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Ciaccio EJ, Lee AR, Lebovits J, Wolf RL, Lewis SK. Physical and psychological symptoms and survey importance in celiac disease. World J Gastrointest Endosc 2024; 16:632-639. [PMID: 39735391 PMCID: PMC11669964 DOI: 10.4253/wjge.v16.i12.632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Revised: 09/20/2024] [Accepted: 10/24/2024] [Indexed: 12/12/2024] Open
Abstract
Celiac disease is an autoimmune condition that affects approximately 1% of the worldwide community. Originally thought to be confined mostly to the small intestine, resulting in villous atrophy and nutrient malabsorption, it has more recently been implicated in systemic manifestations as well, particularly when undiagnosed or left untreated. Herein, the physical and psychological symptoms of celiac disease are described and explored. An emphasis is placed on efforts to query prospective and confirmed celiac disease patients via the use of surveys. Suggestions are made regarding the development of efficacious surveys for the purpose of screening for celiac disease in undiagnosed persons, and monitoring efficacy of the gluten-free diet in persons diagnosed with celiac disease. There are broad categories of physical and psychological symptoms associated with celiac disease. There is also an essential interaction between such physical and the psychological symptoms. It is important to capture the association between symptoms, via queries directed toward suspected and confirmed persons with celiac disease. The use of anonymous online surveys can be helpful to determine the qualities and characteristics which may be associated with this condition. It is suggested that personal surveys should be given a greater role in screening and to lessen the time for diagnosis. Querying the subject directly via a survey can provide clues as to the types of symptoms being experienced by those with celiac disease currently, as well as to determine the salient aspects of the symptomatology, which will be useful for rapid screening and monitoring in future work.
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Affiliation(s)
- Edward J Ciaccio
- Celiac Disease Center at Columbia University Medical Center, Columbia University, New York, NY 10032, United States
| | - Anne R Lee
- Celiac Disease Center at Columbia University Medical Center, Columbia University, New York, NY 10032, United States
| | - Jessica Lebovits
- Celiac Disease Center at Columbia University Medical Center, Columbia University, New York, NY 10032, United States
| | - Randi L Wolf
- Department of Health Studies and Applied Educational Psychology, Columbia University, Teachers College, New York, NY 10027, United States
| | - Suzanne K Lewis
- Celiac Disease Center at Columbia University Medical Center, Columbia University, New York, NY 10032, United States
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2
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Ahamed MF, Shafi FB, Nahiduzzaman M, Ayari MA, Khandakar A. Interpretable deep learning architecture for gastrointestinal disease detection: A Tri-stage approach with PCA and XAI. Comput Biol Med 2024; 185:109503. [PMID: 39647242 DOI: 10.1016/j.compbiomed.2024.109503] [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: 08/02/2024] [Revised: 11/17/2024] [Accepted: 11/27/2024] [Indexed: 12/10/2024]
Abstract
GI abnormalities significantly increase mortality rates and impose considerable strain on healthcare systems, underscoring the essential requirement for rapid detection, precise diagnosis, and efficient strategic treatment. To develop a CAD system, this study aims to automatically classify GI disorders utilizing various deep learning methodologies. The proposed system features a three-stage lightweight architecture, consisting of a feature extractor using PSE-CNN, a feature selector employing PCA, and a classifier based on DELM. The framework, designed with only 24 layers and 1.25 million parameters, is employed on the largest dataset, GastroVision, containing 8000 images of 27 GI disorders. To improve visual clarity, a sequential preprocessing strategy is implemented. The model's robustness is evaluated through 5-fold cross-validation. Additionally, several XAI methods, namely Grad-CAM, heatmaps, saliency maps, SHAP, and activation feature maps, are used to explore the model's interpretability. Statistical significance is ensured by calculating the p-value, demonstrating the framework's reliability. The proposed model PSE-CNN-PCA-DELM has achieved outstanding results in the first stage, categorizing the diseases' positions into three primary classes, with average accuracy (97.24 %), precision (97.33 ± 0.01 %), recall (97.24 ± 0.01 %), F1-score (97.33 ± 0.01 %), ROC-AUC (99.38 %), and AUC-PR (98.94 %). In the second stage, the dataset is further divided into nine separate classes, considering the overall disease characteristics, and achieves excellent outcomes with average performance rates of 90.00 %, 89.71 ± 0.11 %, 89.59 ± 0.14 %, 89.51 ± 0.12 %, 98.49 %, and 94.63 %, respectively. The third stage involves a more detailed classification into twenty-seven classes, maintaining strong performance with scores of 93.00 %, 82.69 ± 0.37 %, 83.00 ± 0.38 %, 81.54 ± 0.35 %, 97.38 %, and 88.03 %, respectively. The framework's compact size of 14.88 megabytes and average testing time of 59.17 milliseconds make it highly efficient. Its effectiveness is further validated through comparisons with several TL approaches. Practically, the framework is extremely resilient for clinical implementation.
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Affiliation(s)
- Md Faysal Ahamed
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh.
| | - Fariya Bintay Shafi
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh.
| | - Md Nahiduzzaman
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh.
| | | | - Amith Khandakar
- Department of Electrical Engineering, College of Engineering, Qatar Univeristy, Doha, Qatar.
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Wei X, Xi P, Chen M, Wen Y, Wu H, Wang L, Zhu Y, Ren Y, Gu Z. Capsule robots for the monitoring, diagnosis, and treatment of intestinal diseases. Mater Today Bio 2024; 29:101294. [PMID: 39483392 PMCID: PMC11525164 DOI: 10.1016/j.mtbio.2024.101294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Revised: 09/21/2024] [Accepted: 10/06/2024] [Indexed: 11/03/2024] Open
Abstract
Current evidence suggests that the intestine as the new frontier for human health directly impacts both our physical and mental health. Therefore, it is highly desirable to develop the intelligent tool for the enhanced diagnosis and treatment of intestinal diseases. During the past 20 years, capsule robots have opened new avenues for research and clinical applications, potentially revolutionizing human health monitor, disease diagnosis and treatment. In this review, we summarize the research progress of edible multifunctional capsule robots in intestinal diseases. To begin, we introduce the correlation between the intestinal microbiome, intestinal gas and human diseases. After that, we focus on the technical structure of edible multifunctional robots. Subsequently, the biomedical applications in the monitoring, diagnosis and treatment of intestinal diseases are discussed in detail. Last but not least, the main challenges of multifunctional capsule robots during the development process are summarized, followed by a vision for future development opportunities.
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Affiliation(s)
- Xiangyu Wei
- Department of Rheumatology, Research Center of Immunology, Affiliated Hospital of Nantong University, Nantong University, Nantong, 226001, China
- Department of Rheumatology, Affiliated Municipal Hospital of Xuzhou Medical University, Xuzhou, 221100, China
- Suzhou Medical College, Soochow University, Suzhou, 215123, China
| | - Peipei Xi
- Department of Emergency, Affiliated Hospital of Nantong University, Nantong University, Nantong, 226001, China
- Suzhou Medical College, Soochow University, Suzhou, 215123, China
| | - Minjie Chen
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Nantong, 226001, China
| | - Ya Wen
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Nantong, 226001, China
| | - Hao Wu
- Department of Otolaryngology, Affiliated Hospital of Nantong University, Nantong University, Nantong, 226001, China
| | - Li Wang
- Institutes of Biomedical Sciences and the Shanghai Key Laboratory of Medical Epigenetics, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Yujuan Zhu
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Nantong, 226001, China
| | - Yile Ren
- Department of Rheumatology, Affiliated Municipal Hospital of Xuzhou Medical University, Xuzhou, 221100, China
| | - Zhifeng Gu
- Department of Rheumatology, Research Center of Immunology, Affiliated Hospital of Nantong University, Nantong University, Nantong, 226001, China
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Lee H, Chung JW, Yun SC, Jung SW, Yoon YJ, Kim JH, Cha B, Kayasseh MA, Kim KO. Validation of Artificial Intelligence Computer-Aided Detection on Gastric Neoplasm in Upper Gastrointestinal Endoscopy. Diagnostics (Basel) 2024; 14:2706. [PMID: 39682614 DOI: 10.3390/diagnostics14232706] [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: 11/14/2024] [Revised: 11/22/2024] [Accepted: 11/25/2024] [Indexed: 12/18/2024] Open
Abstract
BACKGROUND/OBJECTIVES Gastric cancer ranks fifth for incidence and fourth in the leading causes of mortality worldwide. In this study, we aimed to validate previously developed artificial intelligence (AI) computer-aided detection (CADe) algorithm, called ALPHAON® in detecting gastric neoplasm. METHODS We used the retrospective data of 500 still images, including 5 benign gastric ulcers, 95 with gastric cancer, and 400 normal images. Thereby we validated the CADe algorithm measuring accuracy, sensitivity, and specificity with the result of receiver operating characteristic curves (ROC) and area under curve (AUC) in addition to comparing the diagnostic performance status of four expert endoscopists, four trainees, and four beginners from two university-affiliated hospitals with CADe algorithm. After a washing-out period of over 2 weeks, endoscopists performed gastric detection on the same dataset of the 500 endoscopic images again marked by ALPHAON®. RESULTS The CADe algorithm presented high validity in detecting gastric neoplasm with accuracy (0.88, 95% CI: 0.85 to 0.91), sensitivity (0.93, 95% CI: 0.88 to 0.98), specificity (0.87, 95% CI: 0.84 to 0.90), and AUC (0.962). After a washing-out period of over 2 weeks, overall validity improved in the trainee and beginner groups with the assistance of ALPHAON®. Significant improvement was present, especially in the beginner group (accuracy 0.94 (0.93 to 0.96) p < 0.001, sensitivity 0.87 (0.82 to 0.92) p < 0.001, specificity 0.96 (0.95 to 0.97) p < 0.001). CONCLUSIONS The high validation performance state of the CADe algorithm system was verified. Also, ALPHAON® has demonstrated its potential to serve as an endoscopic educator for beginners improving and making progress in sensitivity and specificity.
