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Turtoi DC, Brata VD, Incze V, Ismaiel A, Dumitrascu DI, Militaru V, Munteanu MA, Botan A, Toc DA, Duse TA, Popa SL. Artificial Intelligence for the Automatic Diagnosis of Gastritis: A Systematic Review. J Clin Med 2024; 13:4818. [PMID: 39200959 PMCID: PMC11355427 DOI: 10.3390/jcm13164818] [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: 07/23/2024] [Revised: 08/11/2024] [Accepted: 08/13/2024] [Indexed: 09/02/2024] Open
Abstract
Background and Objective: Gastritis represents one of the most prevalent gastrointestinal diseases and has a multifactorial etiology, many forms of manifestation, and various symptoms. Diagnosis of gastritis is made based on clinical, endoscopic, and histological criteria, and although it is a thorough process, many cases are misdiagnosed or overlooked. This systematic review aims to provide an extensive overview of current artificial intelligence (AI) applications in gastritis diagnosis and evaluate the precision of these systems. This evaluation could highlight the role of AI as a helpful and useful tool in facilitating timely and accurate diagnoses, which in turn could improve patient outcomes. Methods: We have conducted an extensive and comprehensive literature search of PubMed, Scopus, and Web of Science, including studies published until July 2024. Results: Despite variations in study design, participant numbers and characteristics, and outcome measures, our observations suggest that implementing an AI automatic diagnostic tool into clinical practice is currently feasible, with the current systems achieving high levels of accuracy, sensitivity, and specificity. Our findings indicate that AI outperformed human experts in most studies, with multiple studies exhibiting an accuracy of over 90% for AI compared to human experts. These results highlight the significant potential of AI to enhance diagnostic accuracy and efficiency in gastroenterology. Conclusions: AI-based technologies can now automatically diagnose using images provided by gastroscopy, digital pathology, and radiology imaging. Deep learning models exhibited high levels of accuracy, sensitivity, and specificity while assessing the diagnosis, staging, and risk of neoplasia for different types of gastritis, results that are superior to those of human experts in most studies.
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Affiliation(s)
- Daria Claudia Turtoi
- Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.I.); (A.B.); (T.A.D.)
| | - Vlad Dumitru Brata
- Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.I.); (A.B.); (T.A.D.)
| | - Victor Incze
- Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.I.); (A.B.); (T.A.D.)
| | - Abdulrahman Ismaiel
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (A.I.); (S.L.P.)
| | - Dinu Iuliu Dumitrascu
- Department of Anatomy, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania;
| | - Valentin Militaru
- Department of Internal Medicine, Clinical Municipal Hospital, 400139 Cluj-Napoca, Romania;
| | - Mihai Alexandru Munteanu
- Department of Medical Disciplines, Faculty of Medicine and Pharmacy, University of Oradea, 410087 Oradea, Romania;
| | - Alexandru Botan
- Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.I.); (A.B.); (T.A.D.)
| | - Dan Alexandru Toc
- Department of Microbiology, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania;
| | - Traian Adrian Duse
- Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.I.); (A.B.); (T.A.D.)
| | - Stefan Lucian Popa
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (A.I.); (S.L.P.)
