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Cheng B, Wei W, Pan C, Liu L, Cheng S, Yang X, Meng P, Zhao B, Xia J, Liu H, Jia Y, Wen Y, Zhang F. Air pollutants and the risk of incident hepatobiliary diseases: A large prospective cohort study in the UK Biobank. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 949:175047. [PMID: 39074751 DOI: 10.1016/j.scitotenv.2024.175047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 06/26/2024] [Accepted: 07/24/2024] [Indexed: 07/31/2024]
Abstract
The association between air pollutants and hepatobiliary pancreatic diseases remains inconclusive. This study analyzed up to 247,091 participants of White European ancestry (aged 37 to 73 years at recruitment) from the UK Biobank, a large-scale prospective cohort with open access. An air pollution score was utilized to assess the combined effect of PM2.5, PM2.5-10, PM10, NO2, and NOX on total hepatobiliary pancreatic diseases, liver diseases, cholecyst diseases, and pancreatic diseases. Cox proportional hazard models were employed to evaluate the relationships between air pollutants and the incidence of these diseases. Restricted cubic spline regressions were used to examine the dose-response association between air pollutants and the risk of hepatobiliary pancreatic diseases. We identified 4865 cases of total hepatobiliary pancreatic diseases, over a median follow-up of 10.86 years. The air pollution scores were moderately associated with increased liver disease risk (HR = 1.009, 95 % CI: 1.004, 1.014), but not with cholecyst and pancreatic diseases. Among the individual air pollutants, PM2.5 (HR = 1.069, 95 % CI: 1.025, 1.115) and PM10 (HR = 1.036, 95 % CI: 1.011, 1.061) significantly increased liver disease risk. Males showed a higher risk of liver diseases with PM2.5 (HR = 1.075, 95 % CI: 1.015, 1.139). Additionally, individuals with overweight (HR = 1.125, 95 % CI: 1.052, 1.203), age ≥ 60 and ≤73 (HR = 1.098, 95 % CI: 1.028, 1.172), and alcohol intake ≥ 14 unit/week (HR = 1.078, 95 % CI: 1.006, 1.155) had a higher risk of developing liver diseases at high expose to PM2.5. This study suggests that prolonged exposure to ambient air pollutants may elevate the risk of liver diseases.
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Affiliation(s)
- Bolun Cheng
- NHC Key Laboratory of Environment and Endemic Diseases (Xi'an Jiaotong University), Xi'an 710061, China; Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an 710061, China; Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 710061, China
| | - Wenming Wei
- NHC Key Laboratory of Environment and Endemic Diseases (Xi'an Jiaotong University), Xi'an 710061, China; Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an 710061, China; Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 710061, China
| | - Chuyu Pan
- NHC Key Laboratory of Environment and Endemic Diseases (Xi'an Jiaotong University), Xi'an 710061, China; Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an 710061, China; Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 710061, China
| | - Li Liu
- NHC Key Laboratory of Environment and Endemic Diseases (Xi'an Jiaotong University), Xi'an 710061, China; Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an 710061, China; Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 710061, China
| | - Shiqiang Cheng
- NHC Key Laboratory of Environment and Endemic Diseases (Xi'an Jiaotong University), Xi'an 710061, China; Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an 710061, China; Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 710061, China
| | - Xuena Yang
- NHC Key Laboratory of Environment and Endemic Diseases (Xi'an Jiaotong University), Xi'an 710061, China; Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an 710061, China; Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 710061, China
| | - Peilin Meng
- NHC Key Laboratory of Environment and Endemic Diseases (Xi'an Jiaotong University), Xi'an 710061, China; Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an 710061, China; Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 710061, China
| | - Boyue Zhao
- NHC Key Laboratory of Environment and Endemic Diseases (Xi'an Jiaotong University), Xi'an 710061, China; Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an 710061, China; Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 710061, China
| | - Jinyu Xia
- NHC Key Laboratory of Environment and Endemic Diseases (Xi'an Jiaotong University), Xi'an 710061, China; Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an 710061, China; Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 710061, China
| | - Huan Liu
- NHC Key Laboratory of Environment and Endemic Diseases (Xi'an Jiaotong University), Xi'an 710061, China; Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an 710061, China; Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 710061, China
| | - Yumeng Jia
- NHC Key Laboratory of Environment and Endemic Diseases (Xi'an Jiaotong University), Xi'an 710061, China; Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an 710061, China; Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 710061, China
| | - Yan Wen
- NHC Key Laboratory of Environment and Endemic Diseases (Xi'an Jiaotong University), Xi'an 710061, China; Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an 710061, China; Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 710061, China
| | - Feng Zhang
- NHC Key Laboratory of Environment and Endemic Diseases (Xi'an Jiaotong University), Xi'an 710061, China; Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an 710061, China; Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 710061, China.
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Handa P, Goel N, Indu S, Gunjan D. AI-KODA score application for cleanliness assessment in video capsule endoscopy frames. MINIM INVASIV THER 2024:1-10. [PMID: 39138994 DOI: 10.1080/13645706.2024.2390879] [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: 02/08/2024] [Accepted: 07/03/2024] [Indexed: 08/15/2024]
Abstract
BACKGROUND Currently, there is no automated method for assessing cleanliness in video capsule endoscopy (VCE). Our objectives were to automate the process of evaluating and collecting medical scores of VCE frames according to the existing KOrea-CanaDA (KODA) scoring system by developing an easy-to-use mobile application called artificial intelligence-KODA (AI-KODA) score, as well as to determine the inter-rater and intra-rater reliability of the KODA score among three readers for prospective AI applications, and check the efficacy of the application. METHOD From the 28 patient capsule videos considered, 1539 sequential frames were selected at five-minute intervals, and 634 random frames were selected at random intervals during small bowel transit. The frames were processed and shifted to AI-KODA. Three readers (gastroenterology fellows), who had been trained in reading VCE, rated 2173 frames in duplicate four weeks apart after completing the training module on AI-KODA. The scores were saved automatically in real time. Reliability was assessed for each video using estimate of intra-class correlation coefficients (ICCs). Then, the AI dataset was developed using the frames and their respective scores, and it was subjected to automatic classification of the scores via the random forest and the k-nearest neighbors classifiers. RESULTS For sequential frames, ICCs for inter-rater variability were 'excellent' to 'good' among the three readers, while ICCs for intra-rater variability were 'good' to 'moderate'. For random frames, ICCs for inter-rater and intra-rater variability were 'excellent' among the three readers. The overall accuracy achieved was up to 61% for the random forest classifier and 62.38% for the k-nearest neighbors classifier. CONCLUSIONS AI-KODA automates the process of scoring VCE frames based on the existing KODA score. It saves time in cleanliness assessment and is user-friendly for research and clinical use. Comprehensive benchmarking of the AI dataset is in process.
