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Aoyama Y, Matsunobu Y, Etoh T, Suzuki K, Fujita S, Aiba T, Fujishima H, Empuku S, Kono Y, Endo Y, Ueda Y, Shiroshita H, Kamiyama T, Sugita T, Morishima K, Ebe K, Tokuyasu T, Inomata M. Artificial intelligence for surgical safety during laparoscopic gastrectomy for gastric cancer: Indication of anatomical landmarks related to postoperative pancreatic fistula using deep learning. Surg Endosc 2024:10.1007/s00464-024-11117-x. [PMID: 39093411 DOI: 10.1007/s00464-024-11117-x] [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: 04/01/2024] [Accepted: 07/23/2024] [Indexed: 08/04/2024]
Abstract
BACKGROUND Postoperative pancreatic fistula (POPF) is a critical complication of laparoscopic gastrectomy (LG). However, there are no widely recognized anatomical landmarks to prevent POPF during LG. This study aimed to identify anatomical landmarks related to POPF occurrence during LG for gastric cancer and to develop an artificial intelligence (AI) navigation system for indicating these landmarks. METHODS Dimpling lines (DLs)-depressions formed between the pancreas and surrounding organs-were defined as anatomical landmarks related to POPF. The DLs for the mesogastrium, intestine, and transverse mesocolon were named DMP, DIP, and DTP, respectively. We included 50 LG cases to develop the AI system (45/50 were used for training and 5/50 for adjusting the hyperparameters of the employed system). Regarding the validation of the AI system, DLs were assessed by an external evaluation committee using a Likert scale, and the pancreas was assessed using the Dice coefficient, with 10 prospectively registered cases. RESULTS Six expert surgeons confirmed the efficacy of DLs as anatomical landmarks related to POPF in LG. An AI system was developed using a semantic segmentation model that indicated DLs in real-time when this system was synchronized during surgery. Additionally, the distribution of scores for DMP was significantly higher than that of the other DLs (p < 0.001), indicating the relatively high accuracy of this landmark. In addition, the Dice coefficient of the pancreas was 0.70. CONCLUSIONS The DLs may be used as anatomical landmarks related to POPF occurrence. The developed AI navigation system can help visualize the DLs in real-time during LG.
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Affiliation(s)
- Yoshimasa Aoyama
- Department of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, Oita, Japan
| | - Yusuke Matsunobu
- Department of Information Systems and Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan
- Department of Healthcare AI Data Science, Faculty of Medicine, Oita University, Oita, Japan
| | - Tsuyoshi Etoh
- Department of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, Oita, Japan.
- Research Center for GLOBAL and LOCAL Infectious Diseases, Oita University, 1-1 Idaigaoka, Hasama-Machi, Oita, Oita, 879-5593, Japan.
| | - Kosuke Suzuki
- Department of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, Oita, Japan
| | - Shunsuke Fujita
- Department of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, Oita, Japan
| | - Takayuki Aiba
- Department of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, Oita, Japan
| | - Hajime Fujishima
- Department of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, Oita, Japan
| | - Shinichiro Empuku
- Department of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, Oita, Japan
| | - Yohei Kono
- Department of Advanced Medical Research and Development for Cancer and Hair [Aderans], Faculty of Medicine, Oita University, Oita, Japan
| | - Yuichi Endo
- Department of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, Oita, Japan
| | - Yoshitake Ueda
- Department of Comprehensive Surgery for Community Medicine, Faculty of Medicine, Oita University, Oita, Japan
| | - Hidefumi Shiroshita
- Department of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, Oita, Japan
| | - Toshiya Kamiyama
- Advanced AI Technology Research, Advanced Software Technology Research, Olympus Corporation, Tokyo, Japan
| | - Takemasa Sugita
- Advanced AI Technology Research, Advanced Software Technology Research, Olympus Corporation, Tokyo, Japan
| | - Kenichi Morishima
- Advanced AI Technology Research, Advanced Software Technology Research, Olympus Corporation, Tokyo, Japan
| | - Kohei Ebe
- Information Aided Medical Solutions Development, Application Software Engineering, Olympus Medical Systems Corporation, Tokyo, Japan
| | - Tatsushi Tokuyasu
- Department of Information Systems and Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan
- Clinical Engineering Research Center, Faculty of Medicine, Oita University, Oita, Japan
| | - Masafumi Inomata
- Department of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, Oita, Japan
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Kinoshita K, Maruyama T, Kobayashi N, Imanishi S, Maruyama M, Ohira G, Endo S, Tochigi T, Kinoshita M, Fukui Y, Kumauzu Y, Kita J, Shinohara H, Matsubara H. An artificial intelligence-based nerve recognition model is useful as surgical support technology and as an educational tool in laparoscopic and robot-assisted rectal cancer surgery. Surg Endosc 2024:10.1007/s00464-024-10939-z. [PMID: 39073558 DOI: 10.1007/s00464-024-10939-z] [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: 01/07/2024] [Accepted: 05/17/2024] [Indexed: 07/30/2024]
Abstract
BACKGROUND Artificial intelligence (AI) has the potential to enhance surgical practice by predicting anatomical structures within the surgical field, thereby supporting surgeons' experiences and cognitive skills. Preserving and utilising nerves as critical guiding structures is paramount in rectal cancer surgery. Hence, we developed a deep learning model based on U-Net to automatically segment nerves. METHODS The model performance was evaluated using 60 randomly selected frames, and the Dice and Intersection over Union (IoU) scores were quantitatively assessed by comparing them with ground truth data. Additionally, a questionnaire was administered to five colorectal surgeons to gauge the extent of underdetection, overdetection, and the practical utility of the model in rectal cancer surgery. Furthermore, we conducted an educational assessment of non-colorectal surgeons, trainees, physicians, and medical students. We evaluated their ability to recognise nerves in mesorectal dissection scenes, scored them on a 12-point scale, and examined the score changes before and after exposure to the AI analysis videos. RESULTS The mean Dice and IoU scores for the 60 test frames were 0.442 (range 0.0465-0.639) and 0.292 (range 0.0238-0.469), respectively. The colorectal surgeons revealed an under-detection score of 0.80 (± 0.47), an over-detection score of 0.58 (± 0.41), and a usefulness evaluation score of 3.38 (± 0.43). The nerve recognition scores of non-colorectal surgeons, rotating residents, and medical students significantly improved by simply watching the AI nerve recognition videos for 1 min. Notably, medical students showed a more substantial increase in nerve recognition scores when exposed to AI nerve analysis videos than when exposed to traditional lectures on nerves. CONCLUSIONS In laparoscopic and robot-assisted rectal cancer surgeries, the AI-based nerve recognition model achieved satisfactory recognition levels for expert surgeons and demonstrated effectiveness in educating junior surgeons and medical students on nerve recognition.
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Affiliation(s)
- Kazuya Kinoshita
- Department of Frontier Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
- Department of General Surgery, Kumagaya General Hospital, Saitama, Japan
| | - Tetsuro Maruyama
- Department of Frontier Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan.
| | - Nao Kobayashi
- Anaut Inc, 703, 36-3, Nihonbashi Hakozakicho, Chuoku, Tokyo, Japan
| | - Shunsuke Imanishi
- Department of Frontier Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Michihiro Maruyama
- Department of Frontier Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Gaku Ohira
- Department of Frontier Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Satoshi Endo
- Department of Frontier Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Toru Tochigi
- Department of Frontier Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Mayuko Kinoshita
- Department of Frontier Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Yudai Fukui
- Department of Gastroenterological Surgery, Toranomon Hospital, Tokyo, Japan
| | - Yuta Kumauzu
- Anaut Inc, 703, 36-3, Nihonbashi Hakozakicho, Chuoku, Tokyo, Japan
- Department of Surgery, Yokohama City University, Kanagawa, Japan
| | - Junji Kita
- Department of General Surgery, Kumagaya General Hospital, Saitama, Japan
| | - Hisashi Shinohara
- Department of Gastroenterological Surgery, Hyogo College of Medicine, Hyogo, Japan
| | - Hisahiro Matsubara
- Department of Frontier Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
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Uema R, Hayashi Y, Kizu T, Igura T, Ogiyama H, Yamada T, Takeda R, Nagai K, Inoue T, Yamamoto M, Yamaguchi S, Kanesaka T, Yoshihara T, Kato M, Yoshii S, Tsujii Y, Shinzaki S, Takehara T. A novel artificial intelligence-based endoscopic ultrasonography diagnostic system for diagnosing the invasion depth of early gastric cancer. J Gastroenterol 2024; 59:543-555. [PMID: 38713263 PMCID: PMC11217111 DOI: 10.1007/s00535-024-02102-1] [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: 10/13/2023] [Accepted: 03/30/2024] [Indexed: 05/08/2024]
Abstract
BACKGROUND We developed an artificial intelligence (AI)-based endoscopic ultrasonography (EUS) system for diagnosing the invasion depth of early gastric cancer (EGC), and we evaluated the performance of this system. METHODS A total of 8280 EUS images from 559 EGC cases were collected from 11 institutions. Within this dataset, 3451 images (285 cases) from one institution were used as a development dataset. The AI model consisted of segmentation and classification steps, followed by the CycleGAN method to bridge differences in EUS images captured by different equipment. AI model performance was evaluated using an internal validation dataset collected from the same institution as the development dataset (1726 images, 135 cases). External validation was conducted using images collected from the other 10 institutions (3103 images, 139 cases). RESULTS The area under the curve (AUC) of the AI model in the internal validation dataset was 0.870 (95% CI: 0.796-0.944). Regarding diagnostic performance, the accuracy/sensitivity/specificity values of the AI model, experts (n = 6), and nonexperts (n = 8) were 82.2/63.4/90.4%, 81.9/66.3/88.7%, and 68.3/60.9/71.5%, respectively. The AUC of the AI model in the external validation dataset was 0.815 (95% CI: 0.743-0.886). The accuracy/sensitivity/specificity values of the AI model (74.1/73.1/75.0%) and the real-time diagnoses of experts (75.5/79.1/72.2%) in the external validation dataset were comparable. CONCLUSIONS Our AI model demonstrated a diagnostic performance equivalent to that of experts.
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Affiliation(s)
- Ryotaro Uema
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Yoshito Hayashi
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Takashi Kizu
- Department of Gastroenterology, Yao Municipal Hospital, Yao, 581-0069, Japan
| | - Takumi Igura
- Department of Gastroenterology, Sumitomo Hospital, Osaka, 530-0005, Japan
| | - Hideharu Ogiyama
- Department of Gastroenterology, Ikeda Municipal Hospital, Ikeda, 563-0025, Japan
| | - Takuya Yamada
- Department of Gastroenterology, Osaka Rosai Hospital, Sakai, 591-8025, Japan
| | - Risato Takeda
- Department of Gastroenterology, Itami City Hospital, Itami, 664-0015, Japan
| | - Kengo Nagai
- Department of Gastroenterology, Suita Municipal Hospital, Suita, 564-0018, Japan
| | - Takuya Inoue
- Department of Gastroenterology, Osaka General Medical Center, Osaka, 558-8558, Japan
| | - Masashi Yamamoto
- Department of Gastroenterology, Toyonaka Municipal Hospital, Toyonaka, 560-8565, Japan
| | - Shinjiro Yamaguchi
- Department of Gastroenterology, Kansai Rosai Hospital, Amagasaki, 660-0064, Japan
| | - Takashi Kanesaka
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, 540-0008, Japan
| | - Takeo Yoshihara
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Minoru Kato
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, 540-0008, Japan
| | - Shunsuke Yoshii
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, 540-0008, Japan
| | - Yoshiki Tsujii
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Shinichiro Shinzaki
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
- Department of Gastroenterology, Faculty of Medicine, Hyogo Medical University, Nishinomiya, 663-8501, Japan
| | - Tetsuo Takehara
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan.
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4
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Yousef AM, Deliyski DD, Zacharias SRC, Naghibolhosseini M. Detection of Vocal Fold Image Obstructions in High-Speed Videoendoscopy During Connected Speech in Adductor Spasmodic Dysphonia: A Convolutional Neural Networks Approach. J Voice 2024; 38:951-962. [PMID: 35304042 PMCID: PMC9474736 DOI: 10.1016/j.jvoice.2022.01.028] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 01/30/2022] [Accepted: 01/30/2022] [Indexed: 01/10/2023]
Abstract
OBJECTIVE Adductor spasmodic dysphonia (AdSD) is a neurogenic voice disorder, affecting the intrinsic laryngeal muscle control. AdSD leads to involuntary laryngeal spasms and only reveals during connected speech. Laryngeal high-speed videoendoscopy (HSV) coupled with a flexible fiberoptic endoscope provides a unique opportunity to study voice production and visualize the vocal fold vibrations in AdSD during speech. The goal of this study is to automatically detect instances during which the image of the vocal folds is optically obstructed in HSV recordings obtained during connected speech. METHODS HSV data were recorded from vocally normal adults and patients with AdSD during reading of the "Rainbow Passage", six CAPE-V sentences, and production of the vowel /i/. A convolutional neural network was developed and trained as a classifier to detect obstructed/unobstructed vocal folds in HSV frames. Manually labelled data were used for training, validating, and testing of the network. Moreover, a comprehensive robustness evaluation was conducted to compare the performance of the developed classifier and visual analysis of HSV data. RESULTS The developed convolutional neural network was able to automatically detect the vocal fold obstructions in HSV data in vocally normal participants and AdSD patients. The trained network was tested successfully and showed an overall classification accuracy of 94.18% on the testing dataset. The robustness evaluation showed an average overall accuracy of 94.81% on a massive number of HSV frames demonstrating the high robustness of the introduced technique while keeping a high level of accuracy. CONCLUSIONS The proposed approach can be used for efficient analysis of HSV data to study laryngeal maneuvers in patients with AdSD during connected speech. Additionally, this method will facilitate development of vocal fold vibratory measures for HSV frames with an unobstructed view of the vocal folds. Indicating parts of connected speech that provide an unobstructed view of the vocal folds can be used for developing optimal passages for precise HSV examination during connected speech and subject-specific clinical voice assessment protocols.
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Affiliation(s)
- Ahmed M Yousef
- Department of Communicative Sciences and Disorders, Michigan State University, East Lansing, Michigan
| | - Dimitar D Deliyski
- Department of Communicative Sciences and Disorders, Michigan State University, East Lansing, Michigan
| | - Stephanie R C Zacharias
- Head and Neck Regenerative Medicine Program, Mayo Clinic, Scottsdale, Arizona; Department of Otolaryngology-Head and Neck Surgery, Mayo Clinic, Phoenix, Arizona
| | - Maryam Naghibolhosseini
- Department of Communicative Sciences and Disorders, Michigan State University, East Lansing, Michigan.
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Ahn J, Choi M. Advancements and turning point of artificial intelligence in ophthalmology: A comprehensive analysis of research trends and collaborative networks. Ophthalmic Physiol Opt 2024; 44:1031-1040. [PMID: 38581209 DOI: 10.1111/opo.13315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 04/08/2024]
Abstract
Artificial intelligence (AI) has emerged as a transformative force with great potential in various fields, including healthcare. In recent years, AI has garnered significant attention due to its potential to revolutionise ophthalmology, leading to advancements in patient care such as disease detection, diagnosis, treatment and monitoring of disease progression. This study presents a comprehensive analysis of the research trends and collaborative networks at the intersection of AI and ophthalmology. In this study, we conducted an extensive search of the Web of Science Core Collection to identify articles related to 'artificial intelligence' in ophthalmology published from 1968 to 2023. We performed co-occurrence keywords and co-authorship network analyses using VOSviewer software to explore the relationships between keywords and country collaboration. We found a remarkable surge in articles applying AI in ophthalmology after 2017, marking a turning point in the integration of AI within the medical field. The primary application of AI shifted towards the diagnosis of ocular disease, which was particularly evident through keywords such as glaucoma, diabetic retinopathy and age-related macular degeneration. Analysis of the collaboration networks of countries revealed a global expansion of ophthalmology-related AI research. This study provides valuable insights into the evolving landscape of AI integration in ophthalmology, indicating its growing potential for enhancing disease detection, diagnosis, treatment planning and monitoring of disease progression. In order to translate AI technologies into clinical practice effectively, it is imperative to comprehend the evolving research trends and advancements at the intersection of AI and ophthalmology.
