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Zhang L, Wong C, Li Y, Huang T, Wang J, Lin C. Artificial intelligence assisted diagnosis of early tc markers and its application. Discov Oncol 2024; 15:172. [PMID: 38761260 PMCID: PMC11102422 DOI: 10.1007/s12672-024-01017-w] [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: 03/18/2024] [Accepted: 05/06/2024] [Indexed: 05/20/2024] Open
Abstract
Thyroid cancer (TC) is a common endocrine malignancy with an increasing incidence worldwide. Early diagnosis is particularly important for TC patients, because it allows patients to receive treatment as early as possible. Artificial intelligence (AI) provides great advantages for complex healthcare systems by analyzing big data based on machine learning. Nowadays, AI is widely used in the early diagnosis of cancer such as TC. Ultrasound detection and fine needle aspiration biopsy are the main methods for early diagnosis of TC. AI has been widely used in the detection of malignancy in thyroid nodules by ultrasound images, cytopathology images and molecular markers. It shows great potential in auxiliary medical diagnosis. The latest clinical trial has shown that the performance of AI models matches with the diagnostic efficiency of experienced clinicians, and more efficient AI tools will be developed in the future. Therefore, in this review, we summarized the recent advances in the application of AI algorithms in assessing the risk of malignancy in thyroid nodules. The objective of this review was to provide a data base for the clinical use of AI-assisted diagnosis in TC, as well as to provide new ideas for the next generation of AI-assisted diagnosis in TC.
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Affiliation(s)
- Laney Zhang
- Yale School of Public Health, New Haven, CT, USA
| | - Chinting Wong
- Department of Nuclear Medicine, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Yungeng Li
- Department of Nuclear Medicine, The First Hospital of Jilin University, Changchun, Jilin, China
| | | | - Jiawen Wang
- Department of Nuclear Medicine, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Chenghe Lin
- Department of Nuclear Medicine, The First Hospital of Jilin University, Changchun, Jilin, China.
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Belkouchi Y, Lederlin M, Ben Afia A, Fabre C, Ferretti G, De Margerie C, Berge P, Liberge R, Elbaz N, Blain M, Brillet PY, Chassagnon G, Cadour F, Caramella C, Hajjam ME, Boussouar S, Hadchiti J, Fablet X, Khalil A, Luciani A, Cotten A, Meder JF, Talbot H, Lassau N. Detection and quantification of pulmonary embolism with artificial intelligence: The SFR 2022 artificial intelligence data challenge. Diagn Interv Imaging 2023; 104:485-489. [PMID: 37321875 DOI: 10.1016/j.diii.2023.05.007] [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: 05/25/2023] [Revised: 05/29/2023] [Accepted: 05/31/2023] [Indexed: 06/17/2023]
Abstract
PURPOSE In 2022, the French Society of Radiology together with the French Society of Thoracic Imaging and CentraleSupelec organized their 13th data challenge. The aim was to aid in the diagnosis of pulmonary embolism, by identifying the presence of pulmonary embolism and by estimating the ratio between right and left ventricular (RV/LV) diameters, and an arterial obstruction index (Qanadli's score) using artificial intelligence. MATERIALS AND METHODS The data challenge was composed of three tasks: the detection of pulmonary embolism, the RV/LV diameter ratio, and Qanadli's score. Sixteen centers all over France participated in the inclusion of the cases. A health data hosting certified web platform was established to facilitate the inclusion process of the anonymized CT examinations in compliance with general data protection regulation. CT pulmonary angiography images were collected. Each center provided the CT examinations with their annotations. A randomization process was established to pool the scans from different centers. Each team was required to have at least a radiologist, a data scientist, and an engineer. Data were provided in three batches to the teams, two for training and one for evaluation. The evaluation of the results was determined to rank the participants on the three tasks. RESULTS A total of 1268 CT examinations were collected from the 16 centers following the inclusion criteria. The dataset was split into three batches of 310, 580 and 378 C T examinations provided to the participants respectively on September 5, 2022, October 7, 2022 and October 9, 2022. Seventy percent of the data from each center were used for training, and 30% for the evaluation. Seven teams with a total of 48 participants including data scientists, researchers, radiologists and engineering students were registered for participation. The metrics chosen for evaluation included areas under receiver operating characteristic curves, specificity and sensitivity for the classification task, and the coefficient of determination r2 for the regression tasks. The winning team achieved an overall score of 0.784. CONCLUSION This multicenter study suggests that the use of artificial intelligence for the diagnosis of pulmonary embolism is possible on real data. Moreover, providing quantitative measures is mandatory for the interpretability of the results, and is of great aid to the radiologists especially in emergency settings.