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Affiliation(s)
- Palak Handa
- Department of Electronics and Communication Engineering, Delhi Technological University, Delhi, India
| | - Nidhi Goel
- Department of Electronics and Communication Engineering, Indira Gandhi Delhi Technical University for Women, Delhi, India
| | - Sreedevi Indu
- Department of Electronics and Communication Engineering, Delhi Technological University, Delhi, India
| | - Deepak Gunjan
- Department of Gastroenterology & HNU, All India Institute of Medical Sciences, New Delhi, India
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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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Zhang J, Dawkins A. Artificial Intelligence in Ultrasound Imaging: Where Are We Now? Ultrasound Q 2024; 40:93-97. [PMID: 38842384 DOI: 10.1097/ruq.0000000000000680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
Affiliation(s)
- Jie Zhang
- From the Department of Radiology, University of Kentucky, Lexington, KY
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An P, Wang Z. Application value of an artificial intelligence-based diagnosis and recognition system in gastroscopy training for graduate students in gastroenterology: a preliminary study. Wien Med Wochenschr 2024; 174:173-180. [PMID: 37676426 DOI: 10.1007/s10354-023-01020-w] [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: 04/25/2023] [Accepted: 07/20/2023] [Indexed: 09/08/2023]
Abstract
OBJECTIVE This study aimed to discuss the application value of an artificial intelligence-based diagnosis and recognition system (AIDRS) in the teaching activities for Bachelor of Medicine and Bachelor of Surgery (MBBS) in China. The learning performance of graduate students in gastroenterology during gastroscopy training with and without AIDRS was assessed. METHODS The study recruited 32 graduate students of the gastroenterology program at Jiangsu province hospital of Chinese medicine and Xiangyang No. 1 People's Hospital from March 2018 to March 2022 and randomly divided them into AIDRS (n = 16) and non-AIDRS (n = 16) groups. The AIDRS software was used for real-time monitoring of blind spots of gastroscopy to aid in lesion diagnosis and recognition in the AIDRS group. Only a conventional gastroscopic procedure was implemented in the non-AIDRS group. The final performance score, success rate of gastroscopy, lesion detection rate, and pain score of patients were compared between the two groups during gastroscopy. A self-prepared teaching and learning satisfaction questionnaire was administered to the two groups of students. RESULTS The AIDRS group had a higher final performance score (92.60 ± 2.83 vs. 89.21 ± 3.57, t = 2.98, P < 0.05), a higher success rate of gastroscopy (448/480 vs. 417/480, χ2 = 11.23, P < 0.05), and a higher detection rate of lesions (51/52 vs. 41/53, χ2 = 8.56, P < 0.05) compared with the non-AIDRS group. The pain scores of patients were lower in the AIDRS group than in the non-AIDRS group (3.40 [2.23, 3.98] vs. 4.45 [3.72, 4.75], Z = 3.04, P < 0.05). Besides, the average time for gastroscopy was lower in the AIDRS group than in the non-AIDRS group (7.15 ± 1.24 vs. 8.21 ± 1.26, t = 2.38, P = 0.02). The overall satisfaction level with the teaching program was higher in the AIDRS group (43.51 ± 2.29 vs. 40.93 ± 2.07, t = 3.33, P < 0.05). CONCLUSION In the context of medicine-education cooperation, AIDRS offered valuable assistance in gastroscopy training and increased the success rate of gastroscopy and teaching and learning satisfaction. AIDRS is worthy of wider-scale promotion.
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Affiliation(s)
- Peng An
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu province hospital of Chinese medicine, 155 Hanzhong Road, 210029, Nanjing, Jiangsu, China
- Department of Radiology and gastroenterology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, 441000, Xiangyang, China
| | - Zhongqiu Wang
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu province hospital of Chinese medicine, 155 Hanzhong Road, 210029, Nanjing, Jiangsu, China.
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Zhou J, Song W, Liu Y, Yuan X. An efficient computational framework for gastrointestinal disorder prediction using attention-based transfer learning. PeerJ Comput Sci 2024; 10:e2059. [PMID: 38855223 PMCID: PMC11157572 DOI: 10.7717/peerj-cs.2059] [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: 03/06/2024] [Accepted: 04/23/2024] [Indexed: 06/11/2024]
Abstract
Diagnosing gastrointestinal (GI) disorders, which affect parts of the digestive system such as the stomach and intestines, can be difficult even for experienced gastroenterologists due to the variety of ways these conditions present. Early diagnosis is critical for successful treatment, but the review process is time-consuming and labor-intensive. Computer-aided diagnostic (CAD) methods provide a solution by automating diagnosis, saving time, reducing workload, and lowering the likelihood of missing critical signs. In recent years, machine learning and deep learning approaches have been used to develop many CAD systems to address this issue. However, existing systems need to be improved for better safety and reliability on larger datasets before they can be used in medical diagnostics. In our study, we developed an effective CAD system for classifying eight types of GI images by combining transfer learning with an attention mechanism. Our experimental results show that ConvNeXt is an effective pre-trained network for feature extraction, and ConvNeXt+Attention (our proposed method) is a robust CAD system that outperforms other cutting-edge approaches. Our proposed method had an area under the receiver operating characteristic curve of 0.9997 and an area under the precision-recall curve of 0.9973, indicating excellent performance. The conclusion regarding the effectiveness of the system was also supported by the values of other evaluation metrics.
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Affiliation(s)
- Jiajie Zhou
- Huai’an First People’s Hospital, Nanjing Medical University, Jiangsu, China
| | - Wei Song
- Huai’an First People’s Hospital, Nanjing Medical University, Jiangsu, China
| | - Yeliu Liu
- Huai’an First People’s Hospital, Nanjing Medical University, Jiangsu, China
| | - Xiaoming Yuan
- Huai’an First People’s Hospital, Nanjing Medical University, Jiangsu, China
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Davila-Piñón P, Nogueira-Rodríguez A, Díez-Martín AI, Codesido L, Herrero J, Puga M, Rivas L, Sánchez E, Fdez-Riverola F, Glez-Peña D, Reboiro-Jato M, López-Fernández H, Cubiella J. Optical diagnosis in still images of colorectal polyps: comparison between expert endoscopists and PolyDeep, a Computer-Aided Diagnosis system. Front Oncol 2024; 14:1393815. [PMID: 38846970 PMCID: PMC11153726 DOI: 10.3389/fonc.2024.1393815] [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: 02/29/2024] [Accepted: 04/22/2024] [Indexed: 06/09/2024] Open
Abstract
Background PolyDeep is a computer-aided detection and classification (CADe/x) system trained to detect and classify polyps. During colonoscopy, CADe/x systems help endoscopists to predict the histology of colonic lesions. Objective To compare the diagnostic performance of PolyDeep and expert endoscopists for the optical diagnosis of colorectal polyps on still images. Methods PolyDeep Image Classification (PIC) is an in vitro diagnostic test study. The PIC database contains NBI images of 491 colorectal polyps with histological diagnosis. We evaluated the diagnostic performance of PolyDeep and four expert endoscopists for neoplasia (adenoma, sessile serrated lesion, traditional serrated adenoma) and adenoma characterization and compared them with the McNemar test. Receiver operating characteristic curves were constructed to assess the overall discriminatory ability, comparing the area under the curve of endoscopists and PolyDeep with the chi- square homogeneity areas test. Results The diagnostic performance of the endoscopists and PolyDeep in the characterization of neoplasia is similar in terms of sensitivity (PolyDeep: 89.05%; E1: 91.23%, p=0.5; E2: 96.11%, p<0.001; E3: 86.65%, p=0.3; E4: 91.26% p=0.3) and specificity (PolyDeep: 35.53%; E1: 33.80%, p=0.8; E2: 34.72%, p=1; E3: 39.24%, p=0.8; E4: 46.84%, p=0.2). The overall discriminative ability also showed no statistically significant differences (PolyDeep: 0.623; E1: 0.625, p=0.8; E2: 0.654, p=0.2; E3: 0.629, p=0.9; E4: 0.690, p=0.09). In the optical diagnosis of adenomatous polyps, we found that PolyDeep had a significantly higher sensitivity and a significantly lower specificity. The overall discriminative ability of adenomatous lesions by expert endoscopists is significantly higher than PolyDeep (PolyDeep: 0.582; E1: 0.685, p < 0.001; E2: 0.677, p < 0.0001; E3: 0.658, p < 0.01; E4: 0.694, p < 0.0001). Conclusion PolyDeep and endoscopists have similar diagnostic performance in the optical diagnosis of neoplastic lesions. However, endoscopists have a better global discriminatory ability than PolyDeep in the optical diagnosis of adenomatous polyps.