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Affiliation(s)
- Jihye Ahn
- Department of Optometry, College of Energy and Biotechnology, Seoul National University of Science and Technology, Seoul, Republic of Korea
| | - Moonsung Choi
- Department of Optometry, College of Energy and Biotechnology, Seoul National University of Science and Technology, Seoul, Republic of Korea
- Convergence Institute of Biomedical Engineering and Biomaterials, Seoul National University of Science and Technology, Seoul, Republic of Korea
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Maity R, Raja Sankari VM, U S, N A R, Salvador AL. Explainable AI based automated segmentation and multi-stage classification of gastroesophageal reflux using machine learning techniques. Biomed Phys Eng Express 2024; 10:045058. [PMID: 38901416 DOI: 10.1088/2057-1976/ad5a14] [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: 01/14/2024] [Accepted: 06/20/2024] [Indexed: 06/22/2024]
Abstract
Presently, close to two million patients globally succumb to gastrointestinal reflux diseases (GERD). Video endoscopy represents cutting-edge technology in medical imaging, facilitating the diagnosis of various gastrointestinal ailments including stomach ulcers, bleeding, and polyps. However, the abundance of images produced by medical video endoscopy necessitates significant time for doctors to analyze them thoroughly, posing a challenge for manual diagnosis. This challenge has spurred research into computer-aided techniques aimed at diagnosing the plethora of generated images swiftly and accurately. The novelty of the proposed methodology lies in the development of a system tailored for the diagnosis of gastrointestinal diseases. The proposed work used an object detection method called Yolov5 for identifying abnormal region of interest and Deep LabV3+ for segmentation of abnormal regions in GERD. Further, the features are extracted from the segmented image and given as an input to the seven different machine learning classifiers and custom deep neural network model for multi-stage classification of GERD. The DeepLabV3+ attains an excellent segmentation accuracy of 95.2% and an F1 score of 93.3%. The custom dense neural network obtained a classification accuracy of 90.5%. Among the seven different machine learning classifiers, support vector machine (SVM) outperformed with classification accuracy of 87% compared to all other class outperformed combination of object detection, deep learning-based segmentation and machine learning classification enables the timely identification and surveillance of problems associated with GERD for healthcare providers.
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Affiliation(s)
- Rudrani Maity
- Biomedical Engineering Department, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, 603203, Tamil Nadu, India
| | - V M Raja Sankari
- Biomedical Engineering Department, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, 603203, Tamil Nadu, India
| | - Snekhalatha U
- Biomedical Engineering Department, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, 603203, Tamil Nadu, India
- College of Engineering, Architecture and Fine Arts, Batangas State University, Batangas, Philippines
| | - Rajesh N A
- Department of Medical Gastroenterology, SRM Medical College, Hospital and Research centre, Kattankulathur, 603203, Tamil Nadu, India
| | - Anela L Salvador
- College of Engineering, Architecture and Fine Arts, Batangas State University, Batangas, Philippines
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7
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Joo DC, Kim GH, Lee MW, Lee BE, Kim JW, Kim KB. Artificial Intelligence-Based Diagnosis of Gastric Mesenchymal Tumors Using Digital Endosonography Image Analysis. J Clin Med 2024; 13:3725. [PMID: 38999291 PMCID: PMC11242784 DOI: 10.3390/jcm13133725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Revised: 06/13/2024] [Accepted: 06/23/2024] [Indexed: 07/14/2024] Open
Abstract
Background/Objectives: Artificial intelligence (AI)-assisted endoscopic ultrasonography (EUS) diagnostic tools have shown excellent performance in diagnosing gastric mesenchymal tumors. This study aimed to assess whether incorporating clinical and endoscopic factors into AI-assisted EUS classification models based on digital image analysis could improve the diagnostic performance of AI-assisted EUS diagnostic tools. Methods: We retrospectively analyzed the data of 464 patients who underwent both EUS and surgical resection of gastric mesenchymal tumors, including 294 gastrointestinal stromal tumors (GISTs), 52 leiomyomas, and 41 schwannomas. AI-assisted classification models for GISTs and non-GIST tumors were developed utilizing clinical and endoscopic factors and digital EUS image analysis. Results: Regarding the baseline EUS classification models, the area under the receiver operating characteristic (AUC) values of the logistic regression, decision tree, random forest, K-nearest neighbor (KNN), and support vector machine (SVM) models were 0.805, 0.673, 0.781, 0.740, and 0.791, respectively. Using the new classification models incorporating clinical and endoscopic factors into the baseline classification models, the AUC values of the logistic regression, decision tree, random forest, KNN, and SVM models increased to 0.853, 0.715, 0.896, 0.825, and 0.794, respectively. In particular, the random forest and KNN models exhibited significant improvement in performance in Delong's test (both p < 0.001). Conclusion: The diagnostic performance of the AI-assisted EUS classification models improved when clinical and endoscopic factors were incorporated. Our results provided direction for developing new AI-assisted EUS models for gastric mesenchymal tumors.
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Affiliation(s)
- Dong Chan Joo
- Department of Internal Medicine, Pusan National University School of Medicine and Biomedical Research Institute, Pusan National University Hospital, Busan 49241, Republic of Korea
| | - Gwang Ha Kim
- Department of Internal Medicine, Pusan National University School of Medicine and Biomedical Research Institute, Pusan National University Hospital, Busan 49241, Republic of Korea
| | - Moon Won Lee
- Department of Internal Medicine, Pusan National University School of Medicine and Biomedical Research Institute, Pusan National University Hospital, Busan 49241, Republic of Korea
| | - Bong Eun Lee
- Department of Internal Medicine, Pusan National University School of Medicine and Biomedical Research Institute, Pusan National University Hospital, Busan 49241, Republic of Korea
| | - Ji Woo Kim
- Department of Convergence Medical Sciences, The Graduate School Pusan National University, Busan 46241, Republic of Korea
| | - Kwang Baek Kim
- Department of Computer Engineering, Silla University, Busan 46958, Republic of Korea
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Pattilachan TM, Christodoulou M, Ross S. Diagnosis to dissection: AI's role in early detection and surgical intervention for gastric cancer. J Robot Surg 2024; 18:259. [PMID: 38900376 DOI: 10.1007/s11701-024-02005-6] [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: 05/09/2024] [Accepted: 06/01/2024] [Indexed: 06/21/2024]
Abstract
Gastric cancer remains a formidable health challenge worldwide; early detection and effective surgical intervention are critical for improving patient outcomes. This comprehensive review explores the evolving landscape of gastric cancer management, emphasizing the significant contributions of artificial intelligence (AI) in revolutionizing both diagnostic and therapeutic approaches. Despite advancements in the medical field, the subtle nature of early gastric cancer symptoms often leads to late-stage diagnoses, where survival rates are notably decreased. Historically, the treatment of gastric cancer has transitioned from palliative care to surgical resection, evolving further with the introduction of minimally invasive surgical (MIS) techniques. In the current era, AI has emerged as a transformative force, enhancing the precision of early gastric cancer detection through sophisticated image analysis, and supporting surgical decision-making with predictive modeling and real-time preop-, intraop-, and postoperative guidance. However, the deployment of AI in healthcare raises significant ethical, legal, and practical challenges, including the necessity for ongoing professional education and the development of standardized protocols to ensure patient safety and the effective use of AI technologies. Future directions point toward a synergistic integration of AI with clinical best practices, promising a new era of personalized, efficient, and safer gastric cancer management.
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Affiliation(s)
- Tara Menon Pattilachan
- AdventHealth Tampa, Surgery College of Medicine, Digestive Health Institute, University of Central Florida (UCF), 3000 Medical Park Drive, Suite #500, Tampa, FL, 33613, USA
| | - Maria Christodoulou
- AdventHealth Tampa, Surgery College of Medicine, Digestive Health Institute, University of Central Florida (UCF), 3000 Medical Park Drive, Suite #500, Tampa, FL, 33613, USA
| | - Sharona Ross
- AdventHealth Tampa, Surgery College of Medicine, Digestive Health Institute, University of Central Florida (UCF), 3000 Medical Park Drive, Suite #500, Tampa, FL, 33613, USA.
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Nijjar GS, Aulakh SK, Singh R, Chandi SK. Emerging Technologies in Endoscopy for Gastrointestinal Neoplasms: A Comprehensive Overview. Cureus 2024; 16:e62946. [PMID: 39044885 PMCID: PMC11265259 DOI: 10.7759/cureus.62946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/22/2024] [Indexed: 07/25/2024] Open
Abstract
Gastrointestinal neoplasms are a growing global health concern, requiring prompt identification and treatment. Endoscopic procedures have revolutionized the detection and treatment of gastrointestinal tumors by providing accurate, minimally invasive methods. Early-stage malignancies can be treated with endoscopic excision, leading to improved outcomes and increased survival rates. Precancerous lesions, like adenomatous polyps, can be prevented by removing them, reducing cancer occurrence and death rates. Advanced techniques like chromoendoscopy, narrow-band imaging, and confocal laser endomicroscopy improve the ability to see the mucosa surface and diagnose conditions. Artificial Intelligence (AI) applications in endoscopy can enhance diagnostic accuracy and predict histology outcomes. However, challenges remain in accurately defining lesions and ensuring precise diagnosis and treatment selection. Molecular imaging approaches and therapeutic modalities like photodynamic therapy and endoscopic ultrasonography-guided therapies hold potential but require further study and clinical confirmation. This study examines the future prospects and obstacles in endoscopic procedures for the timely identification and treatment of gastrointestinal cancers. The focus is on developing technology, limits, and prospective effects on clinical practice.
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Affiliation(s)
| | - Smriti Kaur Aulakh
- Internal Medicine, Sri Guru Ram Das University of Health Science and Research, Amritsar, IND
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Horita K, Hida K, Itatani Y, Fujita H, Hidaka Y, Yamamoto G, Ito M, Obama K. Real-time detection of active bleeding in laparoscopic colectomy using artificial intelligence. Surg Endosc 2024; 38:3461-3469. [PMID: 38760565 DOI: 10.1007/s00464-024-10874-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 04/20/2024] [Indexed: 05/19/2024]
Abstract
BACKGROUND Most intraoperative adverse events (iAEs) result from surgeons' errors, and bleeding is the majority of iAEs. Recognizing active bleeding timely is important to ensure safe surgery, and artificial intelligence (AI) has great potential for detecting active bleeding and providing real-time surgical support. This study aimed to develop a real-time AI model to detect active intraoperative bleeding. METHODS We extracted 27 surgical videos from a nationwide multi-institutional surgical video database in Japan and divided them at the patient level into three sets: training (n = 21), validation (n = 3), and testing (n = 3). We subsequently extracted the bleeding scenes and labeled distinctively active bleeding and blood pooling frame by frame. We used pre-trained YOLOv7_6w and developed a model to learn both active bleeding and blood pooling. The Average Precision at an Intersection over Union threshold of 0.5 (AP.50) for active bleeding and frames per second (FPS) were quantified. In addition, we conducted two 5-point Likert scales (5 = Excellent, 4 = Good, 3 = Fair, 2 = Poor, and 1 = Fail) questionnaires about sensitivity (the sensitivity score) and number of overdetection areas (the overdetection score) to investigate the surgeons' assessment. RESULTS We annotated 34,117 images of 254 bleeding events. The AP.50 for active bleeding in the developed model was 0.574 and the FPS was 48.5. Twenty surgeons answered two questionnaires, indicating a sensitivity score of 4.92 and an overdetection score of 4.62 for the model. CONCLUSIONS We developed an AI model to detect active bleeding, achieving real-time processing speed. Our AI model can be used to provide real-time surgical support.
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Affiliation(s)
- Kenta Horita
- Department of Surgery, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Koya Hida
- Department of Surgery, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan.
| | - Yoshiro Itatani
- Department of Surgery, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Haruku Fujita
- Department of Surgery, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Yu Hidaka
- Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Goshiro Yamamoto
- Division of Medical Information Technology and Administration Planning, Kyoto University, Kyoto, Japan
| | - Masaaki Ito
- Surgical Device Innovation Office, National Cancer Center Hospital East, Chiba, Japan
- Department of Colorectal Surgery, National Cancer Center Hospital East, Chiba, Japan
| | - Kazutaka Obama
- Department of Surgery, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
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11
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Wang DQ, Xu WH, Cheng XW, Hua L, Ge XS, Liu L, Gao X. Interpretable machine learning for predicting the response duration to Sintilimab plus chemotherapy in patients with advanced gastric or gastroesophageal junction cancer. Front Immunol 2024; 15:1407632. [PMID: 38840913 PMCID: PMC11150638 DOI: 10.3389/fimmu.2024.1407632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 05/08/2024] [Indexed: 06/07/2024] Open
Abstract
Background Sintilimab plus chemotherapy has proven effective as a combination immunotherapy for patients with advanced gastric and gastroesophageal junction adenocarcinoma (GC/GEJC). A multi-center study conducted in China revealed a median progression-free survival (PFS) of 7.1 months. However, the prediction of response duration to this immunotherapy has not been thoroughly investigated. Additionally, the potential of baseline laboratory features in predicting PFS remains largely unexplored. Therefore, we developed an interpretable machine learning (ML) framework, iPFS-SC, aimed at predicting PFS using baseline (pre-treatment) laboratory features and providing interpretations of the predictions. Materials and methods A cohort of 146 patients with advanced GC/GEJC, along with their baseline laboratory features, was included in the iPFS-SC framework. Through a forward feature selection process, predictive baseline features were identified, and four ML algorithms were developed to categorize PFS duration based on a threshold of 7.1 months. Furthermore, we employed explainable artificial intelligence (XAI) methodologies to elucidate the relationship between features and model predictions. Results The findings demonstrated that LightGBM achieved an accuracy of 0.70 in predicting PFS for advanced GC/GEJC patients. Furthermore, an F1-score of 0.77 was attained for identifying patients with PFS durations shorter than 7.1 months. Through the feature selection process, we identified 11 predictive features. Additionally, our framework facilitated the discovery of relationships between laboratory features and PFS. Conclusion A ML-based framework was developed to predict Sintilimab plus chemotherapy response duration with high accuracy. The suggested predictive features are easily accessible through routine laboratory tests. Furthermore, XAI techniques offer comprehensive explanations, both at the global and individual level, regarding PFS predictions. This framework enables patients to better understand their treatment plans, while clinicians can customize therapeutic approaches based on the explanations provided by the model.
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Affiliation(s)
- Dan-qi Wang
- Big Data Center, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Wen-huan Xu
- Department of Oncology, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Xiao-wei Cheng
- Department of Oncology, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Lei Hua
- Big Data Center, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Xiao-song Ge
- Department of Oncology, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Li Liu
- Big Data Center, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Xiang Gao
- Department of Oncology, Affiliated Hospital of Jiangnan University, Wuxi, China
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12
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Kim BS, Kim B, Cho M, Chung H, Ryu JK, Kim S. Enhanced multi-class pathology lesion detection in gastric neoplasms using deep learning-based approach and validation. Sci Rep 2024; 14:11527. [PMID: 38773274 PMCID: PMC11109266 DOI: 10.1038/s41598-024-62494-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 05/17/2024] [Indexed: 05/23/2024] Open
Abstract
This study developed a new convolutional neural network model to detect and classify gastric lesions as malignant, premalignant, and benign. We used 10,181 white-light endoscopy images from 2606 patients in an 8:1:1 ratio. Lesions were categorized as early gastric cancer (EGC), advanced gastric cancer (AGC), gastric dysplasia, benign gastric ulcer (BGU), benign polyp, and benign erosion. We assessed the lesion detection and classification model using six-class, cancer versus non-cancer, and neoplasm versus non-neoplasm categories, as well as T-stage estimation in cancer lesions (T1, T2-T4). The lesion detection rate was 95.22% (219/230 patients) on a per-patient basis: 100% for EGC, 97.22% for AGC, 96.49% for dysplasia, 75.00% for BGU, 97.22% for benign polyps, and 80.49% for benign erosion. The six-class category exhibited an accuracy of 73.43%, sensitivity of 80.90%, specificity of 83.32%, positive predictive value (PPV) of 73.68%, and negative predictive value (NPV) of 88.53%. The sensitivity and NPV were 78.62% and 88.57% for the cancer versus non-cancer category, and 83.26% and 89.80% for the neoplasm versus non-neoplasm category, respectively. The T stage estimation model achieved an accuracy of 85.17%, sensitivity of 88.68%, specificity of 79.81%, PPV of 87.04%, and NPV of 82.18%. The novel CNN-based model remarkably detected and classified malignant, premalignant, and benign gastric lesions and accurately estimated gastric cancer T-stages.