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Affiliation(s)
- Hannah Lee
- Division of Gastroenterology, Department of Internal Medicine, Gachon University, Gil Medical Center, Incheon 21565, Republic of Korea
| | - Jun-Won Chung
- Division of Gastroenterology, Department of Internal Medicine, Gachon University, Gil Medical Center, Incheon 21565, Republic of Korea
| | - Sung-Cheol Yun
- Division of Biostatistics, Center for Medical Research and Information, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea
| | - Sung Woo Jung
- Division of Gastroenterology, Department of Internal Medicine, Korea University College of Medicine, Ansan 15355, Republic of Korea
| | | | - Ji Hee Kim
- CAIMI Co., Ltd., Incheon 22004, Republic of Korea
| | - Boram Cha
- Division of Gastroenterology, Department of Internal Medicine, Inha University Hospital, Inha University School of Medicine, Incheon 22332, Republic of Korea
| | - Mohd Azzam Kayasseh
- Division of Gastroenterology, Dr. Sulaiman AI Habib Medical Group, Dubai Healthcare City, Dubai 51431, United Arab Emirates
| | - Kyoung Oh Kim
- Division of Gastroenterology, Department of Internal Medicine, Gachon University, Gil Medical Center, Incheon 21565, Republic of Korea
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Nie Z, Xu M, Wang Z, Lu X, Song W. A Review of Application of Deep Learning in Endoscopic Image Processing. J Imaging 2024; 10:275. [PMID: 39590739 PMCID: PMC11595772 DOI: 10.3390/jimaging10110275] [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: 09/28/2024] [Revised: 10/24/2024] [Accepted: 10/29/2024] [Indexed: 11/28/2024] Open
Abstract
Deep learning, particularly convolutional neural networks (CNNs), has revolutionized endoscopic image processing, significantly enhancing the efficiency and accuracy of disease diagnosis through its exceptional ability to extract features and classify complex patterns. This technology automates medical image analysis, alleviating the workload of physicians and enabling a more focused and personalized approach to patient care. However, despite these remarkable achievements, there are still opportunities to further optimize deep learning models for endoscopic image analysis, including addressing limitations such as the requirement for large annotated datasets and the challenge of achieving higher diagnostic precision, particularly for rare or subtle pathologies. This review comprehensively examines the profound impact of deep learning on endoscopic image processing, highlighting its current strengths and limitations. It also explores potential future directions for research and development, outlining strategies to overcome existing challenges and facilitate the integration of deep learning into clinical practice. Ultimately, the goal is to contribute to the ongoing advancement of medical imaging technologies, leading to more accurate, personalized, and optimized medical care for patients.
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Affiliation(s)
- Zihan Nie
- School of Mechanical Engineering, Shandong University, Jinan 250061, China; (Z.N.); (M.X.); (Z.W.); (X.L.)
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture of Ministry of Education, Shandong University, Jinan 250061, China
| | - Muhao Xu
- School of Mechanical Engineering, Shandong University, Jinan 250061, China; (Z.N.); (M.X.); (Z.W.); (X.L.)
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture of Ministry of Education, Shandong University, Jinan 250061, China
| | - Zhiyong Wang
- School of Mechanical Engineering, Shandong University, Jinan 250061, China; (Z.N.); (M.X.); (Z.W.); (X.L.)
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture of Ministry of Education, Shandong University, Jinan 250061, China
| | - Xiaoqi Lu
- School of Mechanical Engineering, Shandong University, Jinan 250061, China; (Z.N.); (M.X.); (Z.W.); (X.L.)
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture of Ministry of Education, Shandong University, Jinan 250061, China
| | - Weiye Song
- School of Mechanical Engineering, Shandong University, Jinan 250061, China; (Z.N.); (M.X.); (Z.W.); (X.L.)
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture of Ministry of Education, Shandong University, Jinan 250061, China
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Tabuchi H, Engelmann J, Maeda F, Nishikawa R, Nagasawa T, Yamauchi T, Tanabe M, Akada M, Kihara K, Nakae Y, Kiuchi Y, Bernabeu MO. Using artificial intelligence to improve human performance: efficient retinal disease detection training with synthetic images. Br J Ophthalmol 2024; 108:1430-1435. [PMID: 38485215 PMCID: PMC11503156 DOI: 10.1136/bjo-2023-324923] [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: 11/17/2023] [Accepted: 02/29/2024] [Indexed: 09/22/2024]
Abstract
BACKGROUND Artificial intelligence (AI) in medical imaging diagnostics has huge potential, but human judgement is still indispensable. We propose an AI-aided teaching method that leverages generative AI to train students on many images while preserving patient privacy. METHODS A web-based course was designed using 600 synthetic ultra-widefield (UWF) retinal images to teach students to detect disease in these images. The images were generated by stable diffusion, a large generative foundation model, which we fine-tuned with 6285 real UWF images from six categories: five retinal diseases (age-related macular degeneration, glaucoma, diabetic retinopathy, retinal detachment and retinal vein occlusion) and normal. 161 trainee orthoptists took the course. They were evaluated with two tests: one consisting of UWF images and another of standard field (SF) images, which the students had not encountered in the course. Both tests contained 120 real patient images, 20 per category. The students took both tests once before and after training, with a cool-off period in between. RESULTS On average, students completed the course in 53 min, significantly improving their diagnostic accuracy. For UWF images, student accuracy increased from 43.6% to 74.1% (p<0.0001 by paired t-test), nearly matching the previously published state-of-the-art AI model's accuracy of 73.3%. For SF images, student accuracy rose from 42.7% to 68.7% (p<0.0001), surpassing the state-of-the-art AI model's 40%. CONCLUSION Synthetic images can be used effectively in medical education. We also found that humans are more robust to novel situations than AI models, thus showcasing human judgement's essential role in medical diagnosis.
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Affiliation(s)
- Hitoshi Tabuchi
- Department of Technology and Design Thinking for Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Hiroshima, Japan
- Department of Ophthalmology, Tsukazaki Hospital, Himeji, Hyogo, Japan
| | - Justin Engelmann
- Usher Institute,College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Fumiatsu Maeda
- Department of Orthoptics and Visual Sciences, Niigata University of Health and Welfare, Niigata, Niigata, Japan
| | - Ryo Nishikawa
- Department of Ophthalmology, Tsukazaki Hospital, Himeji, Hyogo, Japan
| | | | - Tomofusa Yamauchi
- Department of Ophthalmology, Tsukazaki Hospital, Himeji, Hyogo, Japan
| | - Mao Tanabe
- Department of Ophthalmology, Tsukazaki Hospital, Himeji, Hyogo, Japan
| | - Masahiro Akada
- Department of Ophthalmology, Tsukazaki Hospital, Himeji, Hyogo, Japan
| | - Keita Kihara
- Department of Ophthalmology, Tsukazaki Hospital, Himeji, Hyogo, Japan
| | - Yasuyuki Nakae
- Department of Ophthalmology, Tsukazaki Hospital, Himeji, Hyogo, Japan
| | - Yoshiaki Kiuchi
- Department of Ophthalmology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Hiroshima, Japan
| | - Miguel O Bernabeu
- Usher Institute,College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, Scotland, UK
- Bayes Centre, College of Science and Engineering, The University of Edinburgh, Edinburgh, Scotland, UK
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Lee JY, Park J, Lee HJ, Park H, Jin EH, Park K, Baek JE, Yang DH, Hong SW, Kim N, Byeon JS. Automatic assessment of bowel preparation by an artificial intelligence model and its clinical applicability. J Gastroenterol Hepatol 2024; 39:1917-1923. [PMID: 38766682 DOI: 10.1111/jgh.16618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 04/06/2024] [Accepted: 05/02/2024] [Indexed: 05/22/2024]
Abstract
BACKGROUND AND AIM Reliable bowel preparation assessment is important in colonoscopy. However, current scoring systems are limited by laborious and time-consuming tasks and interobserver variability. We aimed to develop an artificial intelligence (AI) model to assess bowel cleanliness and evaluate its clinical applicability. METHODS A still image-driven AI model to assess the Boston Bowel Preparation Scale (BBPS) was developed and validated using 2361 colonoscopy images. For evaluating real-world applicability, the model was validated using 113 10-s colonoscopy video clips and 30 full colonoscopy videos to identify "adequate (BBPS 2-3)" or "inadequate (BBPS 0-1)" preparation. The model was tested with an external dataset of 29 colonoscopy videos. The clinical applicability of the model was evaluated using 225 consecutive colonoscopies. Inter-rater variability was analyzed between the AI model and endoscopists. RESULTS The AI model achieved an accuracy of 94.0% and an area under the receiver operating characteristic curve of 0.939 with the still images. Model testing with an external dataset showed an accuracy of 95.3%, an area under the receiver operating characteristic curve of 0.976, and a sensitivity of 100% for the detection of inadequate preparations. The clinical applicability study showed an overall agreement rate of 85.3% between endoscopists and the AI model, with Fleiss' kappa of 0.686. The agreement rate was lower for the right colon compared with the transverse and left colon, with Fleiss' kappa of 0.563, 0.575, and 0.789, respectively. CONCLUSIONS The AI model demonstrated accurate bowel preparation assessment and substantial agreement with endoscopists. Further refinement of the AI model is warranted for effective monitoring of qualified colonoscopy in large-scale screening programs.