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Liu X, Li F, Xu J, Ma J, Duan X, Mao R, Chen M, Chen Z, Huang Y, Jiang J, Huang B, Ye Z. Deep learning model to differentiate Crohn's disease from intestinal tuberculosis using histopathological whole slide images from intestinal specimens. Virchows Arch 2024; 484:965-976. [PMID: 38332051 DOI: 10.1007/s00428-024-03740-9] [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] [Received: 11/13/2023] [Revised: 12/29/2023] [Accepted: 01/13/2024] [Indexed: 02/10/2024]
Abstract
Crohn's disease (CD) and intestinal tuberculosis (ITB) share similar histopathological characteristics, and differential diagnosis can be a dilemma for pathologists. This study aimed to apply deep learning (DL) to analyze whole slide images (WSI) of surgical resection specimens to distinguish CD from ITB. Overall, 1973 WSI from 85 cases from 3 centers were obtained. The DL model was established in internal training and validated in external test cohort, evaluated by area under receiver operator characteristic curve (AUC). Diagnostic results of pathologists were compared with those of the DL model using DeLong's test. DL model had case level AUC of 0.886, 0.893 and slide level AUC of 0.954, 0.827 in training and test cohorts. Attention maps highlighted discriminative areas and top 10 features were extracted from CD and ITB. DL model's diagnostic efficiency (AUC = 0.886) was better than junior pathologists (*1 AUC = 0.701, P = 0.088; *2 AUC = 0.861, P = 0.788) and inferior to senior GI pathologists (*3 AUC = 0.910, P = 0.800; *4 AUC = 0.946, P = 0.507) in training cohort. In the test cohort, model (AUC = 0.893) outperformed senior non-GI pathologists (*5 AUC = 0.782, P = 0.327; *6 AUC = 0.821, P = 0.516). We developed a DL model for the classification of CD and ITB, improving pathological diagnosis accuracy effectively.
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Affiliation(s)
- Xinning Liu
- Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, No. 58 Zhongshan Road 2, Guangzhou, 510080, Guangdong, People's Republic of China
- Department of Pathology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Fei Li
- Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen, 518000, Guangdong, People's Republic of China
| | - Jie Xu
- Department of Pathology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, People's Republic of China
| | - Jinting Ma
- Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen, 518000, Guangdong, People's Republic of China
| | - Xiaoyu Duan
- Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, No. 58 Zhongshan Road 2, Guangzhou, 510080, Guangdong, People's Republic of China
| | - Ren Mao
- Department of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Minhu Chen
- Department of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Zhihui Chen
- Department of Gastrointestinal and Pancreatic Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Yan Huang
- Department of Pathology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Jingyi Jiang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Bingsheng Huang
- Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen, 518000, Guangdong, People's Republic of China.
| | - Ziyin Ye
- Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, No. 58 Zhongshan Road 2, Guangzhou, 510080, Guangdong, People's Republic of China.
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Ibrahim AU, Dirilenoğlu F, Hacisalihoğlu UP, Ilhan A, Mirzaei O. Classification of H. pylori Infection from Histopathological Images Using Deep Learning. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1177-1186. [PMID: 38332407 PMCID: PMC11169399 DOI: 10.1007/s10278-024-01021-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 12/09/2023] [Accepted: 12/28/2023] [Indexed: 02/10/2024]
Abstract
Helicobacter pylori (H. pylori) is a widespread pathogenic bacterium, impacting over 4 billion individuals globally. It is primarily linked to gastric diseases, including gastritis, peptic ulcers, and cancer. The current histopathological method for diagnosing H. pylori involves labour-intensive examination of endoscopic biopsies by trained pathologists. However, this process can be time-consuming and may occasionally result in the oversight of small bacterial quantities. Our study explored the potential of five pre-trained models for binary classification of 204 histopathological images, distinguishing between H. pylori-positive and H. pylori-negative cases. These models include EfficientNet-b0, DenseNet-201, ResNet-101, MobileNet-v2, and Xception. To evaluate the models' performance, we conducted a five-fold cross-validation, ensuring the models' reliability across different subsets of the dataset. After extensive evaluation and comparison of the models, ResNet101 emerged as the most promising. It achieved an average accuracy of 0.920, with impressive scores for sensitivity, specificity, positive predictive value, negative predictive value, F1 score, Matthews's correlation coefficient, and Cohen's kappa coefficient. Our study achieved these robust results using a smaller dataset compared to previous studies, highlighting the efficacy of deep learning models even with limited data. These findings underscore the potential of deep learning models, particularly ResNet101, to support pathologists in achieving precise and dependable diagnostic procedures for H. pylori. This is particularly valuable in scenarios where swift and accurate diagnoses are essential.
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Affiliation(s)
- Abdullahi Umar Ibrahim
- Department of Biomedical Engineering, Faculty of Engineering, Near East University, Nicosia, Cyprus.