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Affiliation(s)
- Younes Belkouchi
- OPIS, CentraleSupelec, Inria, Université Paris-Saclay, 91190 Gif-Sur-Yvette, France; Laboratoire d'Imagerie Biomédicale Multimodale Paris-Saclay, BIOMAPS, UMR 1281, Université Paris-Saclay, Inserm, CNRS, CEA, 94800 Villejuif, France.
| | | | - Amira Ben Afia
- Department of Radiology, APHP Nord, Hôpital Bichat, 75018 Paris, France; Université Paris Cité, 75006 Paris, France
| | - Clement Fabre
- Department of Radiology, Centre Hospitalier de Laval, 53000 Laval, France
| | - Gilbert Ferretti
- Universite Grenobles Alpes, Service de Radiologie et Imagerie Médicale, CHU Grenoble-Alpes, 38000 Grenoble, France
| | - Constance De Margerie
- Department of Radiology, Assistance Publique-Hôpitaux de Paris, Hôpital Saint-Louis, 75010 Paris, France; Université Paris Cité, 75006 Paris, France
| | - Pierre Berge
- Department of Radiology, CHU Angers, 49000 Angers, France
| | - Renan Liberge
- Department of Radiology, CHU Nantes, 44000 Nantes, France
| | - Nicolas Elbaz
- Department of Radiology, Hôpital Européen Georges Pompidou, AP-HP, 75015 Paris, France
| | - Maxime Blain
- Department of Radiology, Hopital Henri Mondor, AP-HP, 94000 Créteil, France
| | - Pierre-Yves Brillet
- Department of Radiology, Hôpital Avicenne, Paris 13 University, 93000 Bobigny, France
| | - Guillaume Chassagnon
- Department of Radiology, Hopital Cochin, APHP, 75014 Paris, France; Université Paris Cité, 75006 Paris, France
| | - Farah Cadour
- APHM, Hôpital Universitaire Timone, CEMEREM, 13005 Marseille, France
| | - Caroline Caramella
- Department of Radiology, Groupe hospitalier Paris Saint-Joseph, Île-de-France, 75015 Paris, France
| | - Mostafa El Hajjam
- Department of Radiology, Ambroise Paré Hospital GH AP-HP Paris Saclay, UMR 1179 INSERM/UVSQ, Team 3, 92100 Boulogne-Billancourt, France
| | - Samia Boussouar
- Sorbonne Université, APHP, Hôpital La Pitié-Salpêtrière, Unité d'Imagerie Cardiovasculaire et Thoracique (ICT), 75013 Paris, France
| | - Joya Hadchiti
- Department of Imaging, Institut Gustave Roussy, 94800 Villejuif, France
| | - Xavier Fablet
- Department of Radiology, CHU Rennes, 35000 Rennes, France
| | - Antoine Khalil
- Department of Radiology, APHP Nord, Hôpital Bichat, 75018 Paris, France; Université Paris Cité, 75006 Paris, France
| | - Alain Luciani
- Medical Imaging Department, AP-HP, Henri Mondor University Hospital, 94000 Créteil, France; INSERM, U955, Team 18, 94000 Créteil, France
| | - Anne Cotten
- Department of Musculoskeletal Radiology, Univ. Lille, CHU Lille, MABlab ULR 4490, 59000 Lille, France
| | - Jean-Francois Meder
- Department of Neuroimaging, Sainte-Anne Hospital, 75013 Paris, France; Université Paris Cité, 75006 Paris, France
| | - Hugues Talbot
- OPIS, CentraleSupelec, Inria, Université Paris-Saclay, 91190 Gif-Sur-Yvette, France
| | - Nathalie Lassau
- Laboratoire d'Imagerie Biomédicale Multimodale Paris-Saclay, BIOMAPS, UMR 1281, Université Paris-Saclay, Inserm, CNRS, CEA, 94800 Villejuif, France; Department of Imaging, Institut Gustave Roussy, 94800 Villejuif, France
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Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review. Diagnostics (Basel) 2022; 12:diagnostics12123111. [PMID: 36553119 PMCID: PMC9777253 DOI: 10.3390/diagnostics12123111] [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: 11/14/2022] [Revised: 12/07/2022] [Accepted: 12/08/2022] [Indexed: 12/14/2022] Open
Abstract
Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a "one-stop center" synthesis and provide a holistic bird's eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest.
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