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Affiliation(s)
- Ji Young Lee
- Health Screening Promotion Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jooyoung Park
- Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Hyo Jeong Lee
- Health Screening Promotion Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Hana Park
- Health Screening Promotion Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Eun Hyo Jin
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Kanggil Park
- Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Ji Eun Baek
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Dong-Hoon Yang
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Seung Wook Hong
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Namkug Kim
- Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jeong-Sik Byeon
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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Tiankanon K, Karuehardsuwan J, Aniwan S, Mekaroonkamol P, Sunthornwechapong P, Navadurong H, Tantitanawat K, Mekritthikrai K, Samutrangsi S, Vateekul P, Rerknimitr R. Performance comparison between two computer-aided detection colonoscopy models by trainees using different false positive thresholds: a cross-sectional study in Thailand. Clin Endosc 2024; 57:217-225. [PMID: 38556473 PMCID: PMC10984740 DOI: 10.5946/ce.2023.145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/24/2023] [Accepted: 09/25/2023] [Indexed: 04/02/2024] Open
Abstract
BACKGROUND/AIMS This study aims to compare polyp detection performance of "Deep-GI," a newly developed artificial intelligence (AI) model, to a previously validated AI model computer-aided polyp detection (CADe) using various false positive (FP) thresholds and determining the best threshold for each model. METHODS Colonoscopy videos were collected prospectively and reviewed by three expert endoscopists (gold standard), trainees, CADe (CAD EYE; Fujifilm Corp.), and Deep-GI. Polyp detection sensitivity (PDS), polyp miss rates (PMR), and false-positive alarm rates (FPR) were compared among the three groups using different FP thresholds for the duration of bounding boxes appearing on the screen. RESULTS In total, 170 colonoscopy videos were used in this study. Deep-GI showed the highest PDS (99.4% vs. 85.4% vs. 66.7%, p<0.01) and the lowest PMR (0.6% vs. 14.6% vs. 33.3%, p<0.01) when compared to CADe and trainees, respectively. Compared to CADe, Deep-GI demonstrated lower FPR at FP thresholds of ≥0.5 (12.1 vs. 22.4) and ≥1 second (4.4 vs. 6.8) (both p<0.05). However, when the threshold was raised to ≥1.5 seconds, the FPR became comparable (2 vs. 2.4, p=0.3), while the PMR increased from 2% to 10%. CONCLUSION Compared to CADe, Deep-GI demonstrated a higher PDS with significantly lower FPR at ≥0.5- and ≥1-second thresholds. At the ≥1.5-second threshold, both systems showed comparable FPR with increased PMR.
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Affiliation(s)
- Kasenee Tiankanon
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | - Julalak Karuehardsuwan
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | - Satimai Aniwan
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | - Parit Mekaroonkamol
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | | | - Huttakan Navadurong
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | - Kittithat Tantitanawat
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | - Krittaya Mekritthikrai
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | - Salin Samutrangsi
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | - Peerapon Vateekul
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
| | - Rungsun Rerknimitr
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
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Danieli MG, Brunetto S, Gammeri L, Palmeri D, Claudi I, Shoenfeld Y, Gangemi S. Machine learning application in autoimmune diseases: State of art and future prospectives. Autoimmun Rev 2024; 23:103496. [PMID: 38081493 DOI: 10.1016/j.autrev.2023.103496] [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: 11/12/2023] [Accepted: 11/29/2023] [Indexed: 04/30/2024]
Abstract
Autoimmune diseases are a group of disorders resulting from an alteration of immune tolerance, characterized by the formation of autoantibodies and the consequent development of heterogeneous clinical manifestations. Diagnosing autoimmune diseases is often complicated, and the available prognostic tools are limited. Machine learning allows us to analyze large amounts of data and carry out complex calculations quickly and with minimal effort. In this work, we examine the literature focusing on the use of machine learning in the field of the main systemic (systemic lupus erythematosus and rheumatoid arthritis) and organ-specific autoimmune diseases (type 1 diabetes mellitus, autoimmune thyroid, gastrointestinal, and skin diseases). From our analysis, interesting applications of machine learning emerged for developing algorithms useful in the early diagnosis of disease or prognostic models (risk of complications, therapeutic response). Subsequent studies and the creation of increasingly rich databases to be supplied to the algorithms will eventually guide the clinician in the diagnosis, allowing intervention when the pathology is still in an early stage and immediately directing towards a correct therapeutic approach.
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Affiliation(s)
- Maria Giovanna Danieli
- SOS Immunologia delle Malattie Rare e dei Trapianti. AOU delle Marche & Dipartimento di Scienze Cliniche e Molecolari, Università Politecnica delle Marche, via Tronto 10/A, 60126 Torrette di Ancona, Italy; Postgraduate School of Allergy and Clinical Immunology, Università Politecnica delle Marche, via Tronto 10/A, 60126 Ancona, Italy.
| | - Silvia Brunetto
- Operative Unit of Allergy and Clinical Immunology, Department of Clinical and Experimental Medicine, University of Messina, Via Consolare Valeria 1, 98125 Messina, Italy
| | - Luca Gammeri
- Operative Unit of Allergy and Clinical Immunology, Department of Clinical and Experimental Medicine, University of Messina, Via Consolare Valeria 1, 98125 Messina, Italy
| | - Davide Palmeri
- Postgraduate School of Allergy and Clinical Immunology, Università Politecnica delle Marche, via Tronto 10/A, 60126 Ancona, Italy
| | - Ilaria Claudi
- Postgraduate School of Allergy and Clinical Immunology, Università Politecnica delle Marche, via Tronto 10/A, 60126 Ancona, Italy
| | - Yehuda Shoenfeld
- Zabludowicz Center for Autoimmune Diseases, Sheba Medical Center, and Reichman University Herzliya, Israel.
| | - Sebastiano Gangemi
- Operative Unit of Allergy and Clinical Immunology, Department of Clinical and Experimental Medicine, University of Messina, Via Consolare Valeria 1, 98125 Messina, Italy.
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Elshaarawy O, Alboraie M, El-Kassas M. Artificial Intelligence in endoscopy: A future poll. Arab J Gastroenterol 2024; 25:13-17. [PMID: 38220477 DOI: 10.1016/j.ajg.2023.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 09/18/2022] [Accepted: 11/28/2023] [Indexed: 01/16/2024]
Abstract
Artificial Intelligence [AI] has been a trendy topic in recent years, with many developed medical applications. In gastrointestinal endoscopy, AI systems include computer-assisted detection [CADe] for lesion detection as bleedings and polyps and computer-assisted diagnosis [CADx] for optical biopsy and lesion characterization. The technology behind these systems is based on a computer algorithm that is trained for a specific function. This function could be to recognize or characterize target lesions such as colonic polyps. Moreover, AI systems can offer technical assistance to improve endoscopic performance as scope insertion guidance. Currently, we believe that such technologies still lack legal and regulatory validations as a large sector of doctors and patients have concerns. However, there is no doubt that these technologies will bring significant improvement in the endoscopic management of patients as well as save money and time.
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Affiliation(s)
- Omar Elshaarawy
- Hepatology and Gastroenterology Department, National Liver Institute, Menoufia University, Menoufia, Egypt; Gastroenterology Department, Royal Liverpool University Hospital, NHS, UK
| | - Mohamed Alboraie
- Department of Internal Medicine, Al-Azhar University, Cairo, Egypt
| | - Mohamed El-Kassas
- Endemic Medicine Department, Faculty of Medicine, Helwan University, Cairo, Egypt.
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Al-Qerem W, Eberhardt J, Jarab A, Al Bawab AQ, Hammad A, Alasmari F, Alazab B, Husein DA, Alazab J, Al-Beool S. Exploring knowledge, attitudes, and practices towards artificial intelligence among health professions' students in Jordan. BMC Med Inform Decis Mak 2023; 23:288. [PMID: 38098095 PMCID: PMC10722664 DOI: 10.1186/s12911-023-02403-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 12/11/2023] [Indexed: 12/17/2023] Open
Abstract
INTRODUCTION The integration of Artificial Intelligence (AI) in medical education and practice is a significant development. This study examined the Knowledge, Attitudes, and Practices (KAP) of health professions' students in Jordan concerning AI, providing insights into their preparedness and perceptions. METHODS An online questionnaire was distributed to 483 Jordanian health professions' students via social media. Demographic data, AI-related KAP, and barriers were collected. Quantile regression models analyzed associations between variables and KAP scores. RESULTS Moderate AI knowledge was observed among participants, with specific understanding of data requirements and barriers. Attitudes varied, combining skepticism about AI replacing human teachers with recognition of its value. While AI tools were used for specific tasks, broader integration in medical education and practice was limited. Barriers included lack of knowledge, access, time constraints, and curriculum gaps. CONCLUSIONS This study highlights the need to enhance medical education with AI topics and address barriers. Students need to be better prepared for AI integration, in order to enable medical education to harness AI's potential for improved patient care and training.