- Research Centre for Science, Technology and Engineering (BILTEM), Near East University, Nicosia, Cyprus.
| | - Fikret Dirilenoğlu
- Department of Pathology, Faculty of Medicine, Near East University, Nicosia, Cyprus
| | | | - Ahmet Ilhan
- Research Centre for Science, Technology and Engineering (BILTEM), Near East University, Nicosia, Cyprus
- Department of Computer Engineering, Applied Artificial Intelligence Research Centre, Near East University, Nicosia, Cyprus
| | - Omid Mirzaei
- Department of Biomedical Engineering, Faculty of Engineering, Near East University, Nicosia, Cyprus
- Research Centre for Science, Technology and Engineering (BILTEM), Near East University, Nicosia, Cyprus
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Alsulimani A, Akhter N, Jameela F, Ashgar RI, Jawed A, Hassani MA, Dar SA. The Impact of Artificial Intelligence on Microbial Diagnosis. Microorganisms 2024; 12:1051. [PMID: 38930432 PMCID: PMC11205376 DOI: 10.3390/microorganisms12061051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 05/19/2024] [Accepted: 05/21/2024] [Indexed: 06/28/2024] Open
Abstract
Traditional microbial diagnostic methods face many obstacles such as sample handling, culture difficulties, misidentification, and delays in determining susceptibility. The advent of artificial intelligence (AI) has markedly transformed microbial diagnostics with rapid and precise analyses. Nonetheless, ethical considerations accompany AI adoption, necessitating measures to uphold patient privacy, mitigate biases, and ensure data integrity. This review examines conventional diagnostic hurdles, stressing the significance of standardized procedures in sample processing. It underscores AI's significant impact, particularly through machine learning (ML), in microbial diagnostics. Recent progressions in AI, particularly ML methodologies, are explored, showcasing their influence on microbial categorization, comprehension of microorganism interactions, and augmentation of microscopy capabilities. This review furnishes a comprehensive evaluation of AI's utility in microbial diagnostics, addressing both advantages and challenges. A few case studies including SARS-CoV-2, malaria, and mycobacteria serve to illustrate AI's potential for swift and precise diagnosis. Utilization of convolutional neural networks (CNNs) in digital pathology, automated bacterial classification, and colony counting further underscores AI's versatility. Additionally, AI improves antimicrobial susceptibility assessment and contributes to disease surveillance, outbreak forecasting, and real-time monitoring. Despite a few limitations, integration of AI in diagnostic microbiology presents robust solutions, user-friendly algorithms, and comprehensive training, promising paradigm-shifting advancements in healthcare.
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Affiliation(s)
- Ahmad Alsulimani
- Medical Laboratory Technology Department, College of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia; (A.A.); (M.A.H.)
| | - Naseem Akhter
- Department of Biology, Arizona State University, Lake Havasu City, AZ 86403, USA;
| | - Fatima Jameela
- Modern American Dental Clinic, West Warren Avenue, Dearborn, MI 48126, USA;
| | - Rnda I. Ashgar
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
| | - Arshad Jawed
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
| | - Mohammed Ahmed Hassani
- Medical Laboratory Technology Department, College of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia; (A.A.); (M.A.H.)
| | - Sajad Ahmad Dar
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
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Yilmaz F, Brickman A, Najdawi F, Yakirevich E, Egger R, Resnick MB. Advancing Artificial Intelligence Integration Into the Pathology Workflow: Exploring Opportunities in Gastrointestinal Tract Biopsies. J Transl Med 2024; 104:102043. [PMID: 38431118 DOI: 10.1016/j.labinv.2024.102043] [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: 10/30/2023] [Revised: 02/14/2024] [Accepted: 02/26/2024] [Indexed: 03/05/2024] Open
Abstract
This review aims to present a comprehensive overview of the current landscape of artificial intelligence (AI) applications in the analysis of tubular gastrointestinal biopsies. These publications cover a spectrum of conditions, ranging from inflammatory ailments to malignancies. Moving beyond the conventional diagnosis based on hematoxylin and eosin-stained whole-slide images, the review explores additional implications of AI, including its involvement in interpreting immunohistochemical results, molecular subtyping, and the identification of cellular spatial biomarkers. Furthermore, the review examines how AI can contribute to enhancing the quality and control of diagnostic processes, introducing new workflow options, and addressing the limitations and caveats associated with current AI platforms in this context.