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Affiliation(s)
- Byeong Soo Kim
- Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, 08826, Korea
| | - Bokyung Kim
- Division of Gastroenterology, Department of Internal Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, 07061, Korea
| | - Minwoo Cho
- Transdisciplinary Department of Medicine, Seoul National University Hospital, Seoul, 03080, Korea
| | - Hyunsoo Chung
- Department of Internal Medicine and Liver Research Institute, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, 03080, Korea
| | - Ji Kon Ryu
- Department of Internal Medicine and Liver Research Institute, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, 03080, Korea.
| | - Sungwan Kim
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, 03080, Korea.
- Artificial Intelligence Institute, Seoul National University, Seoul, 08826, Korea.
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13
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Hayashi Y, Hatta W, Tsuji Y, Yoshio T, Yabuuchi Y, Hoteya S, Tsuji S, Nagami Y, Hikichi T, Kobayashi M, Morita Y, Sumiyoshi T, Iguchi M, Tomida H, Inoue T, Mikami T, Hasatani K, Nishikawa J, Matsumura T, Nebiki H, Nakamatsu D, Ohnita K, Suzuki H, Ueyama H, Sugimoto M, Yamaguchi S, Michida T, Yada T, Asahina Y, Narasaka T, Kuribayashi S, Kiyotoki S, Mabe K, Kurakami H, Fujishiro M, Masamune A, Takehara T. Endoscopic Features of Synchronous Multiple Early Gastric Cancers: Findings from a Nationwide Cohort. Digestion 2024:1-14. [PMID: 38697038 DOI: 10.1159/000538941] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 04/08/2024] [Indexed: 05/04/2024]
Abstract
INTRODUCTION We investigated the factors associated with synchronous multiple early gastric cancers and determined their localization. METHODS We analyzed 8,191 patients who underwent endoscopic submucosal dissection for early gastric cancers at 33 hospitals in Japan from November 2013 to October 2016. Background factors were compared between single-lesion (n = 7,221) and synchronous multi-lesion cases (n = 970) using univariate and multivariate analyses. We extracted cases with two synchronous lesions (n = 832) and evaluated their localization. RESULTS Significant independent risk factors for synchronous multiple early gastric cancer were older age (≥75 years old) (odds ratio [OR] = 1.257), male sex (OR = 1.385), severe mucosal atrophy (OR = 1.400), tumor localization in the middle (OR = 1.362) or lower region (OR = 1.404), and submucosal invasion (OR = 1.528 [SM1], 1.488 [SM2]). Depressed macroscopic type (OR = 0.679) and pure undifferentiated histology OR = 0.334) were more common in single early gastric cancers. When one lesion was in the upper region, the other was more frequently located in the lesser curvature of the middle region. When one lesion was in the middle region, the other was more frequently located in the middle region or the lesser curvature of the lower region. When one lesion was in the lower region, the other was more frequently located in the lesser curvature of the middle region or the lower region. CONCLUSION Factors associated with synchronous multiple early gastric cancer included older age, male sex, severe mucosal atrophy, tumor localization in the middle or lower region, and tumor submucosal invasion. Our findings provide useful information regarding specific areas that should be examined carefully when one lesion is detected.
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Affiliation(s)
- Yoshito Hayashi
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, Suita, Japan,
| | - Waku Hatta
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Yosuke Tsuji
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Toshiyuki Yoshio
- Division of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Yohei Yabuuchi
- Division of Endoscopy, Shizuoka Cancer Center, Nagaizumi, Japan
- Department of Gastroenterology, Kobe City Medical Center General Hospital, Kobe, Japan
| | - Shu Hoteya
- Department of Gastroenterology, Toranomon Hospital, Tokyo, Japan
| | - Shigetsugu Tsuji
- Department of Gastroenterology, Ishikawa Prefectural Central Hospital, Kanazawa, Japan
| | - Yasuaki Nagami
- Department of Gastroenterology, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
| | - Takuto Hikichi
- Department of Endoscopy, Fukushima Medical University Hospital, Fukushima, Japan
| | - Masakuni Kobayashi
- Department of Endoscopy, The Jikei University School of Medicine, Tokyo, Japan
| | - Yoshinori Morita
- Department of Gastroenterology, Kobe University International Clinical Cancer Research Center, Kobe, Japan
- Department of Gastroenterology, Kobe University Graduate School of Medicine, Kobe, Japan
| | | | - Mikitaka Iguchi
- Second Department of Internal Medicine, Wakayama Medical University, Wakayama, Japan
| | - Hideomi Tomida
- Gastroenterology Center, Ehime Prefectural Central Hospital, Matsuyama, Japan
- Department of Gastroenterology and Metabology, Ehime University Graduate School of Medicine, Toon, Japan
| | - Takuya Inoue
- Division of Gastroenterology and Hepatology, Osaka General Medical Center, Osaka, Japan
| | - Tatsuya Mikami
- Division of Endoscopy, Hirosaki University Hospital, Hirosaki, Japan
| | - Kenkei Hasatani
- Department of Gastroenterology, Fukui Prefectural Hospital, Fukui, Japan
| | - Jun Nishikawa
- Department of Gastroenterology and Hepatology, Yamaguchi University Graduate School of Medicine, Ube, Japan
| | - Tomoaki Matsumura
- Department of Gastroenterology, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Hiroko Nebiki
- Department of Gastroenterology, Osaka City General Hospital, Osaka, Japan
| | - Dai Nakamatsu
- Department of Gastroenterology, Toyonaka Municipal Hospital, Toyonaka, Japan
| | - Ken Ohnita
- Department of Gastroenterology and Hepatology, Nagasaki University Hospital, Nagasaki, Japan
| | - Haruhisa Suzuki
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Hiroya Ueyama
- Department of Gastroenterology, Juntendo University School of Medicine, Tokyo, Japan
| | - Mitsushige Sugimoto
- Division of Digestive Endoscopy, Shiga University of Medical Science Hospital, Kusatsu, Japan
- Department of Gastroenterological Endoscopy, Tokyo Medical University Hospital, Tokyo, Japan
| | | | - Tomoki Michida
- Department of Gastroenterology and Hepatology, Saitama Medical Center, Saitama, Japan
- Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Tomoyuki Yada
- Division of Gastroenterology and Hepatology, Kohnodai Hospital, National Center for Global Health and Medicine, Ichikawa, Japan
| | - Yoshiro Asahina
- Department of Gastroenterology, Kanazawa University Hospital, Kanazawa, Japan
| | - Toshiaki Narasaka
- Division of Endoscopic Center, University of Tsukuba Hospital, Tsukuba, Japan
| | - Shiko Kuribayashi
- Department of Gastroenterology and Hepatology, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Shu Kiyotoki
- Department of Gastroenterology, Shuto General Hospital, Yanai, Japan
| | - Katsuhiro Mabe
- Department of Gastroenterology, National Hospital Organization Hakodate National Hospital, Hakodate, Japan
- Junpukai Health Maintenance Center Kurashiki, Kurashiki, Japan
| | - Hiroyuki Kurakami
- Institute for Clinical Translational Science, Nara Medical University, Kashihara, Japan
| | - Mitsuhiro Fujishiro
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Atsushi Masamune
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Tetsuo Takehara
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, Suita, Japan
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14
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Guo F, Meng H. Application of artificial intelligence in gastrointestinal endoscopy. Arab J Gastroenterol 2024; 25:93-96. [PMID: 38228443 DOI: 10.1016/j.ajg.2023.12.010] [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: 02/24/2023] [Revised: 09/06/2023] [Accepted: 12/30/2023] [Indexed: 01/18/2024]
Abstract
Endoscopy is an important method for diagnosing gastrointestinal (GI) diseases. In this study, we provide an overview of the advances in artificial intelligence (AI) technology in the field of GI endoscopy over recent years, including esophagus, stomach, large intestine, and capsule endoscopy (small intestine). AI-assisted endoscopy shows high accuracy, sensitivity, and specificity in the detection and diagnosis of GI diseases at all levels. Hence, AI will make a breakthrough in the field of GI endoscopy in the near future. However, AI technology currently has some limitations and is still in the preclinical stages.
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Affiliation(s)
- Fujia Guo
- The first Affiliated Hospital, Dalian Medical University, Dalian 116044, China
| | - Hua Meng
- The first Affiliated Hospital, Dalian Medical University, Dalian 116044, China.
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15
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Luțenco V, Țocu G, Guliciuc M, Moraru M, Candussi IL, Dănilă M, Luțenco V, Dimofte F, Mihailov OM, Mihailov R. New Horizons of Artificial Intelligence in Medicine and Surgery. J Clin Med 2024; 13:2532. [PMID: 38731061 PMCID: PMC11084145 DOI: 10.3390/jcm13092532] [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: 03/06/2024] [Revised: 04/06/2024] [Accepted: 04/23/2024] [Indexed: 05/13/2024] Open
Abstract
Background: Ideas about Artificial intelligence appeared about half a century ago, but only now is it becoming an essential element of everyday life. The data provided are becoming a bigger pool and we need artificial intelligence that will help us with its superhuman powers. Its interaction with medicine is improving more and more, with medicine being a domain that continues to be perfected. Materials and Methods: The most important databases were used to perform this detailed search that addresses artificial intelligence in the medical and surgical fields. Discussion: Machine learning, deep learning, neural networks and computer vision are some of the mechanisms that are becoming a trend in healthcare worldwide. Developed countries such as Japan, France and Germany have already implemented artificial intelligence in their medical systems. The help it gives is in medical diagnosis, patient monitoring, personalized therapy and workflow optimization. Artificial intelligence will help surgeons to perfect their skills, to standardize techniques and to choose the best surgical techniques. Conclusions: The goal is to predict complications, reduce diagnostic times, diagnose complex pathologies, guide surgeons intraoperatively and reduce medical errors. We are at the beginning of this, and the potential is enormous, but we must not forget the impediments that may appear and slow down its implementation.
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Affiliation(s)
- Valerii Luțenco
- Surgery I Clinic, Emergency Hospital “Sf. Ap. Andrei”, 800578 Galați, Romania; (V.L.); (R.M.)
| | - George Țocu
- Faculty of Medicine and Pharmacy, “Dunărea de Jos” University of Galati, 800008 Galați, Romania; (M.G.); (M.M.); (I.L.C.); (M.D.); (F.D.)
| | - Mădălin Guliciuc
- Faculty of Medicine and Pharmacy, “Dunărea de Jos” University of Galati, 800008 Galați, Romania; (M.G.); (M.M.); (I.L.C.); (M.D.); (F.D.)
| | - Monica Moraru
- Faculty of Medicine and Pharmacy, “Dunărea de Jos” University of Galati, 800008 Galați, Romania; (M.G.); (M.M.); (I.L.C.); (M.D.); (F.D.)
| | - Iuliana Laura Candussi
- Faculty of Medicine and Pharmacy, “Dunărea de Jos” University of Galati, 800008 Galați, Romania; (M.G.); (M.M.); (I.L.C.); (M.D.); (F.D.)
- Clinical Children Emergency Hospital “Sf. Ioan”, 060011 Galați, Romania;
| | - Marius Dănilă
- Faculty of Medicine and Pharmacy, “Dunărea de Jos” University of Galati, 800008 Galați, Romania; (M.G.); (M.M.); (I.L.C.); (M.D.); (F.D.)
- Clinical Children Emergency Hospital “Sf. Ioan”, 060011 Galați, Romania;
| | - Verginia Luțenco
- Clinical Children Emergency Hospital “Sf. Ioan”, 060011 Galați, Romania;
| | - Florentin Dimofte
- Faculty of Medicine and Pharmacy, “Dunărea de Jos” University of Galati, 800008 Galați, Romania; (M.G.); (M.M.); (I.L.C.); (M.D.); (F.D.)
| | - Oana Mariana Mihailov
- Faculty of Medicine and Pharmacy, “Dunărea de Jos” University of Galati, 800008 Galați, Romania; (M.G.); (M.M.); (I.L.C.); (M.D.); (F.D.)
| | - Raul Mihailov
- Surgery I Clinic, Emergency Hospital “Sf. Ap. Andrei”, 800578 Galați, Romania; (V.L.); (R.M.)
- Faculty of Medicine and Pharmacy, “Dunărea de Jos” University of Galati, 800008 Galați, Romania; (M.G.); (M.M.); (I.L.C.); (M.D.); (F.D.)
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16
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Palomba G, Fernicola A, Corte MD, Capuano M, De Palma GD, Aprea G. Artificial intelligence in screening and diagnosis of surgical diseases: A narrative review. AIMS Public Health 2024; 11:557-576. [PMID: 39027395 PMCID: PMC11252578 DOI: 10.3934/publichealth.2024028] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 03/29/2024] [Accepted: 04/02/2024] [Indexed: 07/20/2024] Open
Abstract
Artificial intelligence (AI) is playing an increasing role in several fields of medicine. It is also gaining popularity among surgeons as a valuable screening and diagnostic tool for many conditions such as benign and malignant colorectal, gastric, thyroid, parathyroid, and breast disorders. In the literature, there is no review that groups together the various application domains of AI when it comes to the screening and diagnosis of main surgical diseases. The aim of this review is to describe the use of AI in these settings. We performed a literature review by searching PubMed, Web of Science, Scopus, and Embase for all studies investigating the role of AI in the surgical setting, published between January 01, 2000, and June 30, 2023. Our focus was on randomized controlled trials (RCTs), meta-analysis, systematic reviews, and observational studies, dealing with large cohorts of patients. We then gathered further relevant studies from the reference list of the selected publications. Based on the studies reviewed, it emerges that AI could strongly enhance the screening efficiency, clinical ability, and diagnostic accuracy for several surgical conditions. Some of the future advantages of this technology include implementing, speeding up, and improving the automaticity with which AI recognizes, differentiates, and classifies the various conditions.
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Affiliation(s)
- Giuseppe Palomba
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Agostino Fernicola
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Marcello Della Corte
- Azienda Ospedaliera Universitaria San Giovanni di Dio e Ruggi d'Aragona - OO. RR. Scuola Medica Salernitana, Salerno, Italy
| | - Marianna Capuano
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Giovanni Domenico De Palma
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Giovanni Aprea
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
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Zhang K, Wang H, Cheng Y, Liu H, Gong Q, Zeng Q, Zhang T, Wei G, Wei Z, Chen D. Early gastric cancer detection and lesion segmentation based on deep learning and gastroscopic images. Sci Rep 2024; 14:7847. [PMID: 38570595 PMCID: PMC10991264 DOI: 10.1038/s41598-024-58361-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 03/28/2024] [Indexed: 04/05/2024] Open
Abstract
Gastric cancer is a highly prevalent disease that poses a serious threat to public health. In clinical practice, gastroscopy is frequently used by medical practitioners to screen for gastric cancer. However, the symptoms of gastric cancer at different stages of advancement vary significantly, particularly in the case of early gastric cancer (EGC). The manifestations of EGC are often indistinct, leading to a detection rate of less than 10%. In recent years, researchers have focused on leveraging deep learning algorithms to assist medical professionals in detecting EGC and thereby improve detection rates. To enhance the ability of deep learning to detect EGC and segment lesions in gastroscopic images, an Improved Mask R-CNN (IMR-CNN) model was proposed. This model incorporates a "Bi-directional feature extraction and fusion module" and a "Purification module for feature channel and space" based on the Mask R-CNN (MR-CNN). Our study includes a dataset of 1120 images of EGC for training and validation of the models. The experimental results indicate that the IMR-CNN model outperforms the original MR-CNN model, with Precision, Recall, Accuracy, Specificity and F1-Score values of 92.9%, 95.3%, 93.9%, 92.5% and 94.1%, respectively. Therefore, our proposed IMR-CNN model has superior detection and lesion segmentation capabilities and can effectively aid doctors in diagnosing EGC from gastroscopic images.