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Affiliation(s)
- Walid Al-Qerem
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, 11733, Amman, Jordan.
| | - Judith Eberhardt
- School of Social Sciences, Humanities and Law, Department of Psychology, Teesside University, TS1 3BX, Middlesbrough, UK
| | - Anan Jarab
- College of Pharmacy, Al Ain University, 64141, Abu Dhabi, UAE
- AAU Health and Biomedical Research Center, Al Ain University, 112612, Abu Dhabi, United Arab Emirates
- Department of Clinical Pharmacy, Faculty of Pharmacy, Jordan University of Science and Technology, 22110, Irbid, Jordan
| | - Abdel Qader Al Bawab
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, 11733, Amman, Jordan
| | - Alaa Hammad
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, 11733, Amman, Jordan
| | - Fawaz Alasmari
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, 12372, Riyadh, Saudi Arabia
| | - Badi'ah Alazab
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, 11733, Amman, Jordan
| | - Daoud Abu Husein
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, 11733, Amman, Jordan
| | - Jumana Alazab
- School of Medicine, The University of Jordan, 11910, Amman, Jordan
| | - Saed Al-Beool
- School of Medicine, The University of Jordan, 11910, Amman, Jordan
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12
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Yadav A, Kumar A. Artificial intelligence in rectal cancer: What is the future? Artif Intell Cancer 2023; 4:11-22. [DOI: 10.35713/aic.v4.i2.11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 09/18/2023] [Accepted: 09/25/2023] [Indexed: 12/07/2023] Open
Abstract
Colorectal cancer (CRC) is the third most prevalent cancer in both men and women, and it is the second leading cause of cancer-related deaths globally. Around 60%-70% of CRC patients are diagnosed at advanced stages, with nearly 20% having liver metastases. It is noteworthy that the 5-year survival rates decline significantly from 80%-90% for localized disease to a mere 10%-15% for patients with metastasis at the time of diagnosis. Early diagnosis, appropriate therapeutic strategy, accurate assessment of treatment response, and prognostication is essential for better outcome. There has been significant technological development in the last couple of decades to improve the outcome of rectal cancer including Artificial intelligence (AI). AI is a broad term used to describe the study of machines that mimic human intelligence, such as perceiving the environment, drawing logical conclusions from observations, and performing complex tasks. At present AI has demonstrated a promising role in early diagnosis, prognosis, and treatment outcomes for patients with rectal cancer, a limited role in surgical decision making, and had a bright future.
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Affiliation(s)
- Alka Yadav
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, UP, India
| | - Ashok Kumar
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, UP, India
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Keshtkar K, Reza Safarpour A, Heshmat R, Sotoudehmanesh R, Keshtkar A. A Systematic Review and Meta-analysis of Convolutional Neural Network in the Diagnosis of Colorectal Polyps and Cancer. THE TURKISH JOURNAL OF GASTROENTEROLOGY : THE OFFICIAL JOURNAL OF TURKISH SOCIETY OF GASTROENTEROLOGY 2023; 34:985-997. [PMID: 37681266 PMCID: PMC10645297 DOI: 10.5152/tjg.2023.22491] [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: 07/22/2022] [Accepted: 03/22/2023] [Indexed: 09/09/2023]
Abstract
Convolutional neural networks are a class of deep neural networks used for different clinical purposes, including improving the detection rate of colorectal lesions. This systematic review and meta-analysis aimed to assess the performance of convolutional neural network-based models in the detection or classification of colorectal polyps and colorectal cancer. A systematic search was performed in MEDLINE, SCOPUS, Web of Science, and other related databases. The performance measures of the convolutional neural network models in the detection of colorectal polyps and colorectal cancer were calculated in the 2 scenarios of the best and worst accuracy. Stata and R software were used for conducting the meta-analysis. From 3368 searched records, 24 primary studies were included. The sensitivity and specificity of convolutional neural network models in predicting colorectal polyps in worst and best scenarios ranged from 84.7% to 91.6% and from 86.0% to 93.8%, respectively. These values in predicting colorectal cancer varied between 93.2% and 94.1% and between 94.6% and 97.7%. The positive and negative likelihood ratios varied between 6.2 and 14.5 and 0.09 and 0.17 in these scenarios, respectively, in predicting colorectal polyps, and 17.1-41.2 and 0.07-0.06 in predicting colorectal polyps. The diagnostic odds ratio and accuracy measures of convolutional neural network models in predicting colorectal polyps in worst and best scenarios ranged between 36% and 162% and between 80.5% and 88.6%, respectively. These values in predicting colorectal cancer in the worst and the best scenarios varied between 239.63% and 677.47% and between 88.2% and 96.4%. The area under the receiver operating characteristic varied between 0.92 and 0.97 in the worst and the best scenarios in colorectal polyps, respectively, and between 0.98 and 0.99 in colorectal polyps prediction. Convolutional neural network-based models showed an acceptable accuracy in detecting colorectal polyps and colorectal cancer.
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Affiliation(s)
- Kamyab Keshtkar
- University of Tehran School of Electrical and Computer Engineering, Tehran, Iran
| | - Ali Reza Safarpour
- Gastroenterohepatology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Ramin Heshmat
- Chronic Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Rasoul Sotoudehmanesh
- Department of Gastroenterology, Digestive Disease Research Center, Digestive Disease Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Abbas Keshtkar
- Department of Health Sciences Education Development, Tehran University of Medical Sciences School of Public Health, Tehran, Iran
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Chen Q, Cai M, Fan X, Liu W, Fang G, Yao S, Xu Y, Li Q, Zhao Y, Zhao K, Liu Z, Chen Z. An artificial intelligence-based ecological index for prognostic evaluation of colorectal cancer. BMC Cancer 2023; 23:763. [PMID: 37592224 PMCID: PMC10433587 DOI: 10.1186/s12885-023-11289-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 08/11/2023] [Indexed: 08/19/2023] Open
Abstract
BACKGROUND AND OBJECTIVE In the tumor microenvironment (TME), the dynamic interaction between tumor cells and immune cells plays a critical role in predicting the prognosis of colorectal cancer. This study introduces a novel approach based on artificial intelligence (AI) and immunohistochemistry (IHC)-stained whole-slide images (WSIs) of colorectal cancer (CRC) patients to quantitatively assess the spatial associations between tumor cells and immune cells. To achieve this, we employ the Morisita-Horn ecological index (Mor-index), which allows for a comprehensive analysis of the spatial distribution patterns between tumor cells and immune cells within the TME. MATERIALS AND METHODS In this study, we employed a combination of deep learning technology and traditional computer segmentation methods to accurately segment the tumor nuclei, immune nuclei, and stroma nuclei within the tumor regions of IHC-stained WSIs. The Mor-index was used to assess the spatial association between tumor cells and immune cells in TME of CRC patients by obtaining the results of cell nuclei segmentation. A discovery cohort (N = 432) and validation cohort (N = 137) were used to evaluate the prognostic value of the Mor-index for overall survival (OS). RESULTS The efficacy of our method was demonstrated through experiments conducted on two datasets comprising a total of 569 patients. Compared to other studies, our method is not only superior to the QuPath tool but also produces better segmentation results with an accuracy of 0.85. Mor-index was quantified automatically by our method. Survival analysis indicated that the higher Mor-index correlated with better OS in the discovery cohorts (HR for high vs. low 0.49, 95% CI 0.27-0.77, P = 0.0014) and validation cohort (0.21, 0.10-0.46, < 0.0001). CONCLUSION This study provided a novel AI-based approach to segmenting various nuclei in the TME. The Mor-index can reflect the immune status of CRC patients and is associated with favorable survival. Thus, Mor-index can potentially make a significant role in aiding clinical prognosis and decision-making.
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Affiliation(s)
- Qicong Chen
- Institute of Computing Science and Technology, Guangzhou University, No. 230, Outer Ring West Road, Guangzhou, 510006, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - Ming Cai
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Xinjuan Fan
- Department of Pathology, Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Wenbin Liu
- Institute of Computing Science and Technology, Guangzhou University, No. 230, Outer Ring West Road, Guangzhou, 510006, China
| | - Gang Fang
- Institute of Computing Science and Technology, Guangzhou University, No. 230, Outer Ring West Road, Guangzhou, 510006, China
| | - Su Yao
- Department of Pathology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yao Xu
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Qian Li
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Yingnan Zhao
- Institute of Computing Science and Technology, Guangzhou University, No. 230, Outer Ring West Road, Guangzhou, 510006, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - Ke Zhao
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China.
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
- Guangdong Provincial People's Hospital, Guangdong Cardiovascular Institute, Guangdong Academy of Medical Sciences, Guangzhou, China.
| | - Zaiyi Liu
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China.
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
| | - Zhihua Chen
- Institute of Computing Science and Technology, Guangzhou University, No. 230, Outer Ring West Road, Guangzhou, 510006, China.
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Ahmed AA, Brychcy A, Abouzid M, Witt M, Kaczmarek E. Perception of Pathologists in Poland of Artificial Intelligence and Machine Learning in Medical Diagnosis-A Cross-Sectional Study. J Pers Med 2023; 13:962. [PMID: 37373951 DOI: 10.3390/jpm13060962] [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: 04/18/2023] [Revised: 05/31/2023] [Accepted: 06/04/2023] [Indexed: 06/29/2023] Open
Abstract
BACKGROUND In the past vicennium, several artificial intelligence (AI) and machine learning (ML) models have been developed to assist in medical diagnosis, decision making, and design of treatment protocols. The number of active pathologists in Poland is low, prolonging tumor patients' diagnosis and treatment journey. Hence, applying AI and ML may aid in this process. Therefore, our study aims to investigate the knowledge of using AI and ML methods in the clinical field in pathologists in Poland. To our knowledge, no similar study has been conducted. METHODS We conducted a cross-sectional study targeting pathologists in Poland from June to July 2022. The questionnaire included self-reported information on AI or ML knowledge, experience, specialization, personal thoughts, and level of agreement with different aspects of AI and ML in medical diagnosis. Data were analyzed using IBM® SPSS® Statistics v.26, PQStat Software v.1.8.2.238, and RStudio Build 351. RESULTS Overall, 68 pathologists in Poland participated in our study. Their average age and years of experience were 38.92 ± 8.88 and 12.78 ± 9.48 years, respectively. Approximately 42% used AI or ML methods, which showed a significant difference in the knowledge gap between those who never used it (OR = 17.9, 95% CI = 3.57-89.79, p < 0.001). Additionally, users of AI had higher odds of reporting satisfaction with the speed of AI in the medical diagnosis process (OR = 4.66, 95% CI = 1.05-20.78, p = 0.043). Finally, significant differences (p = 0.003) were observed in determining the liability for legal issues used by AI and ML methods. CONCLUSION Most pathologists in this study did not use AI or ML models, highlighting the importance of increasing awareness and educational programs regarding applying AI and ML in medical diagnosis.