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Affiliation(s)
- Fazilet Yilmaz
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Arlen Brickman
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Fedaa Najdawi
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Evgeny Yakirevich
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | | | - Murray B Resnick
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island.
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Yousif M, Pantanowitz L. Artificial Intelligence-Enabled Gastric Cancer Interpretations: Are We There yet? Surg Pathol Clin 2023; 16:673-686. [PMID: 37863559 DOI: 10.1016/j.path.2023.05.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2023]
Abstract
The integration of digital pathology and artificial intelligence (AI) is revolutionizing pathology by providing pathologists with new tools to improve workflow, enhance diagnostic accuracy, and undertake novel discovery. The capability of AI to recognize patterns and features in digital images beyond human perception is particularly valuable, thereby providing additional information for prognostic and predictive purposes. AI-based tools diagnose gastric carcinoma in digital images, detect gastric carcinoma metastases in lymph nodes, automate Ki-67 scoring in gastric neuroendocrine tumors, and quantify tumor-infiltrating lymphocytes. This article provides an overview of all of these applications of AI pertaining to gastric cancer.
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Affiliation(s)
- Mustafa Yousif
- Department of Pathology, University of Michigan, NCRC Building 35, 2800 Plymouth Road, Ann Arbor, MI 48109, USA.
| | - Liron Pantanowitz
- Department of Pathology, UPMC Shadyside Hospital, 5150 Centre Avenue Cancer Pavilion, POB2, Suite 3B, Room 347, Pittsburgh, PA 15232, USA
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Lin YJ, Chen CC, Lee CH, Yeh CY, Jeng YM. Two-tiered deep-learning-based model for histologic diagnosis of Helicobacter gastritis. Histopathology 2023; 83:771-781. [PMID: 37522271 DOI: 10.1111/his.15018] [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] [Received: 03/31/2023] [Revised: 07/11/2023] [Accepted: 07/15/2023] [Indexed: 08/01/2023]
Abstract
AIMS Helicobacter pylori (HP) infection is the most common cause of chronic gastritis worldwide. Due to the small size of HP and limited resolution, diagnosing HP infections is more difficult when using digital slides. METHODS AND RESULTS We developed a two-tier deep-learning-based model for diagnosing HP gastritis. A whole-slide model was trained on 885 whole-slide images (WSIs) with only slide-level labels (positive or negative slides). An auxiliary model was trained on 824 areas with HP in nine positive WSIs and 446 negative WSIs for localizing HP. The whole-slide model performed well, with an area under the receiver operating characteristic curve (AUC) of 0.9739 (95% confidence interval [CI], 0.9545-0.9932). The calculated sensitivity and specificity were 93.3% and 90.1%, respectively, whereas those of pathologists were 93.3% and 84.2%, respectively. Using the auxiliary model, the highlighted areas of the localization maps had an average precision of 0.5796. CONCLUSIONS HP gastritis can be diagnosed on haematoxylin-and-eosin-stained WSIs with human-level accuracy using a deep-learning-based model trained on slide-level labels and an auxiliary model for localizing HP and confirming the diagnosis. This two-tiered model can shorten the diagnostic process and reduce the need for special staining.