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Affiliation(s)
- Pedro Davila-Piñón
- Research Group in Gastrointestinal Oncology Ourense, Hospital Universitario de Ourense, Ourense, Spain
- Fundación Pública Galega de Investigación Biomédica Galicia Sur, Complexo Hospitalario Universitario de Ourense, Sergas, Ourense, Spain
| | - Alba Nogueira-Rodríguez
- Department of Computer Science, Escuela Superior de Ingenieria Informática (ESEI), CINBIO, University of Vigo, Ourense, Spain
- Next Generation Computer Systems Group (SING) Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), Ourense, Spain
| | - Astrid Irene Díez-Martín
- Research Group in Gastrointestinal Oncology Ourense, Hospital Universitario de Ourense, Ourense, Spain
- Fundación Pública Galega de Investigación Biomédica Galicia Sur, Complexo Hospitalario Universitario de Ourense, Sergas, Ourense, Spain
| | - Laura Codesido
- Research Group in Gastrointestinal Oncology Ourense, Hospital Universitario de Ourense, Ourense, Spain
- Fundación Pública Galega de Investigación Biomédica Galicia Sur, Complexo Hospitalario Universitario de Ourense, Sergas, Ourense, Spain
| | - Jesús Herrero
- Research Group in Gastrointestinal Oncology Ourense, Hospital Universitario de Ourense, Ourense, Spain
- Department of Gastroenterology, Hospital Universitario de Ourense, Ourense, Spain
- Department of Gastroenterology, Hospital Universitario de Ourense, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Ourense, Spain
| | - Manuel Puga
- Research Group in Gastrointestinal Oncology Ourense, Hospital Universitario de Ourense, Ourense, Spain
- Department of Gastroenterology, Hospital Universitario de Ourense, Ourense, Spain
- Department of Gastroenterology, Hospital Universitario de Ourense, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Ourense, Spain
| | - Laura Rivas
- Research Group in Gastrointestinal Oncology Ourense, Hospital Universitario de Ourense, Ourense, Spain
- Department of Gastroenterology, Hospital Universitario de Ourense, Ourense, Spain
- Department of Gastroenterology, Hospital Universitario de Ourense, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Ourense, Spain
| | - Eloy Sánchez
- Research Group in Gastrointestinal Oncology Ourense, Hospital Universitario de Ourense, Ourense, Spain
- Department of Gastroenterology, Hospital Universitario de Ourense, Ourense, Spain
- Department of Gastroenterology, Hospital Universitario de Ourense, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Ourense, Spain
| | - Florentino Fdez-Riverola
- Department of Computer Science, Escuela Superior de Ingenieria Informática (ESEI), CINBIO, University of Vigo, Ourense, Spain
- Next Generation Computer Systems Group (SING) Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), Ourense, Spain
| | - Daniel Glez-Peña
- Department of Computer Science, Escuela Superior de Ingenieria Informática (ESEI), CINBIO, University of Vigo, Ourense, Spain
- Next Generation Computer Systems Group (SING) Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), Ourense, Spain
| | - Miguel Reboiro-Jato
- Department of Computer Science, Escuela Superior de Ingenieria Informática (ESEI), CINBIO, University of Vigo, Ourense, Spain
- Next Generation Computer Systems Group (SING) Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), Ourense, Spain
| | - Hugo López-Fernández
- Department of Computer Science, Escuela Superior de Ingenieria Informática (ESEI), CINBIO, University of Vigo, Ourense, Spain
- Next Generation Computer Systems Group (SING) Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), Ourense, Spain
| | - Joaquín Cubiella
- Research Group in Gastrointestinal Oncology Ourense, Hospital Universitario de Ourense, Ourense, Spain
- Department of Gastroenterology, Hospital Universitario de Ourense, Ourense, Spain
- Department of Gastroenterology, Hospital Universitario de Ourense, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Ourense, Spain
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Savino A, Rondonotti E, Rocchetto S, Piagnani A, Bina N, Di Domenico P, Segatta F, Radaelli F. GI genius endoscopy module: a clinical profile. Expert Rev Med Devices 2024; 21:359-372. [PMID: 38618982 DOI: 10.1080/17434440.2024.2342508] [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: 10/31/2023] [Accepted: 04/09/2024] [Indexed: 04/16/2024]
Abstract
INTRODUCTION The identification of early-stage colorectal cancers (CRC) and the resection of pre-cancerous neoplastic lesions through colonoscopy allows to decrease both CRC incidence and mortality. However, colonoscopy miss rates up to 26% for adenomas and 9% for advanced adenomas have been reported. In recent years, artificial intelligence (AI) systems have been emerging as easy-to-use tools, potentially lowering the risk of missing lesions. AREAS COVERED This review paper focuses on GI Genius device (Medtronic Co. Minneapolis, MN, U.S.A.) a computer-assisted tool designed to assist endoscopists during standard white-light colonoscopies in detecting mucosal lesions. EXPERT OPINION Randomized controlled trials (RCTs) suggest that GI Genius is a safe and effective tool for improving adenoma detection, especially in CRC screening and surveillance colonoscopies. However, its impact seems to be less significant among experienced endoscopists and in real-world clinical scenarios compared to the controlled conditions of RCTs. Furthermore, it appears that GI Genius mainly enhances the detection of non-advanced, small polyps, but does not significantly impact the identification of advanced and difficult-to-detect adenoma. When using GI Genius, no complications were documented. Only a small number of studies reported an increased in withdrawal time or the removal of non-neoplastic lesions.
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Affiliation(s)
- Alberto Savino
- Division of Gastroenterology, Department of Medicine and Surgery, University of Milano-Bicocca, Milano, Italy
| | | | - Simone Rocchetto
- Gastroenterology Unit, Valduce Hospital, Como, Italy
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Gastroenterology and Hepatology, University of Milan, Milan, Italy
| | - Alessandra Piagnani
- Gastroenterology Unit, Valduce Hospital, Como, Italy
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Gastroenterology and Hepatology, University of Milan, Milan, Italy
| | - Niccolò Bina
- Gastroenterology Unit, Valduce Hospital, Como, Italy
| | - Pasquale Di Domenico
- Gastrointestinal Unit, Department of Medicine, Surgery & Dentistry Scuola Medica Salernitana, University of Salerno, Salerno, Italy
| | - Francesco Segatta
- Gastroenterology Unit, Valduce Hospital, Como, Italy
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Gastroenterology and Hepatology, University of Milan, Milan, Italy
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Tagliamonte G, Santagata F, Fraquelli M. Current Developments and Role of Intestinal Ultrasound including the Advent of AI. Diagnostics (Basel) 2024; 14:759. [PMID: 38611672 PMCID: PMC11011653 DOI: 10.3390/diagnostics14070759] [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: 02/14/2024] [Revised: 03/18/2024] [Accepted: 03/29/2024] [Indexed: 04/14/2024] Open
Abstract
Intestinal ultrasound is a non-invasive, safe, and cost-effective technique to study the small and large intestines. In addition to conventional B-mode and color doppler imaging, new US tools have been developed in more recent years that provide auxiliary data on many GI conditions, improving the diagnosis and assessment of relevant outcomes. We have reviewed the more recent literature (from 2010 onwards) on auxiliary tools in bowel ultrasound such as elastography techniques, CEUS, SICUS, and the potential contribution by artificial intelligence (AI) to overcome current intestinal ultrasound limitations. For this scoping review, we performed an extensive literature search on PubMed and EMBASE to identify studies published until December 2023 and investigating the application of elastography techniques, CEUS, SICUS, and AI in the ultrasonographic assessment of the small and large intestines. Multiparametric intestinal ultrasound shows promising capabilities in Crohn's disease, while less is known about the role in ulcerative colitis. Despite some evidence, the CEUS role as a point-of-care examination tool for rare conditions such as intestinal GvHD and ischemic small bowel disease seems promising, possibly avoiding the need to perform further cross-sectional imaging. The use of AI in intestinal ultrasound is still anecdotical and limited to acute appendicitis.