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Affiliation(s)
- Kezhi Zhang
- Guangxi Key Laboratory of Information Functional Materials and Intelligent Information Processing, School of Physics and Electronics, Nanning Normal University, 175 Mingxiu East Road, Nanning, 530001, Guangxi, China
| | - Haibao Wang
- Guangxi Key Laboratory of Information Functional Materials and Intelligent Information Processing, School of Physics and Electronics, Nanning Normal University, 175 Mingxiu East Road, Nanning, 530001, Guangxi, China
| | - Yaru Cheng
- Department of Gastroenterology, Shandong Second Provincial General Hospital, 4 Duan Xing West Road, Jinan, 250022, Shandong, China
| | - Hongyan Liu
- Department of Gastroenterology, Shandong Second Provincial General Hospital, 4 Duan Xing West Road, Jinan, 250022, Shandong, China
| | - Qi Gong
- Department of Gastroenterology, Shandong Second Provincial General Hospital, 4 Duan Xing West Road, Jinan, 250022, Shandong, China
| | - Qian Zeng
- Guangxi Key Laboratory of Information Functional Materials and Intelligent Information Processing, School of Physics and Electronics, Nanning Normal University, 175 Mingxiu East Road, Nanning, 530001, Guangxi, China
| | - Tao Zhang
- Guangxi Key Laboratory of Information Functional Materials and Intelligent Information Processing, School of Physics and Electronics, Nanning Normal University, 175 Mingxiu East Road, Nanning, 530001, Guangxi, China
| | - Guoqiang Wei
- Guangxi Key Laboratory of Information Functional Materials and Intelligent Information Processing, School of Physics and Electronics, Nanning Normal University, 175 Mingxiu East Road, Nanning, 530001, Guangxi, China.
- School of Electronic Engineering, Hunan College of Information, Changsha, 410200, Hunan, China.
| | - Zhi Wei
- Department of Gastroenterology, Shandong Second Provincial General Hospital, 4 Duan Xing West Road, Jinan, 250022, Shandong, China.
| | - Dong Chen
- Guangxi Key Laboratory of Information Functional Materials and Intelligent Information Processing, School of Physics and Electronics, Nanning Normal University, 175 Mingxiu East Road, Nanning, 530001, Guangxi, China.
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Kuwahara T, Hara K, Mizuno N, Haba S, Okuno N, Fukui T, Urata M, Yamamoto Y. Current status of artificial intelligence analysis for the treatment of pancreaticobiliary diseases using endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography. DEN OPEN 2024; 4:e267. [PMID: 37397344 PMCID: PMC10312781 DOI: 10.1002/deo2.267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 06/18/2023] [Indexed: 07/04/2023]
Abstract
Pancreatic and biliary diseases encompass a range of conditions requiring accurate diagnosis for appropriate treatment strategies. This diagnosis relies heavily on imaging techniques like endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography. Artificial intelligence (AI), including machine learning and deep learning, is becoming integral in medical imaging and diagnostics, such as the detection of colorectal polyps. AI shows great potential in diagnosing pancreatobiliary diseases. Unlike machine learning, which requires feature extraction and selection, deep learning can utilize images directly as input. Accurate evaluation of AI performance is a complex task due to varied terminologies, evaluation methods, and development stages. Essential aspects of AI evaluation involve defining the AI's purpose, choosing appropriate gold standards, deciding on the validation phase, and selecting reliable validation methods. AI, particularly deep learning, is increasingly employed in endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography diagnostics, achieving high accuracy levels in detecting and classifying various pancreatobiliary diseases. The AI often performs better than doctors, even in tasks like differentiating benign from malignant pancreatic tumors, cysts, and subepithelial lesions, identifying gallbladder lesions, assessing endoscopic retrograde cholangiopancreatography difficulty, and evaluating the biliary strictures. The potential for AI in diagnosing pancreatobiliary diseases, especially where other modalities have limitations, is considerable. However, a crucial constraint is the need for extensive, high-quality annotated data for AI training. Future advances in AI, such as large language models, promise further applications in the medical field.
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Affiliation(s)
| | - Kazuo Hara
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Nobumasa Mizuno
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Shin Haba
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Nozomi Okuno
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Toshitaka Fukui
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Minako Urata
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
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19
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Yonazu S, Ozawa T, Nakanishi T, Ochiai K, Shibata J, Osawa H, Hirasawa T, Kato Y, Tajiri H, Tada T. Cost-effectiveness analysis of the artificial intelligence diagnosis support system for early gastric cancers. DEN OPEN 2024; 4:e289. [PMID: 37644958 PMCID: PMC10461711 DOI: 10.1002/deo2.289] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 07/24/2023] [Accepted: 08/11/2023] [Indexed: 08/31/2023]
Abstract
Objectives The introduction of artificial intelligence into the medical field has improved the diagnostic capabilities of physicians. However, few studies have analyzed the economic impact of employing artificial intelligence technologies in the clinical environment. This study evaluated the cost-effectiveness of a computer-assisted diagnosis (CADx) system designed to support clinicians in differentiating early gastric cancers from non-cancerous lesions in Japan, where the universal health insurance system was introduced. Methods The target population to be used for the CADx was estimated as those with moderate to severe gastritis caused by Helicobacter pylori infection. Decision trees with Markov models were built to analyze the cumulative cost-effectiveness of using CADx relative to the pre-artificial intelligence status quo, a condition reconstructed from data in published reports. After conducting a base-case analysis, we performed sensitivity analyses by modifying several parameters. The primary outcome was the incremental cost-effectiveness ratio. Results Compared with the status quo as represented in the base-case analysis, the incremental cost-effectiveness ratio of CADx in the Japanese market was forecasted to be 11,093 USD per quality-adjusted life year. The sensitivity analyses demonstrated that the expected incremental cost-effectiveness ratios were within the willingness-to-pay threshold of 50,000 USD per quality-adjusted life year when the cost of the CAD was less than 104 USD. Conclusions Using CADx for EGCs may decrease their misdiagnosis, contributing to improved cost-effectiveness in Japan.
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Affiliation(s)
- Shion Yonazu
- Faculty of MedicineThe University of TokyoTokyoJapan
- AI Medical Service Inc.TokyoJapan
| | - Tsuyoshi Ozawa
- AI Medical Service Inc.TokyoJapan
- Tada Tomohiro Institute of Gastroenterology and ProctologySaitamaJapan
| | | | - Kentaro Ochiai
- AI Medical Service Inc.TokyoJapan
- Department of Surgical Oncology, Graduate School of MedicineThe University of TokyoTokyoJapan
| | - Junichi Shibata
- AI Medical Service Inc.TokyoJapan
- Tada Tomohiro Institute of Gastroenterology and ProctologySaitamaJapan
| | - Hiroyuki Osawa
- Departments of Medicine and GastroenterologyDivision of Gastroenterology, Jichi Medical UniversityTochigiJapan
| | - Toshiaki Hirasawa
- Department of GastroenterologyCancer Institute Hospital of the Japanese Foundation for Cancer ResearchTokyoJapan
| | | | - Hisao Tajiri
- Department of Innovative Interventional Endoscopy ResearchThe Jikei University School of MedicineTokyoJapan
| | - Tomohiro Tada
- AI Medical Service Inc.TokyoJapan
- Tada Tomohiro Institute of Gastroenterology and ProctologySaitamaJapan
- Department of Surgical Oncology, Graduate School of MedicineThe University of TokyoTokyoJapan
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20
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Takeda T, Asaoka D, Ueyama H, Abe D, Suzuki M, Inami Y, Uemura Y, Yamamoto M, Iwano T, Uchida R, Utsunomiya H, Oki S, Suzuki N, Ikeda A, Akazawa Y, Matsumoto K, Ueda K, Hojo M, Nojiri S, Tada T, Nagahara A. Development of an Artificial Intelligence Diagnostic System Using Linked Color Imaging for Barrett's Esophagus. J Clin Med 2024; 13:1990. [PMID: 38610762 PMCID: PMC11012507 DOI: 10.3390/jcm13071990] [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: 03/01/2024] [Revised: 03/21/2024] [Accepted: 03/26/2024] [Indexed: 04/14/2024] Open
Abstract
Background: Barrett's esophagus and esophageal adenocarcinoma cases are increasing as gastroesophageal reflux disease increases. Using artificial intelligence (AI) and linked color imaging (LCI), our aim was to establish a method of diagnosis for short-segment Barrett's esophagus (SSBE). Methods: We retrospectively selected 624 consecutive patients in total at our hospital, treated between May 2017 and March 2020, who experienced an esophagogastroduodenoscopy with white light imaging (WLI) and LCI. Images were randomly chosen as data for learning from WLI: 542 (SSBE+/- 348/194) of 696 (SSBE+/- 444/252); and LCI: 643 (SSBE+/- 446/197) of 805 (SSBE+/- 543/262). Using a Vision Transformer (Vit-B/16-384) to diagnose SSBE, we established two AI systems for WLI and LCI. Finally, 126 WLI (SSBE+/- 77/49) and 137 LCI (SSBE+/- 81/56) images were used for verification purposes. The accuracy of six endoscopists in making diagnoses was compared to that of AI. Results: Study participants were 68.2 ± 12.3 years, M/F 330/294, SSBE+/- 409/215. The accuracy/sensitivity/specificity (%) of AI were 84.1/89.6/75.5 for WLI and 90.5/90.1/91.1/for LCI, and those of experts and trainees were 88.6/88.7/88.4, 85.7/87.0/83.7 for WLI and 93.4/92.6/94.6, 84.7/88.1/79.8 for LCI, respectively. Conclusions: Using AI to diagnose SSBE was similar in accuracy to using a specialist. Our finding may aid the diagnosis of SSBE in the clinic.
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Affiliation(s)
- Tsutomu Takeda
- Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
| | - Daisuke Asaoka
- Department of Gastroenterology, Juntendo Tokyo Koto Geriatric Medical Center, Tokyo 136-0075, Japan; (D.A.); (M.S.); (Y.I.)
| | - Hiroya Ueyama
- Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
| | - Daiki Abe
- Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
| | - Maiko Suzuki
- Department of Gastroenterology, Juntendo Tokyo Koto Geriatric Medical Center, Tokyo 136-0075, Japan; (D.A.); (M.S.); (Y.I.)
| | - Yoshihiro Inami
- Department of Gastroenterology, Juntendo Tokyo Koto Geriatric Medical Center, Tokyo 136-0075, Japan; (D.A.); (M.S.); (Y.I.)
| | - Yasuko Uemura
- Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
| | - Momoko Yamamoto
- Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
| | - Tomoyo Iwano
- Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
| | - Ryota Uchida
- Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
| | - Hisanori Utsunomiya
- Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
| | - Shotaro Oki
- Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
| | - Nobuyuki Suzuki
- Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
| | - Atsushi Ikeda
- Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
| | - Yoichi Akazawa
- Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
| | - Kohei Matsumoto
- Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
| | - Kumiko Ueda
- Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
| | - Mariko Hojo
- Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
| | - Shuko Nojiri
- Department of Medical Technology Innovation Center, Juntendo University School of Medicine, Tokyo 113-8421, Japan;
| | | | - Akihito Nagahara
- Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
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21
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Myslicka M, Kawala-Sterniuk A, Bryniarska A, Sudol A, Podpora M, Gasz R, Martinek R, Kahankova Vilimkova R, Vilimek D, Pelc M, Mikolajewski D. Review of the application of the most current sophisticated image processing methods for the skin cancer diagnostics purposes. Arch Dermatol Res 2024; 316:99. [PMID: 38446274 DOI: 10.1007/s00403-024-02828-1] [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: 12/28/2023] [Revised: 12/28/2023] [Accepted: 01/25/2024] [Indexed: 03/07/2024]
Abstract
This paper presents the most current and innovative solutions applying modern digital image processing methods for the purpose of skin cancer diagnostics. Skin cancer is one of the most common types of cancers. It is said that in the USA only, one in five people will develop skin cancer and this trend is constantly increasing. Implementation of new, non-invasive methods plays a crucial role in both identification and prevention of skin cancer occurrence. Early diagnosis and treatment are needed in order to decrease the number of deaths due to this disease. This paper also contains some information regarding the most common skin cancer types, mortality and epidemiological data for Poland, Europe, Canada and the USA. It also covers the most efficient and modern image recognition methods based on the artificial intelligence applied currently for diagnostics purposes. In this work, both professional, sophisticated as well as inexpensive solutions were presented. This paper is a review paper and covers the period of 2017 and 2022 when it comes to solutions and statistics. The authors decided to focus on the latest data, mostly due to the rapid technology development and increased number of new methods, which positively affects diagnosis and prognosis.
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Affiliation(s)
- Maria Myslicka
- Faculty of Medicine, Wroclaw Medical University, J. Mikulicza-Radeckiego 5, 50-345, Wroclaw, Poland.
| | - Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Proszkowska 76, 45-758, Opole, Poland.
| | - Anna Bryniarska
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Proszkowska 76, 45-758, Opole, Poland
| | - Adam Sudol
- Faculty of Natural Sciences and Technology, University of Opole, Dmowskiego 7-9, 45-368, Opole, Poland
| | - Michal Podpora
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Proszkowska 76, 45-758, Opole, Poland
| | - Rafal Gasz
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Proszkowska 76, 45-758, Opole, Poland
| | - Radek Martinek
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Proszkowska 76, 45-758, Opole, Poland
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University of Ostrava, 17. Listopadu 2172/15, Ostrava, 70800, Czech Republic
| | - Radana Kahankova Vilimkova
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Proszkowska 76, 45-758, Opole, Poland
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University of Ostrava, 17. Listopadu 2172/15, Ostrava, 70800, Czech Republic
| | - Dominik Vilimek
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University of Ostrava, 17. Listopadu 2172/15, Ostrava, 70800, Czech Republic
| | - Mariusz Pelc
- Institute of Computer Science, University of Opole, Oleska 48, 45-052, Opole, Poland
- School of Computing and Mathematical Sciences, University of Greenwich, Old Royal Naval College, Park Row, SE10 9LS, London, UK
| | - Dariusz Mikolajewski
- Institute of Computer Science, Kazimierz Wielki University in Bydgoszcz, ul. Kopernika 1, 85-074, Bydgoszcz, Poland
- Neuropsychological Research Unit, 2nd Clinic of the Psychiatry and Psychiatric Rehabilitation, Medical University in Lublin, Gluska 1, 20-439, Lublin, Poland
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22
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Chen TH, Wang YT, Wu CH, Kuo CF, Cheng HT, Huang SW, Lee C. A colonial serrated polyp classification model using white-light ordinary endoscopy images with an artificial intelligence model and TensorFlow chart. BMC Gastroenterol 2024; 24:99. [PMID: 38443794 PMCID: PMC10913269 DOI: 10.1186/s12876-024-03181-3] [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: 04/26/2023] [Accepted: 02/19/2024] [Indexed: 03/07/2024] Open
Abstract
In this study, we implemented a combination of data augmentation and artificial intelligence (AI) model-Convolutional Neural Network (CNN)-to help physicians classify colonic polyps into traditional adenoma (TA), sessile serrated adenoma (SSA), and hyperplastic polyp (HP). We collected ordinary endoscopy images under both white and NBI lights. Under white light, we collected 257 images of HP, 423 images of SSA, and 60 images of TA. Under NBI light, were collected 238 images of HP, 284 images of SSA, and 71 images of TA. We implemented the CNN-based artificial intelligence model, Inception V4, to build a classification model for the types of colon polyps. Our final AI classification model with data augmentation process is constructed only with white light images. Our classification prediction accuracy of colon polyp type is 94%, and the discriminability of the model (area under the curve) was 98%. Thus, we can conclude that our model can help physicians distinguish between TA, SSA, and HPs and correctly identify precancerous lesions such as TA and SSA.
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Affiliation(s)
- Tsung-Hsing Chen
- Department of Gastroenterology and Hepatology, Linkou Medical Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | | | - Chi-Huan Wu
- Department of Gastroenterology and Hepatology, Linkou Medical Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Chang-Fu Kuo
- Division of Rheumatology, Allergy, and Immunology, Chang Gung Memorial Hospital- Linkou and Chang Gung University College of Medicine, Taoyuan, Taiwan, ROC
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan, ROC
| | - Hao-Tsai Cheng
- Department of Gastroenterology and Hepatology, Linkou Medical Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, New Taipei Municipal TuCheng Hospital, New Taipei City, Taiwan
- Graduate Institute of Clinical Medicine, College of Medicine, Chang Gung University, Taoyuan City, Taiwan
| | - Shu-Wei Huang
- Department of Gastroenterology and Hepatology, Linkou Medical Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, New Taipei Municipal TuCheng Hospital, New Taipei City, Taiwan
| | - Chieh Lee
- Department of Information and Management, College of Business, National Sun Yat-sen University, Kaohsiung city, Taiwan.