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Affiliation(s)
- Yi-Jyun Lin
- Department of Pathology, National Taiwan University Hospital, Taipei, Taiwan
| | | | - Chia-Hsiang Lee
- Department of Pathology, National Taiwan University Hospital, Taipei, Taiwan
| | | | - Yung-Ming Jeng
- Department of Pathology, National Taiwan University Hospital, Taipei, Taiwan
- Graduate Institute of Pathology, College of Medicine, National Taiwan University, Taipei, Taiwan
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Marletta S, L'Imperio V, Eccher A, Antonini P, Santonicco N, Girolami I, Dei Tos AP, Sbaraglia M, Pagni F, Brunelli M, Marino A, Scarpa A, Munari E, Fusco N, Pantanowitz L. Artificial intelligence-based tools applied to pathological diagnosis of microbiological diseases. Pathol Res Pract 2023; 243:154362. [PMID: 36758417 DOI: 10.1016/j.prp.2023.154362] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 02/02/2023] [Accepted: 02/04/2023] [Indexed: 02/09/2023]
Abstract
Infectious diseases still threaten the global community, especially in resource-limited countries. An accurate diagnosis is paramount to proper patient and public health management. Identification of many microbes still relies on manual microscopic examination, a time-consuming process requiring skilled staff. Thus, artificial intelligence (AI) has been exploited for identification of microorganisms. A systematic search was carried out using electronic databases looking for studies dealing with the application of AI to pathology microbiology specimens. Of 4596 retrieved articles, 110 were included. The main applications of AI regarded malaria (54 studies), bacteria (28), nematodes (14), and other protozoa (11). Most publications examined cytological material (95, 86%), mainly analyzing images acquired through microscope cameras (65, 59%) or coupled with smartphones (16, 15%). Various deep-learning strategies were used for the analysis of digital images, achieving highly satisfactory results. The published evidence suggests that AI can be reliably utilized for assisting pathologists in the detection of microorganisms. Further technologic improvement and availability of datasets for training AI-based algorithms would help expand this field and widen its adoption, especially for developing countries.
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Affiliation(s)
- Stefano Marletta
- Department of Diagnostic and Public Health, Section of Pathology, University of Verona, Verona, Italy; Department of Pathology, Pederzoli Hospital, Peschiera del Garda, Italy
| | - Vincenzo L'Imperio
- Department of Medicine and Surgery, ASST Monza, San Gerardo Hospital, University of Milano-Bicocca, Monza, Italy
| | - Albino Eccher
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy.
| | - Pietro Antonini
- Department of Diagnostic and Public Health, Section of Pathology, University of Verona, Verona, Italy
| | - Nicola Santonicco
- Department of Diagnostic and Public Health, Section of Pathology, University of Verona, Verona, Italy
| | - Ilaria Girolami
- Division of Pathology, Bolzano Central Hospital, Bolzano, Italy
| | - Angelo Paolo Dei Tos
- Surgical Pathology & Cytopathology Unit, Department of Medicine - DIMED, University of Padua, Padua, Italy
| | - Marta Sbaraglia
- Surgical Pathology & Cytopathology Unit, Department of Medicine - DIMED, University of Padua, Padua, Italy
| | - Fabio Pagni
- Department of Medicine and Surgery, ASST Monza, San Gerardo Hospital, University of Milano-Bicocca, Monza, Italy
| | - Matteo Brunelli
- Department of Diagnostic and Public Health, Section of Pathology, University of Verona, Verona, Italy
| | - Andrea Marino
- Unit of Infectious Diseases, Department of Clinical and Experimental Medicine, ARNAS Garibaldi Hospital, University of Catania, Catania, Italy
| | - Aldo Scarpa
- Department of Diagnostic and Public Health, Section of Pathology, University of Verona, Verona, Italy
| | - Enrico Munari
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Nicola Fusco
- Division of Pathology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Liron Pantanowitz
- Department of Pathology & Clinical Labs, University of Michigan, Ann Arbor, MI, United States