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Affiliation(s)
- Gennaro Tagliamonte
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano, 20122 Milan, Italy; (G.T.); (F.S.)
| | - Fabrizio Santagata
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano, 20122 Milan, Italy; (G.T.); (F.S.)
| | - Mirella Fraquelli
- Division of Gastroenterology and Endoscopy, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
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10
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Goh WW, Chia KY, Cheung MF, Kee KM, Lwin MO, Schulz PJ, Chen M, Wu K, Ng SS, Lui R, Ang TL, Yeoh KG, Chiu HM, Wu DC, Sung JJ. Risk Perception, Acceptance, and Trust of Using AI in Gastroenterology Practice in the Asia-Pacific Region: Web-Based Survey Study. JMIR AI 2024; 3:e50525. [PMID: 38875591 PMCID: PMC11041476 DOI: 10.2196/50525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/28/2023] [Accepted: 11/23/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND The use of artificial intelligence (AI) can revolutionize health care, but this raises risk concerns. It is therefore crucial to understand how clinicians trust and accept AI technology. Gastroenterology, by its nature of being an image-based and intervention-heavy specialty, is an area where AI-assisted diagnosis and management can be applied extensively. OBJECTIVE This study aimed to study how gastroenterologists or gastrointestinal surgeons accept and trust the use of AI in computer-aided detection (CADe), computer-aided characterization (CADx), and computer-aided intervention (CADi) of colorectal polyps in colonoscopy. METHODS We conducted a web-based questionnaire from November 2022 to January 2023, involving 5 countries or areas in the Asia-Pacific region. The questionnaire included variables such as background and demography of users; intention to use AI, perceived risk; acceptance; and trust in AI-assisted detection, characterization, and intervention. We presented participants with 3 AI scenarios related to colonoscopy and the management of colorectal polyps. These scenarios reflect existing AI applications in colonoscopy, namely the detection of polyps (CADe), characterization of polyps (CADx), and AI-assisted polypectomy (CADi). RESULTS In total, 165 gastroenterologists and gastrointestinal surgeons responded to a web-based survey using the structured questionnaire designed by experts in medical communications. Participants had a mean age of 44 (SD 9.65) years, were mostly male (n=116, 70.3%), and mostly worked in publicly funded hospitals (n=110, 66.67%). Participants reported relatively high exposure to AI, with 111 (67.27%) reporting having used AI for clinical diagnosis or treatment of digestive diseases. Gastroenterologists are highly interested to use AI in diagnosis but show different levels of reservations in risk prediction and acceptance of AI. Most participants (n=112, 72.72%) also expressed interest to use AI in their future practice. CADe was accepted by 83.03% (n=137) of respondents, CADx was accepted by 78.79% (n=130), and CADi was accepted by 72.12% (n=119). CADe and CADx were trusted by 85.45% (n=141) of respondents and CADi was trusted by 72.12% (n=119). There were no application-specific differences in risk perceptions, but more experienced clinicians gave lesser risk ratings. CONCLUSIONS Gastroenterologists reported overall high acceptance and trust levels of using AI-assisted colonoscopy in the management of colorectal polyps. However, this level of trust depends on the application scenario. Moreover, the relationship among risk perception, acceptance, and trust in using AI in gastroenterology practice is not straightforward.
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Affiliation(s)
- Wilson Wb Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
- Center for Biomedical Informatics, Nanyang Technological University, Singapore, Singapore
| | - Kendrick Ya Chia
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
- Center for Biomedical Informatics, Nanyang Technological University, Singapore, Singapore
| | - Max Fk Cheung
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore, Singapore
| | - Kalya M Kee
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore, Singapore
- Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore
| | - May O Lwin
- Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore
| | - Peter J Schulz
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore, Singapore
- Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore
| | - Minhu Chen
- The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Kaichun Wu
- Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Simon Sm Ng
- Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)
| | - Rashid Lui
- Prince of Wales Hospital, Hospital Authority, Hong Kong, China (Hong Kong)
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, SingHealth, Singapore, Singapore
| | - Khay Guan Yeoh
- Department of Gastroenterology and Hepatology, National University Hospital, National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Han-Mo Chiu
- Department of Internal Medicine, National Taiwan University Hospital, Taiwan, China
- Department of Internal Medicine, College of Medicine, National Taiwan University, Taiwan, China
| | | | - Joseph Jy Sung
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore, Singapore
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11
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Reddy S, Shaheed A, Seo Y, Patel R. Development of an Artificial Intelligence Model for the Classification of Gastric Carcinoma Stages Using Pathology Slides. Cureus 2024; 16:e56740. [PMID: 38650818 PMCID: PMC11033212 DOI: 10.7759/cureus.56740] [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: 03/22/2024] [Indexed: 04/25/2024] Open
Abstract
This study showcases a novel AI-driven approach to accurately differentiate between stage one and stage two gastric carcinoma based on pathology slide analysis. Gastric carcinoma, a significant contributor to cancer-related mortality globally, necessitates precise staging for optimal treatment planning and patient management. Leveraging a comprehensive dataset of 3540 high-resolution pathology images sourced from Kaggle.com, comprising an equal distribution of stage one and stage two tumors, the developed AI model demonstrates remarkable performance in tumor staging. Through the application of state-of-the-art deep learning techniques on Google's Collaboration platform, the model achieves outstanding accuracy and precision rates of 100%, accompanied by notable sensitivity (97.09%), specificity (100%), and F1-score (98.31%). Additionally, the model exhibits an impressive area under the receiver operating characteristic curve (AUC) of 0.999, indicating superior discriminatory power and robustness. By providing clinicians with an efficient and reliable tool for gastric carcinoma staging, this AI-driven approach has the potential to significantly enhance diagnostic accuracy, inform treatment decisions, and ultimately improve patient outcomes in the management of gastric carcinoma. This research contributes to the ongoing advancement of cancer diagnosis and underscores the transformative potential of artificial intelligence in clinical practice.
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Affiliation(s)
- Shreya Reddy
- Biomedical Sciences, Creighton University, Omaha, USA
| | - Avneet Shaheed
- Pathology, University of Illinois at Chicago, Chicago, USA
| | - Yui Seo
- Medicine, California Northstate University College of Medicine, Elk Grove, USA
| | - Rakesh Patel
- Internal Medicine, East Tennessee State University Quillen College of Medicine, Johnson City, USA
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12
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Williams LM, Carpenter WT, Carretta C, Papanastasiou E, Vaidyanathan U. Precision psychiatry and Research Domain Criteria: Implications for clinical trials and future practice. CNS Spectr 2024; 29:26-39. [PMID: 37675453 DOI: 10.1017/s1092852923002420] [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] [Indexed: 09/08/2023]
Abstract
Psychiatric disorders are associated with significant social and economic burdens, many of which are related to issues with current diagnosis and treatments. The coronavirus (COVID-19) pandemic is estimated to have increased the prevalence and burden of major depressive and anxiety disorders, indicating an urgent need to strengthen mental health systems globally. To date, current approaches adopted in drug discovery and development for psychiatric disorders have been relatively unsuccessful. Precision psychiatry aims to tailor healthcare more closely to the needs of individual patients and, when informed by neuroscience, can offer the opportunity to improve the accuracy of disease classification, treatment decisions, and prevention efforts. In this review, we highlight the growing global interest in precision psychiatry and the potential for the National Institute of Health-devised Research Domain Criteria (RDoC) to facilitate the implementation of transdiagnostic and improved treatment approaches. The need for current psychiatric nosology to evolve with recent scientific advancements and increase awareness in emerging investigators/clinicians of the value of this approach is essential. Finally, we examine current challenges and future opportunities of adopting the RDoC-associated translational and transdiagnostic approaches in clinical studies, acknowledging that the strength of RDoC is that they form a dynamic framework of guiding principles that is intended to evolve continuously with scientific developments into the future. A collaborative approach that recruits expertise from multiple disciplines, while also considering the patient perspective, is needed to pave the way for precision psychiatry that can improve the prognosis and quality of life of psychiatric patients.