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23
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Farinati F, Pelizzaro F. Gastric cancer screening in Western countries: A call to action. Dig Liver Dis 2024:S1590-8658(24)00269-X. [PMID: 38403513 DOI: 10.1016/j.dld.2024.02.008] [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: 01/25/2024] [Revised: 02/11/2024] [Accepted: 02/12/2024] [Indexed: 02/27/2024]
Abstract
Gastric cancer is a major cause of cancer-related death worldwide, despite the reduction in its incidence. The disease is still burdened with a poor prognosis, particularly in Western countries. The main risk factor is the infection by Helicobacter pylori, classified as a class I carcinogen by the IARC, and It is well-known that primary prevention of gastric cancer can be achieved with the eradication of the infection. Moreover, non-invasive measurement of pepsinogens (PGI and PGI/PGII ratio) allows the identification of patients that should undergo upper gastrointestinal (GI) endoscopy. Gastric non-cardia adenocarcinoma is indeed preceded by a well-defined precancerous process that involves consecutive stages, described for the first time by Correa et al. more than 40 years ago, and patients with advance stages of gastric atrophy/intestinal metaplasia and with dysplastic changes should be followed-up periodically with upper GI endoscopies. Despite these effective screening and surveillance methods, national-level screening campaigns have been adopted only in few countries in eastern Asia (Japan and South Korea). In this review, we describe primary and secondary preventive measures for gastric cancer, discussing the need to introduce screening also in Western countries. Moreover, we propose a simple algorithm for screening that could be easily applied in clinical practice.
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Affiliation(s)
- Fabio Farinati
- Department of Surgery, Oncology and Gastroenterology, University of Padova, Via Giustiniani 2, Padova 35128, Italy; Gastroenterology Unit, Azienda Ospedale-Università di Padova, Via Giustiniani 2, Padova 35128, Italy.
| | - Filippo Pelizzaro
- Department of Surgery, Oncology and Gastroenterology, University of Padova, Via Giustiniani 2, Padova 35128, Italy; Gastroenterology Unit, Azienda Ospedale-Università di Padova, Via Giustiniani 2, Padova 35128, Italy
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24
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Liu S, Peng S, Zhang M, Wang Z, Li L. Multimodal integration for Barrett's esophagus. iScience 2024; 27:108437. [PMID: 38292435 PMCID: PMC10827497 DOI: 10.1016/j.isci.2023.108437] [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: 06/21/2023] [Revised: 07/10/2023] [Accepted: 11/09/2023] [Indexed: 02/01/2024] Open
Abstract
The esophageal adenocarcinoma is facing a worldwide challenge: early prediction and risk assessment in clinical Barrett's esophagus (BE). In recent years, the growing interests have been witnessed in prediction and risk assessment in clinical BE. However, the resolution is limited, and the system is huge and expensive for the existing devices. Inspired by the principle of collaboration between human eye vision and brain cortex in data processing, here we propose multimodal learning framework to tackle tasks from various modalities, which can benefit from each other. To our findings, the experimental result indicates that low-level modality can directly affect high-level modality and form the final risk grading based on contribution, which maximizes the clinical performance of medical professionals based on our findings.
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Affiliation(s)
- Shubin Liu
- School of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China
| | - Shiyu Peng
- Department of Gastroenterology, First Affiliated Hospital of Shihezi University, Xinjiang 832061, China
| | - Mengxuan Zhang
- Faculty of Science, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Ziyuan Wang
- School of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China
| | - Lei Li
- School of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China
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25
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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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26
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Rammohan R, Joy MV, Magam SG, Natt D, Magam SR, Pannikodu L, Desai J, Akande O, Bunting S, Yost RM, Mustacchia P. Understanding the Landscape: The Emergence of Artificial Intelligence (AI), ChatGPT, and Google Bard in Gastroenterology. Cureus 2024; 16:e51848. [PMID: 38327910 PMCID: PMC10847895 DOI: 10.7759/cureus.51848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/07/2024] [Indexed: 02/09/2024] Open
Abstract
Introduction Artificial intelligence (AI) integration in healthcare, specifically in gastroenterology, has opened new avenues for enhanced patient care and medical decision-making. This study aims to assess the reliability and accuracy of two prominent AI tools, ChatGPT 4.0 and Google Bard, in answering gastroenterology-related queries, thereby evaluating their potential utility in medical settings. Methods The study employed a structured approach where typical gastroenterology questions were input into ChatGPT 4.0 and Google Bard. Independent reviewers evaluated responses using a Likert scale and cross-referenced them with guidelines from authoritative gastroenterology bodies. Statistical analysis, including the Mann-Whitney U test, was conducted to assess the significance of differences in ratings. Results ChatGPT 4.0 demonstrated higher reliability and accuracy in its responses than Google Bard, as indicated by higher mean ratings and statistically significant p-values in hypothesis testing. However, limitations in the data structure, such as the inability to conduct detailed correlation analysis, were noted. Conclusion The study concludes that ChatGPT 4.0 outperforms Google Bard in providing reliable and accurate responses to gastroenterology-related queries. This finding underscores the potential of AI tools like ChatGPT in enhancing healthcare delivery. However, the study also highlights the need for a broader and more diverse assessment of AI capabilities in healthcare to leverage their potential in clinical practice fully.
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Affiliation(s)
- Rajmohan Rammohan
- Gastroenterology, Nassau University Medical Center, East Meadow, USA
| | - Melvin V Joy
- Internal Medicine, Nassau University Medical Center, East Meadow, USA
| | | | - Dilman Natt
- Internal Medicine, Nassau University Medical Center, East Meadow, USA
| | - Sai Reshma Magam
- Internal Medicine, Nassau University Medical Center, East Meadow, USA
| | - Leeza Pannikodu
- Internal Medicine, Nassau University Medical Center, East Meadow, USA
| | - Jiten Desai
- Internal Medicine, Nassau University Medical Center, East Meadow, USA
| | - Olawale Akande
- Internal Medicine, Nassau University Medical Center, East Meadow, USA
| | - Susan Bunting
- Internal Medicine, Nassau University Medical Center, East Meadow, USA
| | - Robert M Yost
- Internal Medicine, Nassau University Medical Center, East Meadow, USA
| | - Paul Mustacchia
- Gastroenterology and Hepatology, Nassau University Medical Center, East Meadow, USA
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Horiuchi Y, Hirasawa T, Fujisaki J. Application of artificial intelligence for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging. Clin Endosc 2024; 57:11-17. [PMID: 38178327 PMCID: PMC10834286 DOI: 10.5946/ce.2023.173] [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: 07/08/2023] [Revised: 08/14/2023] [Accepted: 08/16/2023] [Indexed: 01/06/2024] Open
Abstract
Although magnifying endoscopy with narrow-band imaging is the standard diagnostic test for gastric cancer, diagnosing gastric cancer using this technology requires considerable skill. Artificial intelligence has superior image recognition, and its usefulness in endoscopic image diagnosis has been reported in many cases. The diagnostic performance (accuracy, sensitivity, and specificity) of artificial intelligence using magnifying endoscopy with narrow band still images and videos for gastric cancer was higher than that of expert endoscopists, suggesting the usefulness of artificial intelligence in diagnosing gastric cancer. Histological diagnosis of gastric cancer using artificial intelligence is also promising. However, previous studies on the use of artificial intelligence to diagnose gastric cancer were small-scale; thus, large-scale studies are necessary to examine whether a high diagnostic performance can be achieved. In addition, the diagnosis of gastric cancer using artificial intelligence has not yet become widespread in clinical practice, and further research is necessary. Therefore, in the future, artificial intelligence must be further developed as an instrument, and its diagnostic performance is expected to improve with the accumulation of numerous cases nationwide.
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Affiliation(s)
- Yusuke Horiuchi
- Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Toshiaki Hirasawa
- Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Junko Fujisaki
- Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
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Okamoto T, Hirasawa T. Quality indicators in endoscopic screening and the role of artificial intelligence. Dig Endosc 2024; 36:16-18. [PMID: 37872869 DOI: 10.1111/den.14701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 10/02/2023] [Indexed: 10/25/2023]
Affiliation(s)
- Takeshi Okamoto
- Department of Hepato-Biliary-Pancreatic Medicine, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Toshiaki Hirasawa
- Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
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Guldhammer CS, Vásquez JL, Kristensen VM, Norus T, Nadler N, Jensen JB, Azawi N. Cystoscopy Accuracy in Detecting Bladder Tumors: A Prospective Video-Confirmed Study. Cancers (Basel) 2023; 16:160. [PMID: 38201586 PMCID: PMC10777997 DOI: 10.3390/cancers16010160] [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: 11/09/2023] [Revised: 12/15/2023] [Accepted: 12/27/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Bladder cancer ranks as the 10th most common cancer globally. The diagnosis of bladder tumors typically involves cystoscopy. OBJECTIVE This study aimed to evaluate the sensitivity and specificity of cystoscopy in detecting bladder tumors within a surveillance program following a bladder cancer diagnosis. DESIGN, SETTING, AND PARTICIPANTS This study utilized recordings of cystoscopies conducted at the Department of Urology, Zealand University Hospital, between July 2021 and November 2022. Clinical observations were cross-referenced with pathological results or follow-up cystoscopies. Clinically negative cystoscopies were further scrutinized for potential overlooked tumors. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Sensitivity and specificity of cystoscopy were assessed through ROC curve analysis. RESULTS AND LIMITATIONS A total of 565 cystoscopies were recorded, with 135 indicating clinical positivity. Among 181 cystoscopies with clinically negative results that underwent a follow-up cystoscopy, 17 patients (9.4%) were subsequently diagnosed with bladder cancer, with the lesions identified in the initial cystoscopy. The sensitivity and specificity of cystoscopy in these cases were 81% and 73%, respectively. CONCLUSION This trial underscores the underdiagnosis and undertreatment of bladder tumors within the current surveillance program. Additionally, aggressive malignant lesions may be overlooked, heightening the risk of disease progression. Therefore, it is recommended that cystoscopies be complemented by other diagnostic methods to ensure accurate diagnosis and proper patient treatment. PATIENT SUMMARY This study involved 316 patients who underwent video-recorded cystoscopies and subsequent follow-up. Of these patients, 181 initially exhibited no clinical signs of bladder cancer. However, upon reviewing the recorded cystoscopy, bladder cancer was identified in 17 patients (9.4%).
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Affiliation(s)
- Cathrine Silberg Guldhammer
- Department of Urology, Zealand University Hospital, Sygehusvej 10, 4000 Roskilde, Denmark; (C.S.G.); (J.L.V.); (V.M.K.); (T.N.); (N.N.)
| | - Juan Luis Vásquez
- Department of Urology, Zealand University Hospital, Sygehusvej 10, 4000 Roskilde, Denmark; (C.S.G.); (J.L.V.); (V.M.K.); (T.N.); (N.N.)
- Department of Clinical Medicine, University of Copenhagen, Noerregade 10, 1165 Copenhagen, Denmark
| | - Vibeke Møllegaard Kristensen
- Department of Urology, Zealand University Hospital, Sygehusvej 10, 4000 Roskilde, Denmark; (C.S.G.); (J.L.V.); (V.M.K.); (T.N.); (N.N.)
| | - Thomas Norus
- Department of Urology, Zealand University Hospital, Sygehusvej 10, 4000 Roskilde, Denmark; (C.S.G.); (J.L.V.); (V.M.K.); (T.N.); (N.N.)
| | - Naomi Nadler
- Department of Urology, Zealand University Hospital, Sygehusvej 10, 4000 Roskilde, Denmark; (C.S.G.); (J.L.V.); (V.M.K.); (T.N.); (N.N.)
- Department of Clinical Medicine, University of Copenhagen, Noerregade 10, 1165 Copenhagen, Denmark
| | | | - Nessn Azawi
- Department of Urology, Zealand University Hospital, Sygehusvej 10, 4000 Roskilde, Denmark; (C.S.G.); (J.L.V.); (V.M.K.); (T.N.); (N.N.)
- Department of Clinical Medicine, University of Copenhagen, Noerregade 10, 1165 Copenhagen, Denmark
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Lee GP, Kim YJ, Park DK, Kim YJ, Han SK, Kim KG. Gastro-BaseNet: A Specialized Pre-Trained Model for Enhanced Gastroscopic Data Classification and Diagnosis of Gastric Cancer and Ulcer. Diagnostics (Basel) 2023; 14:75. [PMID: 38201385 PMCID: PMC10795822 DOI: 10.3390/diagnostics14010075] [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: 11/29/2023] [Revised: 12/25/2023] [Accepted: 12/26/2023] [Indexed: 01/12/2024] Open
Abstract
Most of the development of gastric disease prediction models has utilized pre-trained models from natural data, such as ImageNet, which lack knowledge of medical domains. This study proposes Gastro-BaseNet, a classification model trained using gastroscopic image data for abnormal gastric lesions. To prove performance, we compared transfer-learning based on two pre-trained models (Gastro-BaseNet and ImageNet) and two training methods (freeze and fine-tune modes). The effectiveness was verified in terms of classification at the image-level and patient-level, as well as the localization performance of lesions. The development of Gastro-BaseNet had demonstrated superior transfer learning performance compared to random weight settings in ImageNet. When developing a model for predicting the diagnosis of gastric cancer and gastric ulcers, the transfer-learned model based on Gastro-BaseNet outperformed that based on ImageNet. Furthermore, the model's performance was highest when fine-tuning the entire layer in the fine-tune mode. Additionally, the trained model was based on Gastro-BaseNet, which showed higher localization performance, which confirmed its accurate detection and classification of lesions in specific locations. This study represents a notable advancement in the development of image analysis models within the medical field, resulting in improved diagnostic predictive accuracy and aiding in making more informed clinical decisions in gastrointestinal endoscopy.
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Affiliation(s)
- Gi Pyo Lee
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon 21565, Republic of Korea;
| | - Young Jae Kim
- Department of Biomedical Engineering, Gachon University Gil Medical Center, College of Medicine, Gachon University, Incheon 21565, Republic of Korea;
| | - Dong Kyun Park
- Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, College of Medicine, Gachon University, Incheon 21565, Republic of Korea; (D.K.P.); (Y.J.K.)
| | - Yoon Jae Kim
- Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, College of Medicine, Gachon University, Incheon 21565, Republic of Korea; (D.K.P.); (Y.J.K.)
| | - Su Kyeong Han
- Health IT Research Center, Gachon University Gil Medical Center, Incheon 21565, Republic of Korea;
| | - Kwang Gi Kim
- Department of Biomedical Engineering, Gachon University Gil Medical Center, College of Medicine, Gachon University, Incheon 21565, Republic of Korea;
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Lam AB, Moore V, Nipp RD. Care Delivery Interventions for Individuals with Cancer: A Literature Review and Focus on Gastrointestinal Malignancies. Healthcare (Basel) 2023; 12:30. [PMID: 38200936 PMCID: PMC10779432 DOI: 10.3390/healthcare12010030] [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: 09/26/2023] [Revised: 12/05/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Gastrointestinal malignancies represent a particularly challenging condition, often requiring a multidisciplinary approach to management in order to meet the unique needs of these individuals and their caregivers. PURPOSE In this literature review, we sought to describe care delivery interventions that strive to improve the quality of life and care for patients with a focus on gastrointestinal malignancies. CONCLUSION We highlight patient-centered care delivery interventions, including patient-reported outcomes, hospital-at-home interventions, and other models of care for individuals with cancer. By demonstrating the relevance and utility of these different care models for patients with gastrointestinal malignancies, we hope to highlight the importance of developing and testing new interventions to address the unique needs of this population.