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Hamza A, Khan MA, Alhaisoni M, Al Hejaili A, Shaban KA, Alsubai S, Alasiry A, Marzougui M. D 2BOF-COVIDNet: A Framework of Deep Bayesian Optimization and Fusion-Assisted Optimal Deep Features for COVID-19 Classification Using Chest X-ray and MRI Scans. Diagnostics (Basel) 2022; 13:101. [PMID: 36611393 PMCID: PMC9818184 DOI: 10.3390/diagnostics13010101] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/23/2022] [Accepted: 12/24/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND AND OBJECTIVE In 2019, a corona virus disease (COVID-19) was detected in China that affected millions of people around the world. On 11 March 2020, the WHO declared this disease a pandemic. Currently, more than 200 countries in the world have been affected by this disease. The manual diagnosis of this disease using chest X-ray (CXR) images and magnetic resonance imaging (MRI) is time consuming and always requires an expert person; therefore, researchers introduced several computerized techniques using computer vision methods. The recent computerized techniques face some challenges, such as low contrast CTX images, the manual initialization of hyperparameters, and redundant features that mislead the classification accuracy. METHODS In this paper, we proposed a novel framework for COVID-19 classification using deep Bayesian optimization and improved canonical correlation analysis (ICCA). In this proposed framework, we initially performed data augmentation for better training of the selected deep models. After that, two pre-trained deep models were employed (ResNet50 and InceptionV3) and trained using transfer learning. The hyperparameters of both models were initialized through Bayesian optimization. Both trained models were utilized for feature extractions and fused using an ICCA-based approach. The fused features were further optimized using an improved tree growth optimization algorithm that finally was classified using a neural network classifier. RESULTS The experimental process was conducted on five publically available datasets and achieved an accuracy of 99.6, 98.5, 99.9, 99.5, and 100%. CONCLUSION The comparison with recent methods and t-test-based analysis showed the significance of this proposed framework.
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Affiliation(s)
- Ameer Hamza
- Department of Computer Science, HITEC University, Taxila 47080, Pakistan
| | | | - Majed Alhaisoni
- Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
| | - Abdullah Al Hejaili
- Faculty of Computers & Information Technology, Computer Science Department, University of Tabuk, Tabuk 71491, Saudi Arabia
| | - Khalid Adel Shaban
- Computer Science Department, College of Computing and Informatics, Saudi Electronic University, Riyadh 11673, Saudi Arabia
| | - Shtwai Alsubai
- College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia
| | - Areej Alasiry
- College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia
| | - Mehrez Marzougui
- College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia
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Kamada T, Maruyama Y, Monobe Y, Haruma K. Endoscopic features and clinical importance of autoimmune gastritis. Dig Endosc 2022; 34:700-713. [PMID: 34674318 DOI: 10.1111/den.14175] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 10/19/2021] [Accepted: 10/19/2021] [Indexed: 12/13/2022]
Abstract
Autoimmune gastritis (AIG) is a special type of chronic gastritis characterized by autoimmune disorders caused by cellular immunity, resulting in the destruction of parietal cells and production of antiparietal cell antibodies. Endoscopic findings of AIG are mainly characterized by corpus-dominant advanced atrophy. The antral area is generally considered to have no or mild atrophy; however, there are cases wherein the gastric mucosa is red or faded due to past infection with Helicobacter pylori or bile reflux. Currently, there are no diagnostic criteria for AIG in Japan, and it is important to make a diagnosis based on the presence of gastric autoantibodies and characteristic endoscopic and histological findings. AIG is associated with gastric cancer, neuroendocrine tumors (NETs), and other autoimmune diseases, such as thyroid diseases, anemia, and neurological symptoms due to impaired absorption of iron and vitamin B12 , and thus requires systemic treatment. The significance of diagnosing AIG is to include patients as a high-risk group for the development of gastric cancer and gastric NETs, provide an opportunity to detect autoimmune endocrine diseases, and initiate therapeutic intervention before anemia and neurological symptoms develop. It is important to pay close attention to the occurrence of AIG comorbidities not only at the time of AIG diagnosis but also during follow-up after detection.