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Affiliation(s)
- Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC), Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - William T Carpenter
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | - Evangelos Papanastasiou
- Boehringer Ingelheim Pharma GmbH & Co, Ingelheim am Rhein, Rhineland-Palatinate, Germany
- HMNC Holding GmbH, Wilhelm-Wagenfeld-Strasse 20, 80807Munich, Bavaria, Germany
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13
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Xie G, Xiao H, Liu Q, Chen T, Chen F, Zhou K, Wang X, Liu P, Jia Z, Chen L, Deng X, Meng F, Zhang Z, Chi X, Jia W. LiveBoost: A GB-based prediction system for liver fibrosis in chronic hepatitis B patients in China - A multi-center retrospective study. Heliyon 2024; 10:e24161. [PMID: 38293489 PMCID: PMC10825336 DOI: 10.1016/j.heliyon.2024.e24161] [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: 05/16/2023] [Revised: 11/24/2023] [Accepted: 01/04/2024] [Indexed: 02/01/2024] Open
Abstract
Background The aim of this study was to evaluate the accuracy of LiveBoost™, a gradient boosting (GB)-based prediction system based on standard biochemical values (AST, ALT, platelet count) and age, in Chinese patients with chronic hepatitis B (CHB) and compare its performance with FIB-4 (fibrosis-4 score) and APRI (the aspartate transaminase to platelet ratio index). Methods This retrospective trial enrolled 454 participants, including 279 CHB patients who underwent liver biopsy and 175 normal controls from 3 centers in China. All participants underwent laboratory blood testing. LiveBoost was constructed using GB and FIB-4 and APRI were calculated from laboratory data. Results LiveBoost outperformed APRI and FIB-4 in predicting hepatic fibrosis and cirrhosis. The GB model had an AUROC of 0.977 for CHB diagnosis, 0.804 for early and advanced fibrosis, and 0.836 for non-cirrhosis and cirrhosis, compared to AUROC of 0.554, 0.673 and 0.720 for FIB-4, AUROC of 0.977, 0.652 and 0.654 for APRI. Conclusions LiveBoost is a more reliable and cost-effective method than APRI and FIB-4 for assessing liver fibrosis in Chinese patients with CHB.
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Affiliation(s)
- Guoxiang Xie
- Human Metabolomics Institute Inc., Shenzhen, Guangdong, China
| | - Huanming Xiao
- Hepatology Department, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China
| | - Quan Liu
- Human Metabolomics Institute Inc., Shenzhen, Guangdong, China
| | - Tianlu Chen
- Center for Translational Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fengyan Chen
- Human Metabolomics Institute Inc., Shenzhen, Guangdong, China
| | - Kejun Zhou
- Human Metabolomics Institute Inc., Shenzhen, Guangdong, China
| | - Xiaoning Wang
- Institute of Interdisciplinary Integrative Medicine Research, Key Laboratory of Liver and Kidney Diseases (Ministry of Education), Institute of Liver Diseases, Shuguang Hospital, Department of Pharmacology, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ping Liu
- Institute of Interdisciplinary Integrative Medicine Research, Key Laboratory of Liver and Kidney Diseases (Ministry of Education), Institute of Liver Diseases, Shuguang Hospital, Department of Pharmacology, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Zhifeng Jia
- Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China
| | - Lei Chen
- Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China
| | - Xin Deng
- Hepatology Department, The Third People's Hospital of Shenzhen, Shenzhen, China
| | - Fankun Meng
- Ultrasound and Functional Diagnosis Center, Beijing Youan Hospital affiliated to Capital Medical University, Beijing, China
| | - Zhenhua Zhang
- Department of Infectious Diseases and Institute of Clinical Virology, The Second Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Xiaoling Chi
- Hepatology Department, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China
| | - Wei Jia
- Human Metabolomics Institute Inc., Shenzhen, Guangdong, China
- School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China
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14
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Aiumtrakul N, Thongprayoon C, Arayangkool C, Vo KB, Wannaphut C, Suppadungsuk S, Krisanapan P, Garcia Valencia OA, Qureshi F, Miao J, Cheungpasitporn W. Personalized Medicine in Urolithiasis: AI Chatbot-Assisted Dietary Management of Oxalate for Kidney Stone Prevention. J Pers Med 2024; 14:107. [PMID: 38248809 PMCID: PMC10817681 DOI: 10.3390/jpm14010107] [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: 12/23/2023] [Revised: 01/13/2024] [Accepted: 01/16/2024] [Indexed: 01/23/2024] Open
Abstract
Accurate information regarding oxalate levels in foods is essential for managing patients with hyperoxaluria, oxalate nephropathy, or those susceptible to calcium oxalate stones. This study aimed to assess the reliability of chatbots in categorizing foods based on their oxalate content. We assessed the accuracy of ChatGPT-3.5, ChatGPT-4, Bard AI, and Bing Chat to classify dietary oxalate content per serving into low (<5 mg), moderate (5-8 mg), and high (>8 mg) oxalate content categories. A total of 539 food items were processed through each chatbot. The accuracy was compared between chatbots and stratified by dietary oxalate content categories. Bard AI had the highest accuracy of 84%, followed by Bing (60%), GPT-4 (52%), and GPT-3.5 (49%) (p < 0.001). There was a significant pairwise difference between chatbots, except between GPT-4 and GPT-3.5 (p = 0.30). The accuracy of all the chatbots decreased with a higher degree of dietary oxalate content categories but Bard remained having the highest accuracy, regardless of dietary oxalate content categories. There was considerable variation in the accuracy of AI chatbots for classifying dietary oxalate content. Bard AI consistently showed the highest accuracy, followed by Bing Chat, GPT-4, and GPT-3.5. These results underline the potential of AI in dietary management for at-risk patient groups and the need for enhancements in chatbot algorithms for clinical accuracy.
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Affiliation(s)
- Noppawit Aiumtrakul
- Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI 96813, USA; (N.A.); (C.A.); (K.B.V.); (C.W.)
| | - Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.S.); (O.A.G.V.); (F.Q.); (J.M.); (W.C.)
| | - Chinnawat Arayangkool
- Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI 96813, USA; (N.A.); (C.A.); (K.B.V.); (C.W.)
| | - Kristine B. Vo
- Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI 96813, USA; (N.A.); (C.A.); (K.B.V.); (C.W.)
| | - Chalothorn Wannaphut
- Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI 96813, USA; (N.A.); (C.A.); (K.B.V.); (C.W.)
| | - Supawadee Suppadungsuk
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.S.); (O.A.G.V.); (F.Q.); (J.M.); (W.C.)
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan 10540, Thailand
| | - Pajaree Krisanapan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.S.); (O.A.G.V.); (F.Q.); (J.M.); (W.C.)
- Division of Nephrology, Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand
| | - Oscar A. Garcia Valencia
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.S.); (O.A.G.V.); (F.Q.); (J.M.); (W.C.)
| | - Fawad Qureshi
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.S.); (O.A.G.V.); (F.Q.); (J.M.); (W.C.)
| | - Jing Miao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.S.); (O.A.G.V.); (F.Q.); (J.M.); (W.C.)
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.S.); (O.A.G.V.); (F.Q.); (J.M.); (W.C.)
