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Chatzipanagiotou OP, Loukas C, Vailas M, Machairas N, Kykalos S, Charalampopoulos G, Filippiadis D, Felekouras E, Schizas D. Artificial intelligence in hepatocellular carcinoma diagnosis: a comprehensive review of current literature. J Gastroenterol Hepatol 2024. [PMID: 38923550 DOI: 10.1111/jgh.16663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 04/26/2024] [Accepted: 06/07/2024] [Indexed: 06/28/2024]
Abstract
BACKGROUND AND AIM Hepatocellular carcinoma (HCC) diagnosis mainly relies on its pathognomonic radiological profile, obviating the need for biopsy. The project of incorporating artificial intelligence (AI) techniques in HCC aims to improve the performance of image recognition. Herein, we thoroughly analyze and evaluate proposed AI models in the field of HCC diagnosis. METHODS A comprehensive review of the literature was performed utilizing MEDLINE/PubMed and Web of Science databases with the end of search date being the 30th of September 2023. The MESH terms "Artificial Intelligence," "Liver Cancer," "Hepatocellular Carcinoma," "Machine Learning," and "Deep Learning" were searched in the title and/or abstract. All references of the obtained articles were also evaluated for any additional information. RESULTS Our search resulted in 183 studies meeting our inclusion criteria. Across all diagnostic modalities, reported area under the curve (AUC) of most developed models surpassed 0.900. A B-mode US and a contrast-enhanced US model achieved AUCs of 0.947 and 0.957, respectively. Regarding the more challenging task of HCC diagnosis, a 2021 deep learning model, trained with CT scans, classified hepatic malignant lesions with an AUC of 0.986. Finally, a MRI machine learning model developed in 2021 displayed an AUC of 0.975 when differentiating small HCCs from benign lesions, while another MRI-based model achieved HCC diagnosis with an AUC of 0.970. CONCLUSIONS AI tools may lead to significant improvement in diagnostic management of HCC. Many models fared better or comparable to experienced radiologists while proving capable of elevating radiologists' accuracy, demonstrating promising results for AI implementation in HCC-related diagnostic tasks.
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Affiliation(s)
- Odysseas P Chatzipanagiotou
- First Department of Surgery, National and Kapodistrian University of Athens, Laikon General Hospital, Athens, Greece
| | - Constantinos Loukas
- Laboratory of Medical Physics, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Michail Vailas
- First Department of Surgery, National and Kapodistrian University of Athens, Laikon General Hospital, Athens, Greece
| | - Nikolaos Machairas
- Second Department of Propaedeutic Surgery, National and Kapodistrian University of Athens, Laikon General Hospital, Athens, Greece
| | - Stylianos Kykalos
- Second Department of Propaedeutic Surgery, National and Kapodistrian University of Athens, Laikon General Hospital, Athens, Greece
| | - Georgios Charalampopoulos
- Second Department of Radiology, National and Kapodistrian University of Athens, Attikon University Hospital, Athens, Greece
| | - Dimitrios Filippiadis
- Second Department of Radiology, National and Kapodistrian University of Athens, Attikon University Hospital, Athens, Greece
| | - Evangellos Felekouras
- First Department of Surgery, National and Kapodistrian University of Athens, Laikon General Hospital, Athens, Greece
| | - Dimitrios Schizas
- First Department of Surgery, National and Kapodistrian University of Athens, Laikon General Hospital, Athens, Greece
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Espinosa Reyes JA, Puerta Romero M, Cobo R, Heredia N, Solís Ruiz LA, Corredor Zuluaga DA. Artificial Intelligence in Facial Plastic and Reconstructive Surgery: A Systematic Review. Facial Plast Surg 2024. [PMID: 37992752 DOI: 10.1055/a-2216-5099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2023] Open
Abstract
Artificial intelligence (AI) is a technology that is evolving rapidly and is changing the world and medicine as we know it. After reviewing the PROSPERO database of systematic reviews, there is no article related to this topic in facial plastic and reconstructive surgery. The objective of this article was to review the literature regarding AI applications in facial plastic and reconstructive surgery.A systematic review of the literature about AI in facial plastic and reconstructive surgery using the following keywords: Artificial Intelligence, robotics, plastic surgery procedures, and surgery plastic and the following databases: PubMed, SCOPUS, Embase, BVS, and LILACS. The inclusion criteria were articles about AI in facial plastic and reconstructive surgery. Articles written in a language other than English and Spanish were excluded. In total, 17 articles about AI in facial plastic met the inclusion criteria; after eliminating the duplicated papers and applying the exclusion criteria, these articles were reviewed thoroughly. The leading type of AI used in these articles was computer vision, explicitly using models of convolutional neural networks to objectively compare the preoperative with the postoperative state in multiple interventions such as facial lifting and facial transgender surgery.In conclusion, AI is a rapidly evolving technology, and it could significantly impact the treatment of patients in facial plastic and reconstructive surgery. Legislation and regulations are developing slower than this technology. It is imperative to learn about this topic as soon as possible and that all stakeholders proactively promote discussions about ethical and regulatory dilemmas.