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Affiliation(s)
- Tomoari Kamada
- Department of, Health Care Medicine, Kawasaki Medical School, Okayama, Japan
| | - Yasuhiko Maruyama
- Department of Gastroenterology, Fujieda Municipal General Hospital, Shizuoka, Japan
| | - Yasumasa Monobe
- Department of, Pathology, Kawasaki Medical School, Okayama, Japan
| | - Ken Haruma
- Department of, General Internal Medicine 2, Kawasaki Medical School, Okayama, Japan
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Deep Learning Facilitates Distinguishing Histologic Subtypes of Pulmonary Neuroendocrine Tumors on Digital Whole-Slide Images. Cancers (Basel) 2022; 14:cancers14071740. [PMID: 35406511 PMCID: PMC8996915 DOI: 10.3390/cancers14071740] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 03/13/2022] [Accepted: 03/25/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary Challenges persist in diagnosing pulmonary neuroendocrine tumors. Our case study shows that deep learning combined with convolutional neural networks has the potential to assist in the diagnosis of pulmonary neuroendocrine tumors from digital whole-slide images. Abstract The histological distinction of lung neuroendocrine carcinoma, including small cell lung carcinoma (SCLC), large cell neuroendocrine carcinoma (LCNEC) and atypical carcinoid (AC), can be challenging in some cases, while bearing prognostic and therapeutic significance. To assist pathologists with the differentiation of histologic subtyping, we applied a deep learning classifier equipped with a convolutional neural network (CNN) to recognize lung neuroendocrine neoplasms. Slides of primary lung SCLC, LCNEC and AC were obtained from the Laboratory of Clinical and Experimental Pathology (University Hospital Nice, France). Three thoracic pathologists blindly established gold standard diagnoses. The HALO-AI module (Indica Labs, UK) trained with 18,752 image tiles extracted from 60 slides (SCLC = 20, LCNEC = 20, AC = 20 cases) was then tested on 90 slides (SCLC = 26, LCNEC = 22, AC = 13 and combined SCLC with LCNEC = 4 cases; NSCLC = 25 cases) by F1-score and accuracy. A HALO-AI correct area distribution (AD) cutoff of 50% or more was required to credit the CNN with the correct diagnosis. The tumor maps were false colored and displayed side by side to original hematoxylin and eosin slides with superimposed pathologist annotations. The trained HALO-AI yielded a mean F1-score of 0.99 (95% CI, 0.939–0.999) on the testing set. Our CNN model, providing further larger validation, has the potential to work side by side with the pathologist to accurately differentiate between the different lung neuroendocrine carcinoma in challenging cases.
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Yang H, Yang WJ, Hu B. Gastric epithelial histology and precancerous conditions. World J Gastrointest Oncol 2022; 14:396-412. [PMID: 35317321 PMCID: PMC8919001 DOI: 10.4251/wjgo.v14.i2.396] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 11/08/2021] [Accepted: 01/05/2022] [Indexed: 02/06/2023] Open
Abstract
The most common histological type of gastric cancer (GC) is gastric adenocarcinoma arising from the gastric epithelium. Less common variants include mesenchymal, lymphoproliferative and neuroendocrine neoplasms. The Lauren scheme classifies GC into intestinal type, diffuse type and mixed type. The WHO classification includes papillary, tubular, mucinous, poorly cohesive and mixed GC. Chronic atrophic gastritis (CAG) and intestinal metaplasia are recommended as common precancerous conditions. No definite precancerous condition of diffuse/poorly/undifferentiated type is recommended. Chronic superficial inflammation and hyperplasia of foveolar cells may be the focus. Presently, the management of early GC and precancerous conditions mainly relies on endoscopy including diagnosis, treatment and surveillance. Management of precancerous conditions promotes the early detection and treatment of early GC, and even prevent the occurrence of GC. In the review, precancerous conditions including CAG, metaplasia, foveolar hyperplasia and gastric hyperplastic polyps derived from the gastric epithelium have been concluded, based on the overview of gastric epithelial histological organization and its renewal.
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Affiliation(s)
- Hang Yang
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Wen-Juan Yang
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bing Hu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
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