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Affiliation(s)
- Alhassan Ali Ahmed
- Department of Bioinformatics and Computational Biology, Poznan University of Medical Sciences, 61-806 Poznan, Poland
- Doctoral School, Poznan University of Medical Sciences, 61-806 Poznan, Poland
| | - Agnieszka Brychcy
- Department of Clinical Patomorphology, Heliodor Swiecicki Clinical Hospital of the Poznan University of Medical Sciences, 61-806 Poznan, Poland
| | - Mohamed Abouzid
- Doctoral School, Poznan University of Medical Sciences, 61-806 Poznan, Poland
- Department of Physical Pharmacy and Pharmacokinetics, Poznan University of Medical Sciences, 60-806 Poznan, Poland
| | - Martin Witt
- Department of Anatomy, Rostock University Medical Centre, 18057 Rostock, Germany
- Department of Anatomy, Technische Universität Dresden, 01307 Dresden, Germany
| | - Elżbieta Kaczmarek
- Department of Bioinformatics and Computational Biology, Poznan University of Medical Sciences, 61-806 Poznan, Poland
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Chin SE, Wan FT, Ladlad J, Chue KM, Teo EK, Lin CL, Foo FJ, Koh FH. One-year review of real-time artificial intelligence (AI)-aided endoscopy performance. Surg Endosc 2023:10.1007/s00464-023-09979-8. [PMID: 36932187 DOI: 10.1007/s00464-023-09979-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 02/21/2023] [Indexed: 03/19/2023]
Abstract
BACKGROUND Colonoscopies have long been the gold standard for detection of pre-malignant neoplastic lesions of the colon. Our previous study tried real-time artificial intelligence (AI)-aided colonoscopy over a three-month period and found significant improvements in collective and individual endoscopist's adenoma detection rates compared to baseline. As an expansion, this study evaluates the 1-year performance of AI-aided colonoscopy in the same institution. METHODS A prospective cohort study was conducted in a single institution in Singapore. The AI software used was GI Genius™ Intelligent Endoscopy Module, US-DG-2000309 © 2021 Medtronic. Between July 2021 and June 2022, polypectomy rates in non-AI-aided colonoscopies and AI-aided colonoscopies were calculated and compared. Some of the AI-aided colonoscopies were recorded and video reviewed. A "hit" was defined as a sustained detection of an area by the AI. If a polypectomy was performed for a "hit," its histology was reviewed. Additional calculations for polyp detection rate (PDR), adenoma detection rate (ADR), and adenoma detection per colonoscopy (ADPC) were performed. Cost analysis was performed to determine cost effectiveness of subscription to the AI program. RESULTS 2433 AI-aided colonoscopies were performed between July 2021 and June 2022 and compared against 1770 non-AI-aided colonoscopies. AI-aided colonoscopies yielded significantly higher rates of polypectomies (33.6%) as compared with non-AI-aided colonoscopies (28.4%) (p < 0.001). Among the AI-aided colonoscopies, 1050 were reviewed and a final 843 were included for additional analysis. The polypectomy to "hit" ratio was 57.4%, PDR = 45.6%, ADR = 32.4%, and ADPC = 2.08. Histological review showed that 25 polyps (3.13%) were sessile-serrated adenomas. Cost analysis found that the increased polypectomy rates in AI-aided colonoscopes led to an increase in revenue, which covered the subscription cost with an excess of USD 20,000. CONCLUSION AI-aided colonoscopy is a cost effective means of improving colonoscopy quality and may help advance colorectal cancer screening in Singapore.
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Affiliation(s)
- Shuen-Ern Chin
- Lee Kong Chian School of Medicine, 11 Mandalay Road, Singapore, 308232, Singapore
| | - Fang-Ting Wan
- Lee Kong Chian School of Medicine, 11 Mandalay Road, Singapore, 308232, Singapore
| | - Jasmine Ladlad
- Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth, 110 Sengkang East Way, Singapore, 544886, Singapore
| | - Koy-Min Chue
- Endoscopy Centre, Division of Hyperacute Care, Sengkang General Hospital, 110 Sengkang East Way, Singapore, 544886, Singapore
| | - Eng-Kiong Teo
- Department of Gastroenterology and Hepatology, Sengkang General Hospital, SingHealth, 110 Sengkang East Way, Singapore, 544886, Singapore
| | - Cui-Li Lin
- Department of Gastroenterology and Hepatology, Sengkang General Hospital, SingHealth, 110 Sengkang East Way, Singapore, 544886, Singapore
| | - Fung-Joon Foo
- Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth, 110 Sengkang East Way, Singapore, 544886, Singapore
| | - Frederick H Koh
- Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth, 110 Sengkang East Way, Singapore, 544886, Singapore.
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Diaconu C, State M, Birligea M, Ifrim M, Bajdechi G, Georgescu T, Mateescu B, Voiosu T. The Role of Artificial Intelligence in Monitoring Inflammatory Bowel Disease-The Future Is Now. Diagnostics (Basel) 2023; 13:735. [PMID: 36832222 PMCID: PMC9954871 DOI: 10.3390/diagnostics13040735] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 02/02/2023] [Accepted: 02/02/2023] [Indexed: 02/17/2023] Open
Abstract
Crohn's disease and ulcerative colitis remain debilitating disorders, characterized by progressive bowel damage and possible lethal complications. The growing number of applications for artificial intelligence in gastrointestinal endoscopy has already shown great potential, especially in the field of neoplastic and pre-neoplastic lesion detection and characterization, and is currently under evaluation in the field of inflammatory bowel disease management. The application of artificial intelligence in inflammatory bowel diseases can range from genomic dataset analysis and risk prediction model construction to the disease grading severity and assessment of the response to treatment using machine learning. We aimed to assess the current and future role of artificial intelligence in assessing the key outcomes in inflammatory bowel disease patients: endoscopic activity, mucosal healing, response to treatment, and neoplasia surveillance.
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Affiliation(s)
- Claudia Diaconu
- Gastroenterology Department, Colentina Clinical Hospital, 020125 Bucharest, Romania
| | - Monica State
- Gastroenterology Department, Colentina Clinical Hospital, 020125 Bucharest, Romania
- Internal Medicine Department, Carol Davila University of Medicine and Pharmacy, 50474 Bucharest, Romania
| | - Mihaela Birligea
- Gastroenterology Department, Colentina Clinical Hospital, 020125 Bucharest, Romania
| | - Madalina Ifrim
- Gastroenterology Department, Colentina Clinical Hospital, 020125 Bucharest, Romania
| | - Georgiana Bajdechi
- Gastroenterology Department, Colentina Clinical Hospital, 020125 Bucharest, Romania
| | - Teodora Georgescu
- Gastroenterology Department, Colentina Clinical Hospital, 020125 Bucharest, Romania
| | - Bogdan Mateescu
- Gastroenterology Department, Colentina Clinical Hospital, 020125 Bucharest, Romania
- Internal Medicine Department, Carol Davila University of Medicine and Pharmacy, 50474 Bucharest, Romania
| | - Theodor Voiosu
- Gastroenterology Department, Colentina Clinical Hospital, 020125 Bucharest, Romania
- Internal Medicine Department, Carol Davila University of Medicine and Pharmacy, 50474 Bucharest, Romania
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The Role of an Artificial Intelligence Method of Improving the Diagnosis of Neoplasms by Colonoscopy. Diagnostics (Basel) 2023; 13:diagnostics13040701. [PMID: 36832189 PMCID: PMC9955100 DOI: 10.3390/diagnostics13040701] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 01/30/2023] [Accepted: 02/06/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND Colorectal cancer (CRC) is the third most common cancer worldwide. Colonoscopy is the gold standard examination that reduces the morbidity and mortality of CRC. Artificial intelligence (AI) could be useful in reducing the errors of the specialist and in drawing attention to the suspicious area. METHODS A prospective single-center randomized controlled study was conducted in an outpatient endoscopy unit with the aim of evaluating the usefulness of AI-assisted colonoscopy in PDR and ADR during the day time. It is important to understand how already available CADe systems improve the detection of polyps and adenomas in order to make a decision about their routine use in practice. In the period from October 2021 to February 2022, 400 examinations (patients) were included in the study. One hundred and ninety-four patients were examined using the ENDO-AID CADe artificial intelligence device (study group), and 206 patients were examined without the artificial intelligence (control group). RESULTS None of the analyzed indicators (PDR and ADR during morning and afternoon colonoscopies) showed differences between the study and control groups. There was an increase in PDR during afternoon colonoscopies, as well as ADR during morning and afternoon colonoscopies. CONCLUSIONS Based on our results, the use of AI systems in colonoscopies is recommended, especially in circumstances of an increase of examinations. Additional studies with larger groups of patients at night are needed to confirm the already available data.
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Goenka MK, Afzalpurkar S, Jejurikar S, Rodge GA, Tiwari A. Role of artificial intelligence-guided esophagogastroduodenoscopy in assessing the procedural completeness and quality. Indian J Gastroenterol 2023; 42:128-135. [PMID: 36715841 DOI: 10.1007/s12664-022-01294-9] [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: 03/22/2022] [Accepted: 08/12/2022] [Indexed: 01/31/2023]
Abstract
BACKGROUND AND AIMS The quality of esophagogastroduodenoscopy (EGD) can have great impact on the detection of esophageal and gastric lesions, including malignancies. The aim of the study is to investigate the use of artificial intelligence (AI) during EGD by the endoscopists-in-training so that a real-time feedback can be provided, ensuring compliance to a pre-decided protocol for examination. METHODS This is an observational pilot study. The videos of the EGD procedure performed between August 1, 2021, and September 30, 2021, were prospectively analyzed using AI system. The assessment of completeness of the procedure was done based on the visualizsation of pre-defined 29 locations. Endoscopists were divided into two categories - whether they are in the training period (category A) or have competed their endoscopy training (category B). RESULTS A total of 277 procedures, which included 114 category-A and 163 category-B endoscopists, respectively, were included. Most commonly covered areas by the endoscopists were greater curvature of antrum (97.47%), second part of duodenum (96.75%), other parts of antrum such as the anterior, lesser curvature and the posterior aspect (96.75%, 94.95%, and 94.22%, respectively). Commonly missed or inadequately seen areas were vocal cords (99.28%), epiglottis (93.14%) and posterior, anterior, and lateral aspect of incisura (78.70%, 73.65%, and 73.53%, respectively). The good quality procedures were done predominantly by categoryB endoscopists (88.68% vs. 11.32%, p < 0.00001). CONCLUSION AI can play an important role in assessing the quality and completeness of EGD and can be a part of training of endoscopy in future.