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15
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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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Yang XL, Ni DH, Yu Y, Zhao JC, Lin R, Xiu C, Chang ZX. Value of magnetic resonance imaging radiomics features in predicting histologic grade of invasive ductal carcinoma of the breast. Technol Health Care 2024; 32:1609-1618. [PMID: 38393931 PMCID: PMC11091567 DOI: 10.3233/thc-230671] [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: 05/25/2023] [Accepted: 08/10/2023] [Indexed: 02/25/2024]
Abstract
BACKGROUND Breast cancer has the second highest mortality rate of all cancers and occurs mainly in women. OBJECTIVE To investigate the relationship between magnetic resonance imaging (MRI) radiomics features and histological grade of invasive ductal carcinoma (IDC) of the breast and to evaluate its diagnostic efficacy. METHODS The two conventional MRI quantitative indicators, i.e. the apparent diffusion coefficient (ADC) and the initial enhancement rate, were collected from 112 patients with breast cancer. The breast cancer lesions were manually segmented in dynamic contrast-enhanced MRI (DCE-MRI) and ADC images, the differences in radiomics features between Grades I, II and III IDCs were compared and the diagnostic efficacy was evaluated. RESULTS The ADC values (0.77 ± 0.22 vs 0.91 ± 0.22 vs 0.92 ± 0.20, F= 4.204, p< 0.01), as well as the B_sum_variance (188.51 ± 67.803 vs 265.37 ± 77.86 vs 263.74 ± 82.58, F= 6.040, p< 0.01), L_energy (0.03 ± 0.02 vs 0.13 ± 0.11 vs 0.12 ± 0.14, F= 7.118, p< 0.01) and L_sum_average (0.78 ± 0.32 vs 16.34 ± 4.23 vs 015.45 ± 3.74, F= 21.860, p< 0.001) values of patients with Grade III IDC were significantly lower than those of patients with Grades I and II IDC. The B_uniform (0.15 ± 0.12 vs 0.11 ± 0.04 vs 0.12 ± 0.03, F= 3.797, p< 0.01) and L_SRE (0.85 ± 0.07 vs 0.78 ± 0.03 vs 0.79 ± 0.32, F= 3.024, p< 0.01) values of patients with Grade III IDC were significantly higher than those of patients with Grades I and II IDC. All differences were statistically significant (p< 0.05). The ADC radiomics signature model had a higher area-under-the-curve value in identifying different grades of IDC than the ADC value model and the DCE radiomics signature model (0.869 vs 0.711 vs 0.682). The accuracy (0.812 vs 0.647 vs 0.710), specificity (0.731 vs 0.435 vs 0.342), positive predictive value (0.815 vs 0.663 vs 0.669) and negative predictive value (0.753 vs 0.570 vs 0.718) of the ADC radiomics signature model were all significantly better than the ADC value model and the DCE radiomics signature model. CONCLUSION ADC values and breast MRI radiomics signatures are significant in identifying the histological grades of IDC, with the ADC radiomics signatures having greater value.
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Affiliation(s)
- Xin-Lei Yang
- Blood Tumor Treatment Center, Beihua University Affiliated Hospital, Jilin, China
| | - Dong-He Ni
- Blood Tumor Treatment Center, Beihua University Affiliated Hospital, Jilin, China
- Department of Radiology, Jilin Province Integrated Traditional Chinese and Western Medicine Hospital, Jilin, China
| | - Yang Yu
- Department of Radiology, Beihua University Affiliated Hospital, Jilin, China
| | - Jin-Cui Zhao
- Department of Radiology, Beihua University Affiliated Hospital, Jilin, China
| | - Rui Lin
- Blood Tumor Treatment Center, Beihua University Affiliated Hospital, Jilin, China
| | - Chao Xiu
- Department of Radiology, Beihua University Affiliated Hospital, Jilin, China
| | - Zhe-Xing Chang
- Blood Tumor Treatment Center, Beihua University Affiliated Hospital, Jilin, China
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17
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Jiang VS, Pavlovic ZJ, Hariton E. The Role of Artificial Intelligence and Machine Learning in Assisted Reproductive Technologies. Obstet Gynecol Clin North Am 2023; 50:747-762. [PMID: 37914492 DOI: 10.1016/j.ogc.2023.09.003] [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: 11/03/2023]
Abstract
Artificial intelligence (AI) and machine learning, the form most commonly used in medicine, offer powerful tools utilizing the strengths of large data sets and intelligent algorithms. These systems can help to revolutionize delivery of treatments, access to medical care, and improvement of outcomes, particularly in the realm of reproductive medicine. Whether that is more robust oocyte and embryo grading or more accurate follicular measurement, AI will be able to aid clinicians, and most importantly patients, in providing the best possible and individualized care. However, despite all of the potential strengths of AI, algorithms are not immune to bias and are vulnerable to the many socioeconomic and demographic biases that current healthcare systems suffer from. Wrong diagnoses as well is furthering of healthcare discrimination are real possibilities if both the capabilities and limitations of AI are not well understood. Armed with appropriate knowledge of how AI can most appropriately operate within medicine, and specifically reproductive medicine, will enable clinicians to both create and utilize machine learning-based innovations for the furthering of reproductive medicine and ultimately achieving the goal of building of healthy families.
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Affiliation(s)
- Victoria S Jiang
- Division of Reproductive Endocrinology & Infertility, Vincent Department of Obstetrics and Gynecology, Massachusetts General Hospital/Harvard Medical School, 55 Fruit Street, Suite 10A, Boston, MA 02116, USA
| | - Zoran J Pavlovic
- Department of Obstetrics and Gynecology/Reproductive Endocrinology and Infertility, University of South Florida, Morsani College of Medicine, 2 Tampa General Circle, 6th Floor, Suite 6022, Tampa, FL 33602, USA
| | - Eduardo Hariton
- Reproductive Science Center of the San Francisco Bay Area, 100 Park Place #200, San Ramon, CA 94583, USA.
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Schulz PJ, Lwin MO, Kee KM, Goh WWB, Lam TYT, Sung JJY. Modeling the influence of attitudes, trust, and beliefs on endoscopists' acceptance of artificial intelligence applications in medical practice. Front Public Health 2023; 11:1301563. [PMID: 38089040 PMCID: PMC10715310 DOI: 10.3389/fpubh.2023.1301563] [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: 09/25/2023] [Accepted: 11/03/2023] [Indexed: 12/18/2023] Open
Abstract
Introduction The potential for deployment of Artificial Intelligence (AI) technologies in various fields of medicine is vast, yet acceptance of AI amongst clinicians has been patchy. This research therefore examines the role of antecedents, namely trust, attitude, and beliefs in driving AI acceptance in clinical practice. Methods We utilized online surveys to gather data from clinicians in the field of gastroenterology. Results A total of 164 participants responded to the survey. Participants had a mean age of 44.49 (SD = 9.65). Most participants were male (n = 116, 70.30%) and specialized in gastroenterology (n = 153, 92.73%). Based on the results collected, we proposed and tested a model of AI acceptance in medical practice. Our findings showed that while the proposed drivers had a positive impact on AI tools' acceptance, not all effects were direct. Trust and belief were found to fully mediate the effects of attitude on AI acceptance by clinicians. Discussion The role of trust and beliefs as primary mediators of the acceptance of AI in medical practice suggest that these should be areas of focus in AI education, engagement and training. This has implications for how AI systems can gain greater clinician acceptance to engender greater trust and adoption amongst public health systems and professional networks which in turn would impact how populations interface with AI. Implications for policy and practice, as well as future research in this nascent field, are discussed.
