syracuse University, Syracuse, NY 13244, USA
| | - Yishu Gong
- Harvard T.H. Chan School of Public Health, Harvard University, Boston, NY 02115, USA
| | - Xin Li
- Department of Statistics, The George Washington University, Washington, DC 20052, USA
| | - Ziyang Wang
- Department of Computer Science, University of Oxford, Oxford, OX1 3QD, UK
| | - Yuli Wang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Song Jiang
- Department of Biochemistry, Huzhou Institute of Biological Products Co., Ltd., 313017, China
| | - Dan Zhang
- Department of Information Science and Engineering, Shandong University, Shan Dong, China
| | - Chen Li
- Department of Biology, Chemistry and Pharmacy, Free University of Berlin, Berlin, 14195, Germany
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