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Affiliation(s)
- Mahesh Kumar Goenka
- Institute of Gastrosciences and Liver, Apollo Multispeciality Hospitals, Kolkata, Day Care Building, 4th Floor, AMHL, EM Bypass Road, Kolkata, 700 054, India.
| | - Shivaraj Afzalpurkar
- Institute of Gastrosciences and Liver, Apollo Multispeciality Hospitals, Kolkata, Day Care Building, 4th Floor, AMHL, EM Bypass Road, Kolkata, 700 054, India
| | | | - Gajanan Ashokrao Rodge
- Institute of Gastrosciences and Liver, Apollo Multispeciality Hospitals, Kolkata, Day Care Building, 4th Floor, AMHL, EM Bypass Road, Kolkata, 700 054, India
| | - Awanish Tiwari
- Institute of Gastrosciences and Liver, Apollo Multispeciality Hospitals, Kolkata, Day Care Building, 4th Floor, AMHL, EM Bypass Road, Kolkata, 700 054, India
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Wang P, Liu XG, Kang M, Peng X, Shu ML, Zhou GY, Liu PX, Xiong F, Deng MM, Xia HF, Li JJ, Long XQ, Song Y, Li LP. Artificial intelligence empowers the second-observer strategy for colonoscopy: a randomized clinical trial. Gastroenterol Rep (Oxf) 2023; 11:goac081. [PMID: 36686571 PMCID: PMC9850273 DOI: 10.1093/gastro/goac081] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 11/15/2022] [Accepted: 11/17/2022] [Indexed: 01/21/2023] Open
Abstract
Background In colonoscopy screening for colorectal cancer, human vision limitations may lead to higher miss rate of lesions; artificial intelligence (AI) assistance has been demonstrated to improve polyp detection. However, there still lacks direct evidence to demonstrate whether AI is superior to trainees or experienced nurses as a second observer to increase adenoma detection during colonoscopy. In this study, we aimed to compare the effectiveness of assistance from AI and human observer during colonoscopy. Methods A prospective multicenter randomized study was conducted from 2 September 2019 to 29 May 2020 at four endoscopy centers in China. Eligible patients were randomized to either computer-aided detection (CADe)-assisted group or observer-assisted group. The primary outcome was adenoma per colonoscopy (APC). Secondary outcomes included polyp per colonoscopy (PPC), adenoma detection rate (ADR), and polyp detection rate (PDR). We compared continuous variables and categorical variables by using R studio (version 3.4.4). Results A total of 1,261 (636 in the CADe-assisted group and 625 in the observer-assisted group) eligible patients were analysed. APC (0.42 vs 0.35, P = 0.034), PPC (1.13 vs 0.81, P < 0.001), PDR (47.5% vs 37.4%, P < 0.001), ADR (25.8% vs 24.0%, P = 0.464), the number of detected sessile polyps (683 vs 464, P < 0.001), and sessile adenomas (244 vs 182, P = 0.005) were significantly higher in the CADe-assisted group than in the observer-assisted group. False detections of the CADe system were lower than those of the human observer (122 vs 191, P < 0.001). Conclusions Compared with the human observer, the CADe system may improve the clinical outcome of colonoscopy and reduce disturbance to routine practice (Chictr.org.cn No.: ChiCTR1900025235).
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Affiliation(s)
| | | | - Min Kang
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, P. R. China
| | - Xue Peng
- Department of Gastroenterology, Xinqiao Hospital, Third Military Medical University, Chongqing, P. R. China
| | - Mei-Ling Shu
- Department of Gastroenterology, Suining Central Hospital, Suining, Sichuan, P. R. China
| | - Guan-Yu Zhou
- Department of Gastroenterology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, P. R. China
| | - Pei-Xi Liu
- Department of Gastroenterology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, P. R. China
| | - Fei Xiong
- Department of Gastroenterology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, P. R. China
| | - Ming-Ming Deng
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, P. R. China
| | - Hong-Fen Xia
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, P. R. China
| | - Jian-Jun Li
- Department of Gastroenterology, Xinqiao Hospital, Third Military Medical University, Chongqing, P. R. China
| | - Xiao-Qi Long
- Department of Gastroenterology, Suining Central Hospital, Suining, Sichuan, P. R. China
| | - Yan Song
- Department of Gastroenterology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, P. R. China
| | - Liang-Ping Li
- Corresponding author. Department of Gastroenterology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, No.32 West Second Section, First Ring Road, Chengdu, Sichuan 610072, China. Tel: +86-28-8739 3927;
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21
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Koh FH, Ladlad J, Teo EK, Lin CL, Foo FJ. Real-time artificial intelligence (AI)-aided endoscopy improves adenoma detection rates even in experienced endoscopists: a cohort study in Singapore. Surg Endosc 2023; 37:165-171. [PMID: 35882667 PMCID: PMC9321269 DOI: 10.1007/s00464-022-09470-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 07/10/2022] [Indexed: 01/18/2023]
Abstract
BACKGROUND Colonoscopy is a mainstay to detect premalignant neoplastic lesions in the colon. Real-time Artificial Intelligence (AI)-aided colonoscopy purportedly improves the polyp detection rate, especially for small flat lesions. The aim of this study is to evaluate the performance of real-time AI-aided colonoscopy in the detection of colonic polyps. METHODS A prospective single institution cohort study was conducted in Singapore. All real-time AI-aided colonoscopies, regardless of indication, performed by specialist-grade endoscopists were anonymously recorded from July to September 2021 and reviewed by 2 independent authors (FHK, JL). Sustained detection of an area by the program was regarded as a "hit". Histology for the polypectomies were reviewed to determine adenoma detection rate (ADR). Individual endoscopist's performance with AI were compared against their baseline performance without AI endoscopy. RESULTS A total of 24 (82.8%) endoscopists participated with 18 (62.1%) performing ≥ 5 AI-aided colonoscopies. Of the 18, 72.2% (n = 13) were general surgeons. During that 3-months period, 487 "hits" encountered in 298 colonoscopies. Polypectomies were performed for 51.3% and 68.4% of these polypectomies were adenomas on histology. The post-intervention median ADR was 30.4% was higher than the median baseline polypectomy rate of 24.3% (p = 0.02). Of the adenomas excised, 14 (5.6%) were sessile serrated adenomas. Of those who performed ≥ 5 AI-aided colonoscopies, 13 (72.2%) had an improvement of ADR compared to their polypectomy rate before the introduction of AI, of which 2 of them had significant improvement. CONCLUSIONS Real-time AI-aided colonoscopy have the potential to improved ADR even for experienced endoscopists and would therefore, improve the quality of colonoscopy.
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Affiliation(s)
- Frederick H. Koh
- grid.508163.90000 0004 7665 4668Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth Services, 110 Sengkang East Way, Singapore, 544886 Singapore
| | - Jasmine Ladlad
- grid.508163.90000 0004 7665 4668Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth Services, 110 Sengkang East Way, Singapore, 544886 Singapore
| | | | - Eng-Kiong Teo
- grid.508163.90000 0004 7665 4668Department of Gastroenterology and Hepatology, Sengkang General Hospital, SingHealth Services, Singapore, Singapore
| | - Cui-Li Lin
- grid.508163.90000 0004 7665 4668Department of Gastroenterology and Hepatology, Sengkang General Hospital, SingHealth Services, Singapore, Singapore
| | - Fung-Joon Foo
- grid.508163.90000 0004 7665 4668Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth Services, 110 Sengkang East Way, Singapore, 544886 Singapore ,grid.508163.90000 0004 7665 4668Endoscopy Centre, Division of Hyperacute Care, Sengkang General Hospital, Singapore, Singapore
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22
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Li MD, Huang ZR, Shan QY, Chen SL, Zhang N, Hu HT, Wang W. Performance and comparison of artificial intelligence and human experts in the detection and classification of colonic polyps. BMC Gastroenterol 2022; 22:517. [PMID: 36513975 PMCID: PMC9749329 DOI: 10.1186/s12876-022-02605-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 12/05/2022] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVE The main aim of this study was to analyze the performance of different artificial intelligence (AI) models in endoscopic colonic polyp detection and classification and compare them with doctors with different experience. METHODS We searched the studies on Colonoscopy, Colonic Polyps, Artificial Intelligence, Machine Learning, and Deep Learning published before May 2020 in PubMed, EMBASE, Cochrane, and the citation index of the conference proceedings. The quality of studies was assessed using the QUADAS-2 table of diagnostic test quality evaluation criteria. The random-effects model was calculated using Meta-DISC 1.4 and RevMan 5.3. RESULTS A total of 16 studies were included for meta-analysis. Only one study (1/16) presented externally validated results. The area under the curve (AUC) of AI group, expert group and non-expert group for detection and classification of colonic polyps were 0.940, 0.918, and 0.871, respectively. AI group had slightly lower pooled specificity than the expert group (79% vs. 86%, P < 0.05), but the pooled sensitivity was higher than the expert group (88% vs. 80%, P < 0.05). While the non-experts had less pooled specificity in polyp recognition than the experts (81% vs. 86%, P < 0.05), and higher pooled sensitivity than the experts (85% vs. 80%, P < 0.05). CONCLUSION The performance of AI in polyp detection and classification is similar to that of human experts, with high sensitivity and moderate specificity. Different tasks may have an impact on the performance of deep learning models and human experts, especially in terms of sensitivity and specificity.