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Affiliation(s)
- Peter J. Schulz
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore
| | - May O. Lwin
- Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore
| | - Kalya M. Kee
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore
| | - Wilson W. B. Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
- Center for Biomedical Informatics, Nanyang Technological University, Singapore, Singapore
| | - Thomas Y. T Lam
- Faculty of Medicine, Institute of Digestive Diseases, The Chinese University of Hong Kong, Hong Kong, China
| | - Joseph J. Y. Sung
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
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Adiwinata R, Tandarto K, Arifputra J, Waleleng BJ, Gosal F, Rotty L, Winarta J, Waleleng A, Simadibrata P, Simadibrata M. The Impact of Artificial Intelligence in Improving Polyp and Adenoma Detection Rate During Colonoscopy: Systematic-Review and Meta-Analysis. Asian Pac J Cancer Prev 2023; 24:3655-3663. [PMID: 38019222 PMCID: PMC10772777 DOI: 10.31557/apjcp.2023.24.11.3655] [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: 06/25/2023] [Accepted: 11/10/2023] [Indexed: 11/30/2023] Open
Abstract
INTRODUCTION Colonoscopy may detect colorectal polyp and facilitate its removal in order to prevent colorectal cancer. However, substantial miss rate for colorectal adenomas detection still occurred during screening colonoscopy procedure. Nowadays, artificial intelligence (AI) have been employed in trials to improve polyp detection rate (PDR) and adenoma detection rate (ADR). Therefore, we would like to determine the impact of AI in increasing PDR and ADR. METHODS The present study adhered to the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-analyses 2020 (PRISMA 2020) statement. To identify relevant literature, comprehensive searches were conducted on major scientific databases, including Pubmed, EBSCO-host, and Proquest. The search was limited to articles published up to November 30, 2022. Inclusion criteria for the study encompassed full-text accessibility, articles written in the English language, and randomized controlled trials (RCTs) that reported both ADR and PDR values, comparing conventional diagnostic methods with AI-aided approaches. To synthesize the data, we computed the combined pooled odds ratio (OR) using a random-effects model. This model was chosen due to the expectation of considerable heterogeneity among the selected studies. To evaluate potential publication bias, the Begg's funnel diagram was employed. RESULTS A total of 13 studies were included in this study. Colonoscopy with AI had significantly higher PDR compared to without AI (pooled OR 1.46, 95% CI 1.13-1.89, p = 0.003) and higher ADR (pooled OR 1.58, 95% CI 1.37-1.82, p < 0.00001). PDR analysis showed moderate heterogeneity between included studies (p = 0.004; I2=63%). Furthermore, ADR analysis showed moderate heterogeneity (p < 0.007; I2 = 57%). Additionally, the funnels plot of ADR and PDR analysis showed an asymmetry plot and low publication bias. CONCLUSION AI may improve colonoscopy result quality through improving PDR and ADR.
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Affiliation(s)
- Randy Adiwinata
- Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Kevin Tandarto
- S.K Lerik Regional Public Hospital, Kupang, East Nusa Tenggara, Indonesia.
| | - Jonathan Arifputra
- Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Bradley Jimmy Waleleng
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Fandy Gosal
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Luciana Rotty
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Jeanne Winarta
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Andrew Waleleng
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Paulus Simadibrata
- Department of Internal Medicine, Abdi Waluyo Hospital, Jakarta, Indonesia.
| | - Marcellus Simadibrata
- Division of Gastroenterology, Pancreatobiliary and Digestive Endoscopy, Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia, Cipto Mangunkusumo General Hospital, Jakarta, Indonesia.
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Giordano A, Escapa M, Urpí-Ferreruela M, Casanova G, Fernández-Esparrach G, Ginès À, Llach J, González-Suárez B. Diagnostic accuracy of artificial intelligence-aided capsule endoscopy (TOP100) in overt small bowel bleeding. Surg Endosc 2023; 37:7658-7666. [PMID: 37495849 PMCID: PMC10520091 DOI: 10.1007/s00464-023-10273-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 07/02/2023] [Indexed: 07/28/2023]
Abstract
BACKGROUND Capsule endoscopy (CE) is the first-choice exploration in case of overt small bowel bleeding (SBB). An early CE is known to increase diagnostic yield, but long reading times may delay therapeutics. The study evaluates the diagnostic performance of the artificial intelligence tool TOP100 in patients with overt SBB undergoing early CE with Pillcam SB3. METHODS Patients who underwent early CE (up to 14 days from the bleeding episode) for suspected overt SBB were included. One experienced endoscopist prospectively performed standard reading (SR) and a second blind experienced endoscopist performed a TOP100-based reading (TR). The primary endpoint was TR diagnostic accuracy for lesions with high bleeding potential (P2). RESULTS A total of 111 patients were analyzed. The most common clinical presentation was melena (64%). CE showed angiodysplasias in 40.5% of patients (45/111). In per-patient analysis, TR showed a sensitivity of 90.48% (95% CI 82.09-95.80), specificity of 100% (95% CI 87.23-100) with a PPV of 100% (95% CI 94.01-100), NPV of 77.14% (95% CI 63.58-86.71) and diagnostic accuracy of 92.79 (86.29-96.84). At multivariate analysis, adequate intestinal cleansing was the only independent predictor of concordance between TR and SR (OR 2.909, p = 0.019). The median reading time for SR and TR was 23 min (18.0-26.8) and 1.9 min (range 1.7-2.1), respectively (p < 0.001). CONCLUSIONS TOP100 provides a fast-reading mode for early CE in case of overt small bowel bleeding. It identifies most patients with active bleeding and angiodysplasias, aiding in the prioritization of therapeutic procedures. However, its accuracy in detecting ulcers, varices and P1 lesions seems insufficient.
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Affiliation(s)
- Antonio Giordano
- Endoscopy Unit, Gastroenterology Department, ICMDM, Hospital Clínic de Barcelona, Barcelona, Spain
- Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Miriam Escapa
- Endoscopy Unit, Gastroenterology Department, ICMDM, Hospital Clínic de Barcelona, Barcelona, Spain
| | - Miquel Urpí-Ferreruela
- Endoscopy Unit, Gastroenterology Department, ICMDM, Hospital Clínic de Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Gherzon Casanova
- Endoscopy Unit, Gastroenterology Department, ICMDM, Hospital Clínic de Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Gloria Fernández-Esparrach
- Endoscopy Unit, Gastroenterology Department, ICMDM, Hospital Clínic de Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Barcelona, Spain
- Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Àngels Ginès
- Endoscopy Unit, Gastroenterology Department, ICMDM, Hospital Clínic de Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Barcelona, Spain
- Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Josep Llach
- Endoscopy Unit, Gastroenterology Department, ICMDM, Hospital Clínic de Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Barcelona, Spain
- Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Begoña González-Suárez
- Endoscopy Unit, Gastroenterology Department, ICMDM, Hospital Clínic de Barcelona, Barcelona, Spain.
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Barcelona, Spain.
- Faculty of Medicine, University of Barcelona, Barcelona, Spain.
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Piraianu AI, Fulga A, Musat CL, Ciobotaru OR, Poalelungi DG, Stamate E, Ciobotaru O, Fulga I. Enhancing the Evidence with Algorithms: How Artificial Intelligence Is Transforming Forensic Medicine. Diagnostics (Basel) 2023; 13:2992. [PMID: 37761359 PMCID: PMC10529115 DOI: 10.3390/diagnostics13182992] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 09/13/2023] [Accepted: 09/14/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND The integration of artificial intelligence (AI) into various fields has ushered in a new era of multidisciplinary progress. Defined as the ability of a system to interpret external data, learn from it, and adapt to specific tasks, AI is poised to revolutionize the world. In forensic medicine and pathology, algorithms play a crucial role in data analysis, pattern recognition, anomaly identification, and decision making. This review explores the diverse applications of AI in forensic medicine, encompassing fields such as forensic identification, ballistics, traumatic injuries, postmortem interval estimation, forensic toxicology, and more. RESULTS A thorough review of 113 articles revealed a subset of 32 papers directly relevant to the research, covering a wide range of applications. These included forensic identification, ballistics and additional factors of shooting, traumatic injuries, post-mortem interval estimation, forensic toxicology, sexual assaults/rape, crime scene reconstruction, virtual autopsy, and medical act quality evaluation. The studies demonstrated the feasibility and advantages of employing AI technology in various facets of forensic medicine and pathology. CONCLUSIONS The integration of AI in forensic medicine and pathology offers promising prospects for improving accuracy and efficiency in medico-legal practices. From forensic identification to post-mortem interval estimation, AI algorithms have shown the potential to reduce human subjectivity, mitigate errors, and provide cost-effective solutions. While challenges surrounding ethical considerations, data security, and algorithmic correctness persist, continued research and technological advancements hold the key to realizing the full potential of AI in forensic applications. As the field of AI continues to evolve, it is poised to play an increasingly pivotal role in the future of forensic medicine and pathology.