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Affiliation(s)
- Anh B. Lam
- Department of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Vanessa Moore
- College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73117, USA;
| | - Ryan D. Nipp
- Division of Hematology and Oncology, University of Oklahoma Health Sciences Center, Stephenson Cancer Center, Oklahoma City, OK 73104, USA
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Xin Y, Zhang Q, Liu X, Li B, Mao T, Li X. Application of artificial intelligence in endoscopic gastrointestinal tumors. Front Oncol 2023; 13:1239788. [PMID: 38144533 PMCID: PMC10747923 DOI: 10.3389/fonc.2023.1239788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 11/17/2023] [Indexed: 12/26/2023] Open
Abstract
With an increasing number of patients with gastrointestinal cancer, effective and accurate early diagnostic clinical tools are required provide better health care for patients with gastrointestinal cancer. Recent studies have shown that artificial intelligence (AI) plays an important role in the diagnosis and treatment of patients with gastrointestinal tumors, which not only improves the efficiency of early tumor screening, but also significantly improves the survival rate of patients after treatment. With the aid of efficient learning and judgment abilities of AI, endoscopists can improve the accuracy of diagnosis and treatment through endoscopy and avoid incorrect descriptions or judgments of gastrointestinal lesions. The present article provides an overview of the application status of various artificial intelligence in gastric and colorectal cancers in recent years, and the direction of future research and clinical practice is clarified from a clinical perspective to provide a comprehensive theoretical basis for AI as a promising diagnostic and therapeutic tool for gastrointestinal cancer.
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Affiliation(s)
| | | | | | | | | | - Xiaoyu Li
- Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China
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Klang E, Sourosh A, Nadkarni GN, Sharif K, Lahat A. Deep Learning and Gastric Cancer: Systematic Review of AI-Assisted Endoscopy. Diagnostics (Basel) 2023; 13:3613. [PMID: 38132197 PMCID: PMC10742887 DOI: 10.3390/diagnostics13243613] [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: 10/14/2023] [Revised: 11/23/2023] [Accepted: 12/02/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Gastric cancer (GC), a significant health burden worldwide, is typically diagnosed in the advanced stages due to its non-specific symptoms and complex morphological features. Deep learning (DL) has shown potential for improving and standardizing early GC detection. This systematic review aims to evaluate the current status of DL in pre-malignant, early-stage, and gastric neoplasia analysis. METHODS A comprehensive literature search was conducted in PubMed/MEDLINE for original studies implementing DL algorithms for gastric neoplasia detection using endoscopic images. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The focus was on studies providing quantitative diagnostic performance measures and those comparing AI performance with human endoscopists. RESULTS Our review encompasses 42 studies that utilize a variety of DL techniques. The findings demonstrate the utility of DL in GC classification, detection, tumor invasion depth assessment, cancer margin delineation, lesion segmentation, and detection of early-stage and pre-malignant lesions. Notably, DL models frequently matched or outperformed human endoscopists in diagnostic accuracy. However, heterogeneity in DL algorithms, imaging techniques, and study designs precluded a definitive conclusion about the best algorithmic approach. CONCLUSIONS The promise of artificial intelligence in improving and standardizing gastric neoplasia detection, diagnosis, and segmentation is significant. This review is limited by predominantly single-center studies and undisclosed datasets used in AI training, impacting generalizability and demographic representation. Further, retrospective algorithm training may not reflect actual clinical performance, and a lack of model details hinders replication efforts. More research is needed to substantiate these findings, including larger-scale multi-center studies, prospective clinical trials, and comprehensive technical reporting of DL algorithms and datasets, particularly regarding the heterogeneity in DL algorithms and study designs.
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Affiliation(s)
- Eyal Klang
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA (A.S.); (G.N.N.)
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- ARC Innovation Center, Sheba Medical Center, Affiliated with Tel Aviv University Medical School, Tel Hashomer, Ramat Gan 52621, Tel Aviv, Israel
| | - Ali Sourosh
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA (A.S.); (G.N.N.)
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Girish N. Nadkarni
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA (A.S.); (G.N.N.)
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kassem Sharif
- Department of Gastroenterology, Sheba Medical Center, Affiliated with Tel Aviv University Medical School, Tel Hashomer, Ramat Gan 52621, Tel Aviv, Israel;
| | - Adi Lahat
- Department of Gastroenterology, Sheba Medical Center, Affiliated with Tel Aviv University Medical School, Tel Hashomer, Ramat Gan 52621, Tel Aviv, Israel;
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Ishikawa Y, Sugino T, Okubo K, Nakajima Y. Detecting the location of lung cancer on thoracoscopic images using deep convolutional neural networks. Surg Today 2023; 53:1380-1387. [PMID: 37354240 DOI: 10.1007/s00595-023-02708-7] [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: 11/20/2022] [Accepted: 04/03/2023] [Indexed: 06/26/2023]
Abstract
OBJECTIVES The prevalence of minimally invasive surgeries has increased the need for tumor detection using thoracoscopic images during lung cancer surgery. We conducted this study to analyze the efficacy of a deep convolutional neural network (DCNN) for tumor detection using recorded thoracoscopic images of pulmonary surfaces. MATERIALS AND METHODS We collected 644 intraoperative thoracoscopic images of changes in pulmonary appearance from 427 patients with lung cancer between 2012 and 2021. The lesion areas on the thoracoscopic images were detected by bounding boxes using an advanced version of YOLO, a well-known DCNN for object detection. The DCNN model was trained and evaluated by a 15-fold cross-validation scheme. Each predicted bounding box was considered successful detection when it overlapped more than 50% of the lesion areas annotated by board-certified surgeons. RESULTS AND CONCLUSIONS Precision, recall, and F1-measured values of 91.9%, 90.5%, and 91.1%, respectively, were obtained. The presence of lymphatic vessel invasion was associated with successful detection (p = 0.045). The presence of pathological pleural invasion also showed a tendency toward successful detection (p = 0.081). The proposed DCNN-based algorithm yielded an accuracy of more than 90% tumor detection. These algorithms will help surgeons detect lung cancer displayed on a screen automatically.
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Affiliation(s)
- Yuya Ishikawa
- Department of Thoracic Surgery, Tokyo Medical and Dental University, Tokyo, Japan
| | - Takaaki Sugino
- Department of Biomedical Information, Institute of Biomaterials and Bioengineering, Tokyo Medical and Dental University, 2-3-10, Surugadai, Chiyoda-ku, Tokyo, 101-0062, Japan
| | - Kenichi Okubo
- Department of Thoracic Surgery, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yoshikazu Nakajima
- Department of Biomedical Information, Institute of Biomaterials and Bioengineering, Tokyo Medical and Dental University, 2-3-10, Surugadai, Chiyoda-ku, Tokyo, 101-0062, Japan.
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Dhali A, Kipkorir V, Srichawla BS, Kumar H, Rathna RB, Ongidi I, Chaudhry T, Morara G, Nurani K, Cheruto D, Biswas J, Chieng LR, Dhali GK. Artificial intelligence assisted endoscopic ultrasound for detection of pancreatic space-occupying lesion: a systematic review and meta-analysis. Int J Surg 2023; 109:4298-4308. [PMID: 37800594 PMCID: PMC10720860 DOI: 10.1097/js9.0000000000000717] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 08/21/2023] [Indexed: 10/07/2023]
Abstract
BACKGROUND Diagnosing pancreatic lesions, including chronic pancreatitis, autoimmune pancreatitis, and pancreatic cancer, poses a challenge and, as a result, is time-consuming. To tackle this issue, artificial intelligence (AI) has been increasingly utilized over the years. AI can analyze large data sets with heightened accuracy, reduce interobserver variability, and can standardize the interpretation of radiologic and histopathologic lesions. Therefore, this study aims to review the use of AI in the detection and differentiation of pancreatic space-occupying lesions and to compare AI-assisted endoscopic ultrasound (EUS) with conventional EUS in terms of their detection capabilities. METHODS Literature searches were conducted through PubMed/Medline, SCOPUS, and Embase to identify studies eligible for inclusion. Original articles, including observational studies, randomized control trials, systematic reviews, meta-analyses, and case series specifically focused on AI-assisted EUS in adults, were included. Data were extracted and pooled, and a meta-analysis was conducted using Meta-xl. For results exhibiting significant heterogeneity, a random-effects model was employed; otherwise, a fixed-effects model was utilized. RESULTS A total of 21 studies were included in the review with four studies pooled for a meta-analysis. A pooled accuracy of 93.6% (CI 90.4-96.8%) was found using the random-effects model on four studies that showed significant heterogeneity ( P <0.05) in the Cochrane's Q test. Further, a pooled sensitivity of 93.9% (CI 92.4-95.3%) was found using a fixed-effects model on seven studies that showed no significant heterogeneity in the Cochrane's Q test. When it came to pooled specificity, a fixed-effects model was utilized in six studies that showed no significant heterogeneity in the Cochrane's Q test and determined as 93.1% (CI 90.7-95.4%). The pooled positive predictive value which was done using the random-effects model on six studies that showed significant heterogeneity was 91.6% (CI 87.3-95.8%). The pooled negative predictive value which was done using the random-effects model on six studies that showed significant heterogeneity was 93.6% (CI 90.4-96.8%). CONCLUSION AI-assisted EUS shows a high degree of accuracy in the detection and differentiation of pancreatic space-occupying lesions over conventional EUS. Its application may promote prompt and accurate diagnosis of pancreatic pathologies.
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Affiliation(s)
- Arkadeep Dhali
- NIHR Academic Clinical Fellow in Gastroenterology, University of Sheffield; Internal Medicine Trainee, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Vincent Kipkorir
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | | | | | | | - Ibsen Ongidi
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | - Talha Chaudhry
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | - Gisore Morara
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | - Khulud Nurani
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | - Doreen Cheruto
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | | | - Leonard R. Chieng
- NIHR Academic Clinical Fellow in Gastroenterology, University of Sheffield; Internal Medicine Trainee, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Gopal Krishna Dhali
- School of Digestive and Liver Diseases, Institute of Postgraduate Medical Education and Research, Kolkata, India
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Yuan G, Lv B, Hao C. Application of artificial neural networks in reproductive medicine. HUM FERTIL 2023; 26:1195-1201. [PMID: 36628627 DOI: 10.1080/14647273.2022.2156301] [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: 12/14/2021] [Accepted: 09/01/2022] [Indexed: 01/12/2023]
Abstract
With the emergence of the age of information, the data on reproductive medicine has improved immensely. Nonetheless, healthcare workers who wish to utilise the relevance and implied value of the various data available to aid clinical decision-making encounter the difficulty of statistically analysing such large data. The application of artificial intelligence becoming widespread in recent years has emerged as a turning point in this regard. Artificial neural networks (ANNs) exhibit beneficial characteristics of comprehensive analysis and autonomous learning, owing to which these are being applied to disease diagnosis, embryo quality assessment, and prediction of pregnancy outcomes. The present report aims to summarise the application of ANNs in the field of reproduction and analyse its further application potential.
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Affiliation(s)
- Guanghui Yuan
- Department of Qingdao Medical College, Qingdao University, Qingdao, Shandong, China
| | - Bohan Lv
- Department of Intensive Care Unit, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Cuifang Hao
- Department of Reproductive Medicine, The Affiliated Women and Children's Hospital of Qingdao University, Qingdao, Shandong, China
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Zhang JQ, Mi JJ, Wang R. Application of convolutional neural network-based endoscopic imaging in esophageal cancer or high-grade dysplasia: A systematic review and meta-analysis. World J Gastrointest Oncol 2023; 15:1998-2016. [DOI: 10.4251/wjgo.v15.i11.1998] [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: 08/01/2023] [Revised: 09/05/2023] [Accepted: 10/11/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Esophageal cancer is the seventh-most common cancer type worldwide, accounting for 5% of death from malignancy. Development of novel diagnostic techniques has facilitated screening, early detection, and improved prognosis. Convolutional neural network (CNN)-based image analysis promises great potential for diagnosing and determining the prognosis of esophageal cancer, enabling even early detection of dysplasia.
AIM To conduct a meta-analysis of the diagnostic accuracy of CNN models for the diagnosis of esophageal cancer and high-grade dysplasia (HGD).
METHODS PubMed, EMBASE, Web of Science and Cochrane Library databases were searched for articles published up to November 30, 2022. We evaluated the diagnostic accuracy of using the CNN model with still image-based analysis and with video-based analysis for esophageal cancer or HGD, as well as for the invasion depth of esophageal cancer. The pooled sensitivity, pooled specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and area under the curve (AUC) were estimated, together with the 95% confidence intervals (CI). A bivariate method and hierarchical summary receiver operating characteristic method were used to calculate the diagnostic test accuracy of the CNN model. Meta-regression and subgroup analyses were used to identify sources of heterogeneity.
RESULTS A total of 28 studies were included in this systematic review and meta-analysis. Using still image-based analysis for the diagnosis of esophageal cancer or HGD provided a pooled sensitivity of 0.95 (95%CI: 0.92-0.97), pooled specificity of 0.92 (0.89-0.94), PLR of 11.5 (8.3-16.0), NLR of 0.06 (0.04-0.09), DOR of 205 (115-365), and AUC of 0.98 (0.96-0.99). When video-based analysis was used, a pooled sensitivity of 0.85 (0.77-0.91), pooled specificity of 0.73 (0.59-0.83), PLR of 3.1 (1.9-5.0), NLR of 0.20 (0.12-0.34), DOR of 15 (6-38) and AUC of 0.87 (0.84-0.90) were found. Prediction of invasion depth resulted in a pooled sensitivity of 0.90 (0.87-0.92), pooled specificity of 0.83 (95%CI: 0.76-0.88), PLR of 7.8 (1.9-32.0), NLR of 0.10 (0.41-0.25), DOR of 118 (11-1305), and AUC of 0.95 (0.92-0.96).
CONCLUSION CNN-based image analysis in diagnosing esophageal cancer and HGD is an excellent diagnostic method with high sensitivity and specificity that merits further investigation in large, multicenter clinical trials.
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Affiliation(s)
- Jun-Qi Zhang
- The Fifth Clinical Medical College, Shanxi Medical University, Taiyuan 030001, Shanxi Province, China
| | - Jun-Jie Mi
- Department of Gastroenterology, Shanxi Provincial People’s Hospital, Taiyuan 030012, Shanxi Province, China
| | - Rong Wang
- Department of Gastroenterology, The Fifth Hospital of Shanxi Medical University (Shanxi Provincial People’s Hospital), Taiyuan 030012, Shanxi Province, China
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Zhao Q, Jia Q, Chi T. U-Net deep learning model for endoscopic diagnosis of chronic atrophic gastritis and operative link for gastritis assessment staging: a prospective nested case-control study. Therap Adv Gastroenterol 2023; 16:17562848231208669. [PMID: 37928896 PMCID: PMC10624012 DOI: 10.1177/17562848231208669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 10/02/2023] [Indexed: 11/07/2023] Open
Abstract
Background The operative link for the gastritis assessment (OLGA) system can objectively reflect the stratification of gastric cancer risk in patients with chronic atrophic gastritis (CAG). Objectives We developed a real-time video monitoring model for the endoscopic diagnosis of CAG and OLGA staging based on U-Net deep learning (DL). To further validate and improve its performance, we designed a study to evaluate the diagnostic evaluation indices. Design A prospective nested case-control study. Methods Our cohort consisted of 1306 patients from 31 July 2021 to 31 January 2022. According to the pathological results, patients in the cohort were divided into the CAG group and the chronic non-atrophic gastritis group to evaluate the diagnostic evaluation indices. Each atrophy lesion was automatically labeled and the atrophy severity was assessed by the model. Propensity score matching was used to minimize selection bias. Results The diagnostic evaluation indices and the consistency between OLGA staging and pathological diagnosis of the model were superior to those of endoscopists [sensitivity (89.31% versus 67.56%), specificity (90.46% versus 70.23%), positive predictive value (90.35% versus 69.41%), negative predictive value (89.43% versus 68.40%), accuracy rate (89.89% versus 68.89%), Youden index (79.77% versus 37.79%), odd product (79.23 versus 4.91), positive likelihood ratio (9.36 versus 2.27), negative likelihood ratio (0.12 versus 0.46)], areas under the curves (AUC) (95% CI) (0.919 (0.893-0.945) versus 0.749 (0.707-0.792), p < 0.001) and kappa (0.816 versus 0.291)]. Conclusion Our study demonstrated that the DL model can assist endoscopists in real-time diagnosis of CAG during gastroscopy and synchronous identification of high-risk OLGA stage (OLGA stages III and IV) patients. Trial registration ChiCTR2100044458.