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Affiliation(s)
- Ming-De Li
- grid.412615.50000 0004 1803 6239Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080 People’s Republic of China
| | - Ze-Rong Huang
- grid.412615.50000 0004 1803 6239Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080 People’s Republic of China
| | - Quan-Yuan Shan
- grid.412615.50000 0004 1803 6239Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080 People’s Republic of China
| | - Shu-Ling Chen
- grid.412615.50000 0004 1803 6239Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080 People’s Republic of China
| | - Ning Zhang
- grid.412615.50000 0004 1803 6239Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Hang-Tong Hu
- grid.412615.50000 0004 1803 6239Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080 People’s Republic of China
| | - Wei Wang
- grid.412615.50000 0004 1803 6239Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080 People’s Republic of China
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23
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Rondonotti E, Di Paolo D, Rizzotto ER, Alvisi C, Buscarini E, Spadaccini M, Tamanini G, Paggi S, Amato A, Scardino G, Romeo S, Alicante S, Ancona F, Guido E, Marzo V, Chicco F, Agazzi S, Rosa C, Correale L, Repici A, Hassan C, Radaelli F. Efficacy of a computer-aided detection system in a fecal immunochemical test-based organized colorectal cancer screening program: a randomized controlled trial (AIFIT study). Endoscopy 2022; 54:1171-1179. [PMID: 35545122 DOI: 10.1055/a-1849-6878] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
BACKGROUND Computer-aided detection (CADe) increases adenoma detection in primary screening colonoscopy. The potential benefit of CADe in a fecal immunochemical test (FIT)-based colorectal cancer (CRC) screening program is unknown. This study assessed whether use of CADe increases the adenoma detection rate (ADR) in a FIT-based CRC screening program. METHODS In a multicenter, randomized trial, FIT-positive individuals aged 50-74 years undergoing colonoscopy, were randomized (1:1) to receive high definition white-light (HDWL) colonoscopy, with or without a real-time deep-learning CADe by endoscopists with baseline ADR > 25 %. The primary outcome was ADR. Secondary outcomes were mean number of adenomas per colonoscopy (APC) and advanced adenoma detection rate (advanced-ADR). Subgroup analysis according to baseline endoscopists' ADR (≤ 40 %, 41 %-45 %, ≥ 46 %) was also performed. RESULTS 800 individuals (median age 61.0 years [interquartile range 55-67]; 409 men) were included: 405 underwent CADe-assisted colonoscopy and 395 underwent HDWL colonoscopy alone. ADR and APC were significantly higher in the CADe group than in the HDWL arm: ADR 53.6 % (95 %CI 48.6 %-58.5 %) vs. 45.3 % (95 %CI 40.3 %-50.45 %; RR 1.18; 95 %CI 1.03-1.36); APC 1.13 (SD 1.54) vs. 0.90 (SD 1.32; P = 0.03). No significant difference in advanced-ADR was found (18.5 % [95 %CI 14.8 %-22.6 %] vs. 15.9 % [95 %CI 12.5 %-19.9 %], respectively). An increase in ADR was observed in all endoscopist groups regardless of baseline ADR. CONCLUSIONS Incorporating CADe significantly increased ADR and APC in the framework of a FIT-based CRC screening program. The impact of CADe appeared to be consistent regardless of endoscopist baseline ADR.
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Affiliation(s)
| | - Dhanai Di Paolo
- Gastroenterology Unit, Valduce Hospital, Como, Italy.,Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Gastroenterology and Hepatology, Milan, Italy
| | - Erik Rosa Rizzotto
- Gastroenterology Unit, St. Antonio Hospital, Azienda Ospedaliera Universitaria, Padova, Italy
| | | | | | - Marco Spadaccini
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy.,Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Milan, Italy
| | | | - Silvia Paggi
- Gastroenterology Unit, Valduce Hospital, Como, Italy
| | - Arnaldo Amato
- Gastroenterology Unit, Valduce Hospital, Como, Italy
| | | | - Samanta Romeo
- Gastroenterology Unit, Azienda Ospedaliera "Ospedale Maggiore", Crema, Italy
| | - Saverio Alicante
- Gastroenterology Unit, Azienda Ospedaliera "Ospedale Maggiore", Crema, Italy
| | - Fabio Ancona
- Gastroenterology Unit, St. Antonio Hospital, Azienda Ospedaliera Universitaria, Padova, Italy
| | - Ennio Guido
- Gastroenterology Unit, St. Antonio Hospital, Azienda Ospedaliera Universitaria, Padova, Italy
| | | | - Fabio Chicco
- USD Endoscopia Digestiva, ASST Pavia, Pavia, Italy
| | | | - Cesare Rosa
- USD Endoscopia Digestiva, ASST Pavia, Pavia, Italy
| | - Loredana Correale
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
| | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy.,Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Milan, Italy
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy.,Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Milan, Italy
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24
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Doumat G, Daher D, Ghanem NN, Khater B. Knowledge and attitudes of medical students in Lebanon toward artificial intelligence: A national survey study. Front Artif Intell 2022; 5:1015418. [PMID: 36406470 PMCID: PMC9668059 DOI: 10.3389/frai.2022.1015418] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 09/06/2022] [Indexed: 11/07/2022] Open
Abstract
Purpose This study assesses the knowledge and attitudes of medical students in Lebanon toward Artificial Intelligence (AI) in medical education. It also explores the students' perspectives regarding the role of AI in medical education as a subject in the curriculum and a teaching tool. Methods This is a cross-sectional study using an online survey consisting of close-ended questions. The survey targets medical students at all medical levels across the 7 medical schools in Lebanon. Results A total of 206 medical students responded. When assessing AI knowledge sources (81.1%) got their information from the media as compared to (9.7%) from medical school curriculum. However, Students who learned the basics of AI as part of the medical school curriculum were more knowledge about AI than their peers who did not. Students in their clinical years appear to be more knowledgeable about AI in medicine. The advancements in AI affected the choice of specialty of around a quarter of the students (26.8%). Finally, only a quarter of students (26.5%) want to be assessed by AI, even though the majority (57.7%) reported that assessment by AI is more objective. Conclusions Education about AI should be incorporated in the medical school curriculum to improve the knowledge and attitudes of medical students. Improving AI knowledge in medical students will in turn increase acceptance of AI as a tool in medical education, thus unlocking its potential in revolutionizing medical education.
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Affiliation(s)
- George Doumat
- Faculty of Medicine, American University of Beirut, Beirut, Lebanon
| | - Darine Daher
- Faculty of Medicine, American University of Beirut, Beirut, Lebanon
| | - Nadim-Nicolas Ghanem
- Department of Family Medicine, Faculty of Medicine, American University of Beirut, Beirut, Lebanon
| | - Beatrice Khater
- Department of Family Medicine, Faculty of Medicine, American University of Beirut, Beirut, Lebanon
- *Correspondence: Beatrice Khater
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25
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Wang Z, Li Z, Xiao Y, Liu X, Hou M, Chen S. Three feature streams based on a convolutional neural network for early esophageal cancer identification. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:38001-38018. [DOI: 10.1007/s11042-022-13135-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Revised: 03/23/2021] [Accepted: 04/10/2022] [Indexed: 01/04/2025]
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An artificial intelligence algorithm is highly accurate for detecting endoscopic features of eosinophilic esophagitis. Sci Rep 2022; 12:11115. [PMID: 35778456 PMCID: PMC9249895 DOI: 10.1038/s41598-022-14605-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 06/09/2022] [Indexed: 11/17/2022] Open
Abstract
The endoscopic features associated with eosinophilic esophagitis (EoE) may be missed during routine endoscopy. We aimed to develop and evaluate an Artificial Intelligence (AI) algorithm for detecting and quantifying the endoscopic features of EoE in white light images, supplemented by the EoE Endoscopic Reference Score (EREFS). An AI algorithm (AI-EoE) was constructed and trained to differentiate between EoE and normal esophagus using endoscopic white light images extracted from the database of the University Hospital Augsburg. In addition to binary classification, a second algorithm was trained with specific auxiliary branches for each EREFS feature (AI-EoE-EREFS). The AI algorithms were evaluated on an external data set from the University of North Carolina, Chapel Hill (UNC), and compared with the performance of human endoscopists with varying levels of experience. The overall sensitivity, specificity, and accuracy of AI-EoE were 0.93 for all measures, while the AUC was 0.986. With additional auxiliary branches for the EREFS categories, the AI algorithm (AI-EoE-EREFS) performance improved to 0.96, 0.94, 0.95, and 0.992 for sensitivity, specificity, accuracy, and AUC, respectively. AI-EoE and AI-EoE-EREFS performed significantly better than endoscopy beginners and senior fellows on the same set of images. An AI algorithm can be trained to detect and quantify endoscopic features of EoE with excellent performance scores. The addition of the EREFS criteria improved the performance of the AI algorithm, which performed significantly better than endoscopists with a lower or medium experience level.