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Affiliation(s)
- Jorge Alberto Espinosa Reyes
- Department of Otolaryngology and Facial Plastic & Reconstructive Surgery, The Face & Nose Institute, Private Practice Clínica INO, Bogotá, DC, Colombia
| | - Mauricio Puerta Romero
- Department of Otolaryngology and Facial Plastic & Reconstructive Surgery, Private Practice Clínica Sebastían de Belalcázar, Cali, Valle del Cauca, Colombia
| | - Roxana Cobo
- Department of Otolaryngology and Facial Plastic & Reconstructive Surgery, The Face & Nose Institute, Private Practice at Clínica Imbanaco, Cali, Valle del Cauca Colombia
| | - Nicolas Heredia
- Department of Otolaryngology and Facial Plastic & Reconstructive Surgery, The Face & Nose Institute, Bogotá, D.C, Colombia
| | - Luis Alberto Solís Ruiz
- Department of Otolaryngology and Facial Plastic & Reconstructive Surgery, Private Practice, Chihuahua, Chihuahua, México
| | - Diego Andres Corredor Zuluaga
- Department of Otolaryngology and Facial Plastic & Reconstructive Surgery, Private Practice, Pereira, Risaralda, Colombia
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Eckhoff JA, Meireles O. Could Artificial Intelligence guide surgeons' hands? Rev Col Bras Cir 2023; 50:e20233696EDIT01. [PMID: 38088637 PMCID: PMC10668586 DOI: 10.1590/0100-6991e-20233696edit01-en] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 11/23/2023] [Indexed: 12/18/2023] Open
Affiliation(s)
- Jennifer A. Eckhoff
- - Harvard Medical School, Surgical Artificial Intelligence and Innovation Laboratory, Massachusetts General Hospital - Boston - MA - Estados Unidos
| | - Ozanan Meireles
- - Harvard Medical School, Surgical Artificial Intelligence and Innovation Laboratory, Massachusetts General Hospital - Boston - MA - Estados Unidos
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Nyangoh Timoh K, Huaulme A, Cleary K, Zaheer MA, Lavoué V, Donoho D, Jannin P. A systematic review of annotation for surgical process model analysis in minimally invasive surgery based on video. Surg Endosc 2023:10.1007/s00464-023-10041-w. [PMID: 37157035 DOI: 10.1007/s00464-023-10041-w] [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: 10/16/2022] [Accepted: 03/25/2023] [Indexed: 05/10/2023]
Abstract
BACKGROUND Annotated data are foundational to applications of supervised machine learning. However, there seems to be a lack of common language used in the field of surgical data science. The aim of this study is to review the process of annotation and semantics used in the creation of SPM for minimally invasive surgery videos. METHODS For this systematic review, we reviewed articles indexed in the MEDLINE database from January 2000 until March 2022. We selected articles using surgical video annotations to describe a surgical process model in the field of minimally invasive surgery. We excluded studies focusing on instrument detection or recognition of anatomical areas only. The risk of bias was evaluated with the Newcastle Ottawa Quality assessment tool. Data from the studies were visually presented in table using the SPIDER tool. RESULTS Of the 2806 articles identified, 34 were selected for review. Twenty-two were in the field of digestive surgery, six in ophthalmologic surgery only, one in neurosurgery, three in gynecologic surgery, and two in mixed fields. Thirty-one studies (88.2%) were dedicated to phase, step, or action recognition and mainly relied on a very simple formalization (29, 85.2%). Clinical information in the datasets was lacking for studies using available public datasets. The process of annotation for surgical process model was lacking and poorly described, and description of the surgical procedures was highly variable between studies. CONCLUSION Surgical video annotation lacks a rigorous and reproducible framework. This leads to difficulties in sharing videos between institutions and hospitals because of the different languages used. There is a need to develop and use common ontology to improve libraries of annotated surgical videos.
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Affiliation(s)
- Krystel Nyangoh Timoh
- Department of Gynecology and Obstetrics and Human Reproduction, CHU Rennes, Rennes, France.
- INSERM, LTSI - UMR 1099, University Rennes 1, Rennes, France.
- Laboratoire d'Anatomie et d'Organogenèse, Faculté de Médecine, Centre Hospitalier Universitaire de Rennes, 2 Avenue du Professeur Léon Bernard, 35043, Rennes Cedex, France.