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Affiliation(s)
- Quchuan Zhao
- Department of Gastroenterology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Qing Jia
- Department of Anesthesiology, Guang’anmen Hospital China Academy of Chinese Medical Sciences, 5 North Court Street, Beijing 100053, China
| | - Tianyu Chi
- Department of Gastroenterology, Xuanwu Hospital of Capital Medical University, 45 Chang-Chun Street, Beijing 100053, China
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Cho Y, Park JM, Youn S. General Overview of Artificial Intelligence for Interstitial Cystitis in Urology. Int Neurourol J 2023; 27:S64-72. [PMID: 38048820 DOI: 10.5213/inj.2346294.147] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 11/13/2023] [Indexed: 12/06/2023] Open
Abstract
Our understanding of interstitial cystitis/bladder pain syndrome (IC/BPS) has evolved over time. The diagnosis of IC/BPS is primarily based on symptoms such as urgency, frequency, and bladder or pelvic pain. While the exact causes of IC/BPS remain unclear, it is thought to involve several factors, including abnormalities in the bladder's urothelium, mast cell degranulation within the bladder, inflammation of the bladder, and altered innervation of the bladder. Treatment options include patient education, dietary and lifestyle modifications, medications, intravesical therapy, and surgical interventions. This review article provides insights into IC/BPS, including aspects of treatment, prognosis prediction, and emerging therapeutic options. Additionally, it explores the application of deep learning for diagnosing major diseases associated with IC/BPS.
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Affiliation(s)
- Yongwon Cho
- Department of AI Center, Korea University Anam Hospital, Seoul, Korea
| | - Jong Mok Park
- Department of Urology, Chungnam National University Sejong Hospital, Chungnam National University College of Medicine, Sejong, Korea
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Shimada S, Yabuuchi Y, Kawata N, Maeda Y, Yoshida M, Yamamoto Y, Minamide T, Shigeta K, Takada K, Kishida Y, Ito S, Imai K, Hotta K, Ishiwatari H, Matsubayashi H, Ono H. Endoscopic causes and characteristics of missed gastric cancers after endoscopic submucosal dissection. Gastrointest Endosc 2023; 98:735-743.e2. [PMID: 36849058 DOI: 10.1016/j.gie.2023.02.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 02/10/2023] [Accepted: 02/20/2023] [Indexed: 03/01/2023]
Abstract
BACKGROUND AND AIMS Because endoscopic submucosal dissection (ESD) for early gastric cancer (EGC) preserves the entire stomach, missed gastric cancers (MGCs) are often found in the remaining gastric mucosa. However, the endoscopic causes of MGCs remain unclear. Therefore, we aimed to elucidate the endoscopic causes and characteristics of MGCs after ESD. METHODS From January 2009 to December 2018, all patients undergoing ESD for initially detected EGC were enrolled. According to a review of EGD images before ESD, we identified the endoscopic causes (perceptual, exposure, sampling errors, and inadequate preparation) and characteristics of MGC in each endoscopic cause. RESULTS Of 2208 patients who underwent ESD for initial EGC, 82 patients (3.7%) had 100 MGCs. The breakdown of endoscopic causes of MGCs was as follows: 69 (69%) perceptual errors, 23 (23%) exposure errors, 7 (7%) sampling errors, and 1 (1%) inadequate preparation. Logistic regression analysis showed that the risk factors for perceptual error were male sex (odds ratio [OR], 2.45; 95% confidence interval [CI], 1.16-5.18), isochromatic coloration (OR, 3.17; 95% CI, 1.47-6.84), greater curvature (OR, 2.31; 95% CI, 1.121-4.40), and lesion size ≤12 mm (OR, 1.74; 95% CI, 1.07-2.84). The sites of exposure errors were around the incisura angularis (11 [48%]), posterior wall of the gastric body (6 [26%]), and antrum (5 [21%]). CONCLUSIONS We identified MGCs in 4 categories and clarified their characteristics. Quality improvements in EGD observation, with attention to the risks of perceptual and site of exposure errors, can potentially prevent missing EGCs.
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Affiliation(s)
- Seitaro Shimada
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan; Third Department of Internal Medicine, University of Toyama, Toyama, Japan
| | - Yohei Yabuuchi
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan; Department of Gastroenterology, Kobe City Medical Center General Hospital, Kobe, Japan
| | - Noboru Kawata
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Yuki Maeda
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Masao Yoshida
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Yoichi Yamamoto
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | | | - Kohei Shigeta
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Kazunori Takada
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | | | - Sayo Ito
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Kenichiro Imai
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Kinichi Hotta
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | | | | | - Hiroyuki Ono
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
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Fu L, Li M, Lv J, Yang C, Zhang Z, Qin S, Li W, Wang X, Chen L. Deep neural network for discovering metabolism-related biomarkers for lung adenocarcinoma. Front Endocrinol (Lausanne) 2023; 14:1270772. [PMID: 37955007 PMCID: PMC10634586 DOI: 10.3389/fendo.2023.1270772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 10/03/2023] [Indexed: 11/14/2023] Open
Abstract
Introduction Lung cancer is a major cause of illness and death worldwide. Lung adenocarcinoma (LUAD) is its most common subtype. Metabolite-mRNA interactions play a crucial role in cancer metabolism. Thus, metabolism-related mRNAs are potential targets for cancer therapy. Methods This study constructed a network of metabolite-mRNA interactions (MMIs) using four databases. We retrieved mRNAs from the Tumor Genome Atlas (TCGA)-LUAD cohort showing significant expressional changes between tumor and non-tumor tissues and identified metabolism-related differential expression (DE) mRNAs among the MMIs. Candidate mRNAs showing significant contributions to the deep neural network (DNN) model were mined. Using MMIs and the results of function analysis, we created a subnetwork comprising candidate mRNAs and metabolites. Results Finally, 10 biomarkers were obtained after survival analysis and validation. Their good prognostic value in LUAD was validated in independent datasets. Their effectiveness was confirmed in the TCGA and an independent Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset by comparison with traditional machine-learning models. Conclusion To summarize, 10 metabolism-related biomarkers were identified, and their prognostic value was confirmed successfully through the MMI network and the DNN model. Our strategy bears implications to pave the way for investigating metabolic biomarkers in other cancers.
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Affiliation(s)
- Lei Fu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Manshi Li
- Department of Radiation Oncology, The Fourth Affiliated Hospital of China Medical University, Shenyang, China
| | - Junjie Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chengcheng Yang
- Department of Respiratory, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Zihan Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shimei Qin
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Wan Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xinyan Wang
- Department of Respiratory, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Lina Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
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Shen Y, Chen A, Zhang X, Zhong X, Ma A, Wang J, Wang X, Zheng W, Sun Y, Yue L, Zhang Z, Zhang X, Lin N, Kim JJ, Du Q, Liu J, Hu W. Real-Time Evaluation of Helicobacter pylori Infection by Convolution Neural Network During White-Light Endoscopy: A Prospective, Multicenter Study (With Video). Clin Transl Gastroenterol 2023; 14:e00643. [PMID: 37800683 PMCID: PMC10589579 DOI: 10.14309/ctg.0000000000000643] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 09/19/2023] [Indexed: 10/07/2023] Open
Abstract
INTRODUCTION Convolutional neural network during endoscopy may facilitate evaluation of Helicobacter pylori infection without obtaining gastric biopsies. The aim of the study was to evaluate the diagnosis accuracy of a computer-aided decision support system for H. pylori infection (CADSS-HP) based on convolutional neural network under white-light endoscopy. METHODS Archived video recordings of upper endoscopy with white-light examinations performed at Sir Run Run Shaw Hospital (January 2019-September 2020) were used to develop CADSS-HP. Patients receiving endoscopy were prospectively enrolled (August 2021-August 2022) from 3 centers to calculate the diagnostic property. Accuracy of CADSS-HP for H. pylori infection was also compared with endoscopic impression, urea breath test (URT), and histopathology. H. pylori infection was defined by positive test on histopathology and/or URT. RESULTS Video recordings of 599 patients who received endoscopy were used to develop CADSS-HP. Subsequently, 456 patients participated in the prospective evaluation including 189 (41.4%) with H. pylori infection. With a threshold of 0.5, CADSS-HP achieved an area under the curve of 0.95 (95% confidence interval [CI], 0.93-0.97) with sensitivity and specificity of 91.5% (95% CI 86.4%-94.9%) and 88.8% (95% CI 84.2%-92.2%), respectively. CADSS-HP demonstrated higher sensitivity (91.5% vs 78.3%; mean difference = 13.2%, 95% CI 5.7%-20.7%) and accuracy (89.9% vs 83.8%, mean difference = 6.1%, 95% CI 1.6%-10.7%) compared with endoscopic diagnosis by endoscopists. Sensitivity of CADSS-HP in diagnosing H. pylori was comparable with URT (91.5% vs 95.2%; mean difference = 3.7%, 95% CI -1.8% to 9.4%), better than histopathology (91.5% vs 82.0%; mean difference = 9.5%, 95% CI 2.3%-16.8%). DISCUSSION CADSS-HP achieved high sensitivity in the diagnosis of H. pylori infection in the real-time test, outperforming endoscopic diagnosis by endoscopists and comparable with URT. Clinicaltrials.gov ; ChiCTR2000030724.
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Affiliation(s)
- Yuqin Shen
- Department of Gastroenterology, Sir Run Run Shaw Hospital, Medical School, Zhejiang University, Hangzhou, China
- West China Xiamen Hospital, Sichuan University, Xiamen, China
| | - Angli Chen
- Shaoxing University School of Medicine, Shaoxing, Zhejiang, China
| | - Xinsen Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Xingwei Zhong
- Department of Gastroenterology, Deqing County People's Hospital, Huzhou, China
| | - Ahuo Ma
- Department of Gastroenterology, Shaoxing People's Hospital, Shaoxing, China
| | - Jianping Wang
- Department of Gastroenterology, Deqing County People's Hospital, Huzhou, China
| | - Xinjie Wang
- Department of Gastroenterology, Sir Run Run Shaw Hospital, Medical School, Zhejiang University, Hangzhou, China
| | - Wenfang Zheng
- Department of Gastroenterology, Hangzhou First People's Hospital, Hangzhou, China
| | - Yingchao Sun
- Department of Gastroenterology, Sir Run Run Shaw Hospital, Medical School, Zhejiang University, Hangzhou, China
| | - Lei Yue
- Department of Gastroenterology, Sir Run Run Shaw Hospital, Medical School, Zhejiang University, Hangzhou, China
| | - Zhe Zhang
- Department of Gastroenterology, Longyou County People's Hospital, Quzhou, China
| | - Xiaoyan Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Ne Lin
- Department of Gastroenterology, Sir Run Run Shaw Hospital, Medical School, Zhejiang University, Hangzhou, China
| | - John J. Kim
- Division of Gastroenterology and Hepatology, Loma Linda University Health, Loma Linda, California, USA
| | - Qin Du
- Department of Gastroenterology, The Second Affiliated Hospital, Medical School, Zhejiang University, Hangzhou, China
| | - Jiquan Liu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Weiling Hu
- Department of Gastroenterology, Sir Run Run Shaw Hospital, Medical School, Zhejiang University, Hangzhou, China
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Dong Z, Tao X, Du H, Wang J, Huang L, He C, Zhao Z, Mao X, Ai Y, Zhang B, Liu M, Xu H, Jiang Z, Sun Y, Li X, Liu Z, Chen J, Song Y, Liu G, Luo C, Li Y, Zeng X, Liu J, Zhu Y, Wu L, Yu H. Exploring the challenge of early gastric cancer diagnostic AI system face in multiple centers and its potential solutions. J Gastroenterol 2023; 58:978-989. [PMID: 37515597 DOI: 10.1007/s00535-023-02025-3] [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: 05/02/2023] [Accepted: 07/10/2023] [Indexed: 07/31/2023]
Abstract
BACKGROUND Artificial intelligence (AI) performed variously among test sets with different diversity due to sample selection bias, which can be stumbling block for AI applications. We previously tested AI named ENDOANGEL, diagnosing early gastric cancer (EGC) on single-center videos in man-machine competition. We aimed to re-test ENDOANGEL on multi-center videos to explore challenges applying AI in multiple centers, then upgrade ENDOANGEL and explore solutions to the challenge. METHODS ENDOANGEL was re-tested on multi-center videos retrospectively collected from 12 institutions and compared with performance in previously reported single-center videos. We then upgraded ENDOANGEL to ENDOANGEL-2022 with more training samples and novel algorithms and conducted competition between ENDOANGEL-2022 and endoscopists. ENDOANGEL-2022 was then tested on single-center videos and compared with performance in multi-center videos; the two AI systems were also compared with each other and endoscopists. RESULTS Forty-six EGCs and 54 non-cancers were included in multi-center video cohort. On diagnosing EGCs, compared with single-center videos, ENDOANGEL showed stable sensitivity (97.83% vs. 100.00%) while sharply decreased specificity (61.11% vs. 82.54%); ENDOANGEL-2022 showed similar tendency while achieving significantly higher specificity (79.63%, p < 0.01) making fewer mistakes on typical lesions than ENDOANGEL. On detecting gastric neoplasms, both AI showed stable sensitivity while sharply decreased specificity. Nevertheless, both AI outperformed endoscopists in the two competitions. CONCLUSIONS Great increase of false positives is a prominent challenge for applying EGC diagnostic AI in multiple centers due to high heterogeneity of negative cases. Optimizing AI by adding samples and using novel algorithms is promising to overcome this challenge.
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Affiliation(s)
- Zehua Dong
- Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xiao Tao
- Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Hongliu Du
- Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Junxiao Wang
- Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Li Huang
- Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Chiyi He
- Department of Gastroenterology, Yijishan Hospital of Wannan Medical College, Wuhu, 241001, Anhui, People's Republic of China
| | - Zhifeng Zhao
- Department of Digestive Endoscopy, The Fourth Hospital of China Medical University, Shenyang, 110032, Liaoning Province, People's Republic of China
| | - Xinli Mao
- Department of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, Zhejiang, China
| | - Yaowei Ai
- Department of Gastroenterology, The People's Hospital of China Three Gorges University, The First People's Hospital of Yichang, Yichang, China
| | - Beiping Zhang
- Department of Gastroenterology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Mei Liu
- Department of Gastroenterology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hong Xu
- Department of Endoscopy, The First Hospital of Jilin University, Changchun, China
| | - Zhenyu Jiang
- Department of Gastroenterology, The Second Affiliated Hospital of Baotou Medical College, Baotou, Inner Mongolia, China
| | - Yunwei Sun
- Department of Gastroenterology, Ruijin Hospital, Shanghai Jiaotong University, Gubei Branch, Shanghai, People's Republic of China
| | - Xiuling Li
- Department of Gastroenterology, School of Clinical Medicine, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Henan University, Zhengzhou, Henan, China
| | - Zhihong Liu
- Department of Gastroenterology, Jilin City People's Hospital, Jilin, China
| | - Jinzhong Chen
- Endoscopy Center, School of Medicine, The First Affiliated Hospital of Xiamen University, Xiamen University, Xiamen, China
| | - Ying Song
- Department of Gastroenterology, Xi'an Gaoxin Hospital, Xi'an, 710032, Shaanxi Province, China
| | - Guowei Liu
- Yi Xin Clinic, Changzhou, Jiangsu, China
| | - Chaijie Luo
- Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yanxia Li
- Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xiaoquan Zeng
- Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jun Liu
- Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yijie Zhu
- Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Lianlian Wu
- Renmin Hospital of Wuhan University, Wuhan, China.
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China.
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China.
| | - Honggang Yu
- Renmin Hospital of Wuhan University, Wuhan, China.
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China.
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China.
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Albahli S, Nazir T. A Circular Box-Based Deep Learning Model for the Identification of Signet Ring Cells from Histopathological Images. Bioengineering (Basel) 2023; 10:1147. [PMID: 37892876 PMCID: PMC10604551 DOI: 10.3390/bioengineering10101147] [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: 08/15/2023] [Revised: 09/16/2023] [Accepted: 09/18/2023] [Indexed: 10/29/2023] Open
Abstract
Signet ring cell (SRC) carcinoma is a particularly serious type of cancer that is a leading cause of death all over the world. SRC carcinoma has a more deceptive onset than other carcinomas and is mostly encountered in its later stages. Thus, the recognition of SRCs at their initial stages is a challenge because of different variants and sizes and illumination changes. The recognition process of SRCs at their early stages is costly because of the requirement for medical experts. A timely diagnosis is important because the level of the disease determines the severity, cure, and survival rate of victims. To tackle the current challenges, a deep learning (DL)-based methodology is proposed in this paper, i.e., custom CircleNet with ResNet-34 for SRC recognition and classification. We chose this method because of the circular shapes of SRCs and achieved better performance due to the CircleNet method. We utilized a challenging dataset for experimentation and performed augmentation to increase the dataset samples. The experiments were conducted using 35,000 images and attained 96.40% accuracy. We performed a comparative analysis and confirmed that our method outperforms the other methods.