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27
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Tang CP, Lin TL, Hsieh YH, Hsieh CH, Tseng CW, Leung FW. Polyp detection and false-positive rates by computer-aided analysis of withdrawal-phase videos of colonoscopy of the right-sided colon segment in a randomized controlled trial comparing water exchange and air insufflation. Gastrointest Endosc 2022; 95:1198-1206.e6. [PMID: 34973967 DOI: 10.1016/j.gie.2021.12.020] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 12/17/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIMS Water exchange (WE) improves lesion detection but misses polyps because of human limitations. Computer-aided detection (CADe) identifies additional polyps overlooked by the colonoscopist. Additional polyp detection rate (APDR) is the proportion of patients with at least 1 additional polyp detected by CADe. The number of false positives (because of feces and air bubble) per colonoscopy (FPPC) is a major CADe limitation, which might be reduced by salvage cleaning with WE. We compared the APDR and FPPC by CADe between videos of WE and air insufflation in the right-sided colon. METHODS CADe used a convolutional neural network with transfer learning. We edited and coded withdrawal-phase videos in a randomized controlled trial that compared right-sided colon findings between air insufflation and WE. Two experienced blinded endoscopists analyzed the CADe-overlaid videos and identified additional polyps by consensus. An artifact triggered by CADe but not considered a polyp by the reviewers was defined as a false positive. The primary outcome was APDR. RESULTS Two hundred forty-five coded videos of colonoscopies inserted with WE (n = 123) and air insufflation (n = 122) methods were analyzed. The APDR in the WE group was significantly higher (37 [30.1%] vs 15 [12.3%], P = .001). The mean [standard deviation] FPPC related to feces (1.78 [1.67] vs 2.09 [2.09], P = .007) and bubbles (.53 [.89] vs 1.25 [2.45], P = .001) in the WE group were significantly lower. CONCLUSIONS CADe showed significantly higher APDR and lower number of FPPC related to feces and bubbles in the WE group. The results support the hypothesis that the strengths of CADe and WE complement the weaknesses of each other in optimizing polyp detection.
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Affiliation(s)
- Chia-Pei Tang
- Division of Gastroenterology, Department of Internal Medicine, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan; School of Medicine, Tzu Chi University, Hualien City, Taiwan
| | - Tu-Liang Lin
- Department of Management Information Systems, National Chiayi University, Chiayi, Taiwan
| | - Yu-Hsi Hsieh
- Division of Gastroenterology, Department of Internal Medicine, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan; School of Medicine, Tzu Chi University, Hualien City, Taiwan
| | - Chen-Hung Hsieh
- Department of Management Information Systems, National Chiayi University, Chiayi, Taiwan
| | - Chih-Wei Tseng
- Division of Gastroenterology, Department of Internal Medicine, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan; School of Medicine, Tzu Chi University, Hualien City, Taiwan
| | - Felix W Leung
- Sepulveda Ambulatory Care Center, Veterans Affairs Greater Los Angeles Healthcare System, North Hills, California, USA; David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California, USA
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Maulahela H, Annisa NG. Current advancements in application of artificial intelligence in clinical decision-making by gastroenterologists in gastrointestinal bleeding. Artif Intell Gastroenterol 2022; 3:13-20. [DOI: 10.35712/aig.v3.i1.13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 01/24/2022] [Accepted: 02/23/2022] [Indexed: 02/06/2023] Open
Abstract
Artificial Intelligence (AI) is a type of intelligence that comes from machines or computer systems that mimics human cognitive function. Recently, AI has been utilized in medicine and helped clinicians make clinical decisions. In gastroenterology, AI has assisted colon polyp detection, optical biopsy, and diagnosis of Helicobacter pylori infection. AI also has a broad role in the clinical prediction and management of gastrointestinal bleeding. Machine learning can determine the clinical risk of upper and lower gastrointestinal bleeding. AI can assist the management of gastrointestinal bleeding by identifying high-risk patients who might need urgent endoscopic treatment or blood transfusion, determining bleeding stigmata during endoscopy, and predicting recurrence of gastrointestinal bleeding. The present review will discuss the role of AI in the clinical prediction and management of gastrointestinal bleeding, primarily on how it could assist gastroenterologists in their clinical decision-making compared to conventional methods. This review will also discuss challenges in implementing AI in routine practice.
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Affiliation(s)
- Hasan Maulahela
- Department of Internal Medicine, Gastroenterology Division, Faculty of Medicine University of Indonesia - Cipto Mangunkusumo General Central National Hospital, Jakarta 10430, Indonesia
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29
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Mutaguchi J, Morooka KI, Kobayashi S, Umehara A, Miyauchi S, Kinoshita F, Inokuchi J, Oda Y, Kurazume R, Eto M. Artificial intelligence for segmentation of bladder tumor cystoscopic images performed by U-Net with dilated convolution. J Endourol 2022; 36:827-834. [PMID: 35018828 DOI: 10.1089/end.2021.0483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Early intravesical recurrence after transurethral resection of bladder tumors (TURBT) is often caused by overlooking of tumors during TURBT. Although narrow-band imaging and photodynamic diagnosis were developed to detect more tumors than conventional white-light imaging, the accuracy of these systems has been subjective, along with poor reproducibility due to their dependence on the physician's experience and skills. To create an objective and reproducible diagnosing system, we aimed to assess the utility of artificial intelligence (AI) with Dilated U-Net to reduce the risk of overlooked bladder tumors when compared with the conventional AI system, termed U-Net. MATERIAL AND METHODS We retrospectively obtained cystoscopic images by converting videos obtained from 120 patients who underwent TURBT into 1,790 cystoscopic images. The Dilated U-Net, which is an extension of the conventional U-Net, analyzed these image datasets. The diagnostic accuracy of the Dilated U-Net and conventional U-Net were compared using the following four measurements: pixel-wise sensitivity (PWSe); pixel-wise specificity (PWSp); pixel-wise positive predictive value (PWPPV), representing the AI diagnostic accuracy per pixel; and dice similarity coefficient (DSC), representing the overlap area between the bladder tumors in the ground truth images and segmentation maps. RESULTS The cystoscopic images were divided as follows, according to the pathological T-stage: 944, Ta; 412, T1; 329, T2; and 116, carcinoma in-situ. The PWSe, PWSp, PWPPV, and DSC of the Dilated U-Net were 84.9%, 88.5%, 86.7%, and 83.0%, respectively, which had improved when compared to that with the conventional U-Net by 1.7%, 1.3%, 2.1%, and 2.3%, respectively. The DSC values were high for elevated lesions and low for flat lesions for both Dilated and conventional U-Net. CONCLUSIONS Dilated U-Net, with higher DSC values than conventional U-Net, might reduce the risk of overlooking bladder tumors during cystoscopy and TURBT.
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Affiliation(s)
- Jun Mutaguchi
- Kyushu University Hospital, 145181, Urology, Fukuoka, Japan.,Kyushu University Hospital, 145181, Advanced Medical Initiatives Faculty of Medical Sciences, Fukuoka, Japan;
| | - Ken Ichi Morooka
- Okayama University, 12997, Graduate School of Natural Science and Technology, Okayama, Japan;
| | - Satoshi Kobayashi
- Kyushu University Hospital, 145181, urology, Fukuoka, Japan.,Kyushu University Hospital, 145181, Advanced Medical Initiatives Faculty of Medical Sciences, Fukuoka, Japan;
| | - Aiko Umehara
- Kyushu University, 12923, Graduate School of Information Science and Electrical Engineering, Fukuoka, Japan;
| | - Shoko Miyauchi
- Kyushu University, 12923, Graduate School of Information Science and Electrical Engineering, Fukuoka, Japan;
| | - Fumio Kinoshita
- Kyushu University Hospital, 145181, Urology, Fukuoka, Japan.,Kyushu University Hospital, 145181, Anatomic Pathology, Graduate School of Medical Sciences, Fukuoka, Japan;
| | | | - Yoshinao Oda
- Kyushu University Hospital, 145181, Anatomic Pathology, Graduate School of Medical Sciences, Fukuoka, Japan;
| | - Ryo Kurazume
- Kyushu University, 12923, Graduate School of Information Science and Electrical Engineering, Fukuoka, Japan;
| | - Masatoshi Eto
- Kyushu University Hospital, 145181, Urology, Fukuoka, Japan.,Kyushu University Hospital, 145181, Advanced Medical Initiatives Faculty of Medical Sciences, Fukuoka, Japan;
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30
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Glissen Brown JR, Waljee AK, Mori Y, Sharma P, Berzin TM. Charting a path forward for clinical research in artificial intelligence and gastroenterology. Dig Endosc 2022; 34:4-12. [PMID: 33715244 DOI: 10.1111/den.13974] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 03/02/2021] [Accepted: 03/11/2021] [Indexed: 12/12/2022]
Abstract
Gastroenterology has been an early leader in bridging the gap between artificial intelligence (AI) model development and clinical trial validation, and in recent years we have seen the publication of several randomized clinical trials examining the role of AI in gastroenterology. As AI applications for clinical medicine advance rapidly, there is a clear need for guidance surrounding AI-specific study design, evaluation, comparison, analysis and reporting of results. Several initiatives are in the publication or pre-publication phase including AI-specific amendments to minimum reporting guidelines for clinical trials, society task force initiatives aimed at priority use cases and research priorities, and minimum reporting guidelines that guide the reporting of clinical prediction models. In this paper, we examine applications of AI in clinical trials and discuss elements of newly published AI-specific extensions to the Consolidated Standards of Reporting Trials and Standard Protocol Items: Recommendations for Interventional Trials statements that guide clinical trial reporting and development. We then review AI applications at the pre-trial level in both endoscopy and other subfields of gastroenterology and explore areas where further guidance is needed to supplement the current guidance available at the pre-trial level.
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Affiliation(s)
- Jeremy R Glissen Brown
- Center for Advanced Endoscopy, Division of Gastroenterology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, USA
| | - Akbar K Waljee
- Division of Gastroenterology, University of Michigan Health System, University of Michigan, Ann Arbor, USA
| | - Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan.,Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo, Oslo, Norway
| | - Prateek Sharma
- Department of Gastroenterology and Hepatology, University of Kansas Medical Center, Kansas City, KS, USA.,Department of Gastroenterology, Kansas City VA Medical Center, Kansas City, USA
| | - Tyler M Berzin
- Center for Advanced Endoscopy, Division of Gastroenterology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, USA
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31
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AIM in Endoscopy Procedures. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Key Words
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