- Department of Obstetrics and Gynecology, Rennes Hospital, Rennes, France.
| | - Arnaud Huaulme
- INSERM, LTSI - UMR 1099, University Rennes 1, Rennes, France
| | - Kevin Cleary
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC, 20010, USA
| | - Myra A Zaheer
- George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Vincent Lavoué
- Department of Gynecology and Obstetrics and Human Reproduction, CHU Rennes, Rennes, France
| | - Dan Donoho
- Division of Neurosurgery, Center for Neuroscience, Children's National Hospital, Washington, DC, 20010, USA
| | - Pierre Jannin
- INSERM, LTSI - UMR 1099, University Rennes 1, Rennes, France
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de Souza LL, Fonseca FP, Araújo ALD, Lopes MA, Vargas PA, Khurram SA, Kowalski LP, Dos Santos HT, Warnakulasuriya S, Dolezal J, Pearson AT, Santos-Silva AR. Machine learning for detection and classification of oral potentially malignant disorders: A conceptual review. J Oral Pathol Med 2023; 52:197-205. [PMID: 36792771 DOI: 10.1111/jop.13414] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 12/09/2022] [Indexed: 02/17/2023]
Abstract
Oral potentially malignant disorders represent precursor lesions that may undergo malignant transformation to oral cancer. There are many known risk factors associated with the development of oral potentially malignant disorders, and contribute to the risk of malignant transformation. Although many advances have been reported to understand the biological behavior of oral potentially malignant disorders, their clinical features that indicate the characteristics of malignant transformation are not well established. Early diagnosis of malignancy is the most important factor to improve patients' prognosis. The integration of machine learning into routine diagnosis has recently emerged as an adjunct to aid clinical examination. Increased performances of artificial intelligence AI-assisted medical devices are claimed to exceed the human capability in the clinical detection of early cancer. Therefore, the aim of this narrative review is to introduce artificial intelligence terminology, concepts, and models currently used in oncology to familiarize oral medicine scientists with the language skills, best research practices, and knowledge for developing machine learning models applied to the clinical detection of oral potentially malignant disorders.
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Affiliation(s)
- Lucas Lacerda de Souza
- Oral Diagnosis, Piracicaba Dental School, University of Campinas (UNICAMP), São Paulo, Brazil
| | - Felipe Paiva Fonseca
- Oral Diagnosis, Piracicaba Dental School, University of Campinas (UNICAMP), São Paulo, Brazil
- Department of Oral Surgery and Pathology, School of Dentistry, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | - Marcio Ajudarte Lopes
- Oral Diagnosis, Piracicaba Dental School, University of Campinas (UNICAMP), São Paulo, Brazil
| | - Pablo Agustin Vargas
- Oral Diagnosis, Piracicaba Dental School, University of Campinas (UNICAMP), São Paulo, Brazil
| | - Syed Ali Khurram
- Unit of Oral & Maxillofacial Pathology, School of Clinical Dentistry, University of Sheffield, Sheffield, UK
| | - Luiz Paulo Kowalski
- Department of Head and Neck Surgery, University of Sao Paulo Medical School and Department of Head and Neck Surgery and Otorhinolaryngology, AC Camargo Cancer Center, Sao Paulo, Brazil
| | - Harim Tavares Dos Santos
- Department of Otolaryngology-Head and Neck Surgery, University of Missouri, Columbia, Missouri, USA
- Department of Bond Life Sciences Center, University of Missouri, Columbia, Missouri, USA
| | - Saman Warnakulasuriya
- King's College London, London, UK
- WHO Collaborating Centre for Oral Cancer, London, UK
| | - James Dolezal
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, Illinois, USA
| | - Alexander T Pearson
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, Illinois, USA
| | - Alan Roger Santos-Silva
- Oral Diagnosis, Piracicaba Dental School, University of Campinas (UNICAMP), São Paulo, Brazil
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Artificial Intelligence in Surgical Learning. SURGERIES 2023. [DOI: 10.3390/surgeries4010010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023] Open
Abstract
(1) Background: Artificial Intelligence (AI) is transforming healthcare on all levels. While AI shows immense potential, the clinical implementation is lagging. We present a concise review of AI in surgical learning; (2) Methods: A non-systematic review of AI in surgical learning of the literature in English is provided; (3) Results: AI shows utility for all components of surgical competence within surgical learning. AI presents with great potential within robotic surgery specifically (4) Conclusions: Technology will evolve in ways currently unimaginable, presenting us with novel applications of AI and derivatives thereof. Surgeons must be open to new modes of learning to be able to implement all evidence-based applications of AI in the future. Systematic analyses of AI in surgical learning are needed.
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