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Affiliation(s)
- Saleh Albahli
- Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia;
| | - Tahira Nazir
- Faculty of Computing, Riphah International University, Islamabad 44600, Pakistan
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Suzuki H, Nonaka S, Maetani I, Matsuda T, Abe S, Yoshinaga S, Oda I, Yamagata Y, Yoshikawa T, Saito Y. Clinical and endoscopic features of metachronous gastric cancer with possible lymph node metastasis after endoscopic submucosal dissection and Helicobacter pylori eradication. Gastric Cancer 2023; 26:743-754. [PMID: 37160633 DOI: 10.1007/s10120-023-01394-1] [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: 02/06/2023] [Accepted: 04/29/2023] [Indexed: 05/11/2023]
Abstract
BACKGROUND Several studies have reported the metachronous gastric cancers (MGCs) with possible lymph node metastasis (LNM) after endoscopic submucosal dissection (ESD) and Helicobacter pylori (H. pylori) eradication in which a curative ESD had not been achieved. There have been no published reports of evaluations of the features of patients with MGC with possible LNM after ESD and H. pylori eradication. METHODS We identified 264 patients with 369 MGCs after H. pylori eradication among the 4354 patients with 5059 early gastric cancers (EGCs) who underwent ESD between 1999 and 2017 and divided them into two groups: patients with MGCs with possible LNM (Group I) and patients with MGCs undergone curative ESD (Group II). We retrospectively compared the features of patients with MGCs and patients with EGCs at index ESD in the two groups. RESULT Group I consisted of 20 patients with 21 MGCs, and Group II consisted of 244 patients with 348 MGCs. Group I lesions were significantly more common in the posterior wall than in the lesser curvature (odds ratio [OR] = 3.97; 95% confidence intervals [CI] 1.20-13.10). Development of Group I was significantly more common in patients with a body mass index (BMI) < 19.0 kg/m2 than in patients with a BMI ≥ 19.0 kg/m2 at index ESD (OR = 4.44; 95% CI 1.30-15.20). CONCLUSIONS During surveillance endoscopy after gastric ESD and H. pylori eradication, the posterior wall should be carefully examined to detect MGCs early. Lower BMI may be associated with the development of MGCs with possible LNM.
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Affiliation(s)
- Haruhisa Suzuki
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
| | - Satoru Nonaka
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Iruru Maetani
- Division of Gastroenterology, Department of Internal Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Takahisa Matsuda
- Division of Gastroenterology and Hepatology, Toho University Omori Medical Center, Tokyo, Japan
| | - Seiichiro Abe
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Shigetaka Yoshinaga
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Ichiro Oda
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Yukinori Yamagata
- Gastric Surgery Division, National Cancer Center Hospital, Tokyo, Japan
| | - Takaki Yoshikawa
- Gastric Surgery Division, National Cancer Center Hospital, Tokyo, Japan
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
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46
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Ning X, Liu R, Wang N, Xiao X, Wu S, Wang Y, Yi C, He Y, Li D, Chen H. Development of a deep learning-based model to diagnose mixed-type gastric cancer accurately. Int J Biochem Cell Biol 2023; 162:106452. [PMID: 37482265 DOI: 10.1016/j.biocel.2023.106452] [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: 04/23/2023] [Revised: 07/16/2023] [Accepted: 07/19/2023] [Indexed: 07/25/2023]
Abstract
OBJECTIVE The accurate diagnosis of mixed-type gastric cancer from pathology images presents a formidable challenge for pathologists, given its intricate features and resemblance to other subtypes of gastric cancer. Artificial Intelligence has the potential to overcome this hurdle. This study aimed to leverage deep machine learning techniques to establish a precise and efficient diagnostic approach for this cancer type which can also predict the metastatic risk using two software, U-Net and QuPath, which have not been trialled in gastric cancers. METHODS A U-Net neural network was trained to recognise, and segment differentiated components from 186 pathology images of mixed-type gastric cancer. Undifferentiated components in the same images were annotated using the open-source pathology imaging software QuPath. The outcomes from U-Net and QuPath were used to calculate the ratios of differentiation/undifferentiated components which were correlated to lymph node metastasis. RESULTS The models established by U-Net recognised ∼91% of the regions of interest, with precision, recall, and F1 values of 90.2%, 90.9% and 94.6%, respectively, indicating a high level of accuracy and reliability. Furthermore, the receiver operating characteristic curve analysis showed an area under the cure of 91%, indicating good performance. A bell-curve correlation between the differentiated/undifferentiated ratio and lymphatic metastasis was found (highest risk between 0.683 and 1.03), which is paradigm-shifting. CONCLUSION U-Net and QuPath exhibit promising accuracy in the identification of differentiated and undifferentiated components in mixed-type gastric cancer, as well as paradigm-shifting prediction of metastasis. These findings bring us one step closer to their potential clinical application.
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Affiliation(s)
- Xinjie Ning
- Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, China
| | - Ruide Liu
- Department of Pathology, The First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China
| | - Nan Wang
- Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, China
| | - Xuewen Xiao
- Department of Pathology, The First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China
| | - Siqi Wu
- Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, China
| | - Yu Wang
- Department of Respiratory Diseases, Central Medical Branch of PLA General Hospital, Beijing 100081, China
| | - Chenju Yi
- Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, China; Shenzhen Key Laboratory of Chinese Medicine Active substance screening and Translational Research, Shenzhen 518107, China; Guangdong Provincial Key Laboratory of Brain Function and Disease, Guangzhou 510080, China.
| | - Yulong He
- Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, China.
| | - Dan Li
- Department of Pathology, The First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China.
| | - Hui Chen
- School of Life Sciences, Faculty of Science, University of Technology Sydney, Ultimo, NSW 2007, Australia
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47
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Matsushima J, Sato T, Ohnishi T, Yoshimura Y, Mizutani H, Koto S, Ikeda JI, Kano M, Matsubara H, Hayashi H. The Use of Deep Learning-Based Computer Diagnostic Algorithm for Detection of Lymph Node Metastases of Gastric Adenocarcinoma. Int J Surg Pathol 2023; 31:975-981. [PMID: 35898183 DOI: 10.1177/10668969221113475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Objectives. The diversifying modalities of treatment for gastric cancer raise urgent demands for the rapid and precise diagnosis of metastases in regional lymph nodes, thereby significantly impact the workload of pathologists. Meanwhile, the recent advent of whole-slide scanners and deep-learning techniques have enabled the computer-assisted analysis of histopathological images, which could help to alleviate this impact. Thus, we developed a deep learning-based diagnostic algorithm to detect lymph node metastases of gastric adenocarcinoma and evaluated its performance. Methods. We randomly selected 20 patients with gastric adenocarcinoma who underwent surgery as definitive treatment and were found to be node metastasis-positive. HEMATOXYLIN-eosin (HE) stained glass slides, including a total of 51 metastasis-positive nodes, were retrieved from the specimens of these cases. Other slides with 776 metastasis-negative nodes were also retrieved from other twenty cases with the same disease that were diagnosed as metastasis-negative by the final pathological examinations. All glass slides were digitized using a whole-slide scanner. A deep-learning algorithm to detect metastases was developed using the data in which metastasis-positive parts of the images were annotated by a well-trained pathologist, and its performance in detecting metastases was evaluated. Results. Cross-validation analysis indicated an area of 0.9994 under the receiver operating characteristic curve. Free-response receiver operating characteristic curve (FROC) analysis indicated a sensitivity of 1.00 with three false positives. Further evaluation using an independent dataset also showed similar level of accuracies. Conclusion. This deep learning-based diagnosis-aid system is a promising tool that can assist pathologists involved in gastric cancer care and reduce their workload.
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Affiliation(s)
- Jun Matsushima
- Department of Pathology, Saitama Medical Center, Dokkyo Medical University, Saitama, Japan
- Center for Frontier Medical Engineering, Chiba University, Chiba, Japan
- Department of Diagnostic Pathology, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Tamotsu Sato
- Toshiba Digital Solutions Corporation, Kanagawa, Japan
| | - Takashi Ohnishi
- Center for Frontier Medical Engineering, Chiba University, Chiba, Japan
| | | | | | | | - Jun-Ichiro Ikeda
- Department of Diagnostic Pathology, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Masayuki Kano
- Department of Frontier Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Hisahiro Matsubara
- Department of Frontier Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Hideki Hayashi
- Center for Frontier Medical Engineering, Chiba University, Chiba, Japan
- Department of Frontier Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
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48
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Wang L, Yang Y, Yang A, Li T. Lightweight deep learning model incorporating an attention mechanism and feature fusion for automatic classification of gastric lesions in gastroscopic images. BIOMEDICAL OPTICS EXPRESS 2023; 14:4677-4695. [PMID: 37791283 PMCID: PMC10545198 DOI: 10.1364/boe.487456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 06/11/2023] [Accepted: 06/29/2023] [Indexed: 10/05/2023]
Abstract
Accurate diagnosis of various lesions in the formation stage of gastric cancer is an important problem for doctors. Automatic diagnosis tools based on deep learning can help doctors improve the accuracy of gastric lesion diagnosis. Most of the existing deep learning-based methods have been used to detect a limited number of lesions in the formation stage of gastric cancer, and the classification accuracy needs to be improved. To this end, this study proposed an attention mechanism feature fusion deep learning model with only 14 million (M) parameters. Based on that model, the automatic classification of a wide range of lesions covering the stage of gastric cancer formation was investigated, including non-neoplasm(including gastritis and intestinal metaplasia), low-grade intraepithelial neoplasia, and early gastric cancer (including high-grade intraepithelial neoplasia and early gastric cancer). 4455 magnification endoscopy with narrow-band imaging(ME-NBI) images from 1188 patients were collected to train and test the proposed method. The results of the test dataset showed that compared with the advanced gastric lesions classification method with the best performance (overall accuracy = 94.3%, parameters = 23.9 M), the proposed method achieved both higher overall accuracy and a relatively lightweight model (overall accuracy =95.6%, parameter = 14 M). The accuracy, sensitivity, and specificity of low-grade intraepithelial neoplasia were 94.5%, 93.0%, and 96.5%, respectively, achieving state-of-the-art classification performance. In conclusion, our method has demonstrated its potential in diagnosing various lesions at the stage of gastric cancer formation.
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Affiliation(s)
- Lingxiao Wang
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300192, China
| | - Yingyun Yang
- Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Aiming Yang
- Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Ting Li
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300192, China
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Nurmaini S, Rachmatullah MN, Agustiansyah P, Partan RU, Tutuko B, Rini DP, Darmawahyuni A, Firdaus F, Sapitri AI, Arum AW. CervicoXNet: an automated cervicogram interpretation network. Med Biol Eng Comput 2023; 61:2405-2416. [PMID: 37185967 DOI: 10.1007/s11517-023-02835-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 04/05/2023] [Indexed: 05/17/2023]
Abstract
Visual inspection with acetic acid (VIA) is a pre-cancerous screening program for low-middle-income countries (LMICs). Due to the limited number of oncology-gynecologist clinicians in LMICs, VIA examinations are performed mainly by medical workers. However, the inability of the medical workers to recognize a significant pattern based on cervicograms, VIA examination produces high inter-observer variance and high false-positive rate. This study proposed an automated cervicogram interpretation using explainable convolutional neural networks named "CervicoXNet" to support medical workers decision. The total number of 779 cervicograms was used for the learning process: 487 with VIA ( +) and 292 with VIA ( -). We performed data augmentation process under a geometric transformation scenario, such process produces 7325 cervicogram with VIA ( -) and 7242 cervicogram with VIA ( +). The proposed model outperformed other deep learning models, with 99.22% accuracy, 100% sensitivity, and 98.28% specificity. Moreover, to test the robustness of the proposed model, colposcope images used to validate the model's generalization ability. The results showed that the proposed architecture still produced satisfactory performance, with 98.11% accuracy, 98.33% sensitivity, and 98% specificity. It can be proven that the proposed model has been achieved satisfactory results. To make the prediction results visually interpretable, the results are localized with a heat map in fine-grained pixels using a combination of Grad-CAM and guided backpropagation. CervicoXNet can be used an alternative early screening tool with VIA alone.
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Affiliation(s)
- Siti Nurmaini
- Intelligent System Research Group, Universitas Sriwijaya, Palembang, Indonesia.
| | | | - Patiyus Agustiansyah
- Department of Obstetrics and Gynaecology, Division of Oncology-Gynaecology, Mohammad Hoesin General Hospital, Palembang, Indonesia
| | - Radiyati Umi Partan
- Department of Internal Medicine, Mohammad Hoesin General Hospital, Palembang, Indonesia
| | - Bambang Tutuko
- Intelligent System Research Group, Universitas Sriwijaya, Palembang, Indonesia
| | - Dian Palupi Rini
- Department of Informatic Engineering, Faculty of Computer Science, Universitas Sriwijaya, Palembang, Indonesia
| | - Annisa Darmawahyuni
- Intelligent System Research Group, Universitas Sriwijaya, Palembang, Indonesia
| | - Firdaus Firdaus
- Intelligent System Research Group, Universitas Sriwijaya, Palembang, Indonesia
| | - Ade Iriani Sapitri
- Intelligent System Research Group, Universitas Sriwijaya, Palembang, Indonesia
| | - Akhiar Wista Arum
- Intelligent System Research Group, Universitas Sriwijaya, Palembang, Indonesia
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50
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Kubo M, Ono S, Yokota I, Matsumoto S, Nishimura Y, Ono M, Yamamoto K, Sakamoto N. Quantitative diagnostic algorithm using endocytoscopy for superficial nonampullary duodenal epithelial tumors. J Gastroenterol Hepatol 2023; 38:1496-1502. [PMID: 37129220 DOI: 10.1111/jgh.16207] [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: 01/11/2023] [Revised: 03/03/2023] [Accepted: 04/20/2023] [Indexed: 05/03/2023]
Abstract
BACKGROUND AND AIM Optical biopsy using endocytoscopy for superficial nonampullary duodenal epithelial tumors (SNADETs) is practical; however, a diagnostic algorithm has not been established. The aim of this study was to determine correlations of endocytoscopic findings of SNADETs with histology using computer analysis and to establish an algorithm. METHODS Endocytoscopic images and histological images of duodenal lesions from 70 patients were retrospectively collected. The numbers of glands and densely stained areas with methylene blue (DSMs) per 1 mm2 and the percentage of DSMs per screen in endocytoscopy were determined. Moreover, correlations in DSMs and glands between endocytoscopy and histological images were analyzed. Histopathological diagnoses were assessed according to the revised Vienna classification. The primary outcome was correlation between the number of glands in endocytoscopy and that in histology. Finally, a diagnostic algorithm for endoscopic intervention of SNADETs with a statistical program command was established. RESULTS The number of glands in endocytoscopic images was correlated with that in histopathological images (ρ 0.64, P < 0.001). There were significant differences in the mean number of glands between category 4/5 and category 3 (P = 0.03) and the mean percentage of DSMs between category 4/5 and category 1 (P < 0.001). When the cutoffs for the number of glands and percentage of DSMs were set at 47 per 1 mm2 and 20.8% in one screen, respectively, the area under the ROC curve was 0.89. CONCLUSIONS Endocytoscopic images of SNADETs reflect histopathological atypia, and computer analysis provides a practical diagnostic algorithm for endoscopic intervention.
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Affiliation(s)
- Marina Kubo
- Department of Gastroenterology and Hepatology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, 060-8638, Japan
| | - Shoko Ono
- Division of Endoscopy, Hokkaido University Hospital, Sapporo, Hokkaido, 060-8648, Japan
| | - Isao Yokota
- Department of Biostatistics, Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, 060-8638, Japan
| | - Shogo Matsumoto
- Department of Gastroenterology and Hepatology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, 060-8638, Japan
| | - Yusuke Nishimura
- Department of Gastroenterology and Hepatology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, 060-8638, Japan
| | - Masayoshi Ono
- Department of Gastroenterology and Hepatology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, 060-8638, Japan
| | - Keiko Yamamoto
- Division of Endoscopy, Hokkaido University Hospital, Sapporo, Hokkaido, 060-8648, Japan
| | - Naoya Sakamoto
- Department of Gastroenterology and Hepatology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, 060-8638, Japan
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