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Affiliation(s)
| | - Ana Fulga
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza St., 800010 Galati, Romania; (A.-I.P.); (C.L.M.); (O.-R.C.); (D.G.P.); (O.C.); (I.F.)
| | | | | | | | - Elena Stamate
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza St., 800010 Galati, Romania; (A.-I.P.); (C.L.M.); (O.-R.C.); (D.G.P.); (O.C.); (I.F.)
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22
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Tee CHN, Ravi R, Ang TL, Li JW. Role of artificial intelligence in Barrett’s esophagus. Artif Intell Gastroenterol 2023; 4:28-35. [DOI: 10.35712/aig.v4.i2.28] [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: 03/06/2023] [Revised: 05/17/2023] [Accepted: 06/12/2023] [Indexed: 09/07/2023] Open
Abstract
The application of artificial intelligence (AI) in gastrointestinal endoscopy has gained significant traction over the last decade. One of the more recent applications of AI in this field includes the detection of dysplasia and cancer in Barrett’s esophagus (BE). AI using deep learning methods has shown promise as an adjunct to the endoscopist in detecting dysplasia and cancer. Apart from visual detection and diagnosis, AI may also aid in reducing the considerable interobserver variability in identifying and distinguishing dysplasia on whole slide images from digitized BE histology slides. This review aims to provide a comprehensive summary of the key studies thus far as well as providing an insight into the future role of AI in Barrett’s esophagus.
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Affiliation(s)
- Chin Hock Nicholas Tee
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore 529889, Singapore
| | - Rajesh Ravi
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore 529889, Singapore
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore 529889, Singapore
| | - James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore 529889, Singapore
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23
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Vodanović M, Subašić M, Milošević DP, Galić I, Brkić H. Artificial intelligence in forensic medicine and forensic dentistry. THE JOURNAL OF FORENSIC ODONTO-STOMATOLOGY 2023; 41:30-41. [PMID: 37634174 PMCID: PMC10473456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Abstract
This review article aims to highlight the current possibilities for applying Artificial Intelligence in modern forensic medicine and forensic dentistry and present the advantages and disadvantages of its use. For this purpose, the relevant academic literature was searched using PubMed, Web of Science and Scopus. The application of Artificial Intelligence in forensic medicine and forensic dentistry is still in its early stages. However, the possibilities are great, and the future will show what is applicable in daily practice. Artificial Intelligence will improve the accuracy and efficiency of work in forensic medicine and forensic dentistry; it can automate some tasks; and enhance the quality of evidence. Disadvantages of the application of Artificial Intelligence may be related to discrimination, transparency, accountability, privacy, security, ethics and others. Artificial Intelligence systems should be used as a support tool, not as a replacement for forensic experts.
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Affiliation(s)
- M Vodanović
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
| | - M Subašić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - D P Milošević
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - I Galić
- School of Medicine, University of Split, Croatia
| | - H Brkić
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
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24
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Komanduri S, Sethi A, Muthusamy VR. Future Implications of Innovation in Gastroenterology for Clinical Practice: A Call to Action. Am J Gastroenterol 2023; 118:1307-1310. [PMID: 36705491 DOI: 10.14309/ajg.0000000000002194] [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: 12/02/2022] [Accepted: 01/19/2023] [Indexed: 01/28/2023]
Affiliation(s)
- Srinadh Komanduri
- Northewestern University, Feinberg School of Medicine, Chicago, Illinois, USA
| | - Amrita Sethi
- Columbia University Irving Medical Center, New York, New York, USA
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Poalelungi DG, Musat CL, Fulga A, Neagu M, Neagu AI, Piraianu AI, Fulga I. Advancing Patient Care: How Artificial Intelligence Is Transforming Healthcare. J Pers Med 2023; 13:1214. [PMID: 37623465 PMCID: PMC10455458 DOI: 10.3390/jpm13081214] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 07/27/2023] [Accepted: 07/28/2023] [Indexed: 08/26/2023] Open
Abstract
Artificial Intelligence (AI) has emerged as a transformative technology with immense potential in the field of medicine. By leveraging machine learning and deep learning, AI can assist in diagnosis, treatment selection, and patient monitoring, enabling more accurate and efficient healthcare delivery. The widespread implementation of AI in healthcare has the role to revolutionize patients' outcomes and transform the way healthcare is practiced, leading to improved accessibility, affordability, and quality of care. This article explores the diverse applications and reviews the current state of AI adoption in healthcare. It concludes by emphasizing the need for collaboration between physicians and technology experts to harness the full potential of AI.
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Affiliation(s)
- Diana Gina Poalelungi
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
| | - Carmina Liana Musat
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
| | - Ana Fulga
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
| | - Marius Neagu
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
| | - Anca Iulia Neagu
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
- ‘Saint John’ Clinical Emergency Hospital for Children, 800487 Galati, Romania
| | - Alin Ionut Piraianu
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
| | - Iuliu Fulga
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
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McDonnell KJ. Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise. J Clin Med 2023; 12:4830. [PMID: 37510945 PMCID: PMC10381436 DOI: 10.3390/jcm12144830] [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: 06/07/2023] [Revised: 07/05/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
Over the last 75 years, artificial intelligence has evolved from a theoretical concept and novel paradigm describing the role that computers might play in our society to a tool with which we daily engage. In this review, we describe AI in terms of its constituent elements, the synthesis of which we refer to as the AI Silecosystem. Herein, we provide an historical perspective of the evolution of the AI Silecosystem, conceptualized and summarized as a Kuhnian paradigm. This manuscript focuses on the role that the AI Silecosystem plays in oncology and its emerging importance in the care of the community oncology patient. We observe that this important role arises out of a unique alliance between the academic oncology enterprise and community oncology practices. We provide evidence of this alliance by illustrating the practical establishment of the AI Silecosystem at the City of Hope Comprehensive Cancer Center and its team utilization by community oncology providers.
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Affiliation(s)
- Kevin J McDonnell
- Center for Precision Medicine, Department of Medical Oncology & Therapeutics Research, City of Hope Comprehensive Cancer Center, Duarte, CA 91010, USA
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Galati JS, Lin K, Gross SA. Recent advances in devices and technologies that might prove revolutionary for colonoscopy procedures. Expert Rev Med Devices 2023; 20:1087-1103. [PMID: 37934873 DOI: 10.1080/17434440.2023.2280773] [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: 03/27/2023] [Accepted: 11/03/2023] [Indexed: 11/09/2023]
Abstract
INTRODUCTION Colorectal cancer (CRC) is the third most common malignancy and second leading cause of cancer-related mortality in the world. Adenoma detection rate (ADR), a quality indicator for colonoscopy, has gained prominence as it is inversely related to CRC incidence and mortality. As such, recent efforts have focused on developing novel colonoscopy devices and technologies to improve ADR. AREAS COVERED The main objective of this paper is to provide an overview of advancements in the fields of colonoscopy mechanical attachments, artificial intelligence-assisted colonoscopy, and colonoscopy optical enhancements with respect to ADR. We accomplished this by performing a comprehensive search of multiple electronic databases from inception to September 2023. This review is intended to be an introduction to colonoscopy devices and technologies. EXPERT OPINION Numerous mechanical attachments and optical enhancements have been developed that have the potential to improve ADR and AI has gone from being an inaccessible concept to a feasible means for improving ADR. While these advances are exciting and portend a change in what will be considered standard colonoscopy, they continue to require refinement. Future studies should focus on combining modalities to further improve ADR and exploring the use of these technologies in other facets of colonoscopy.
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Affiliation(s)
- Jonathan S Galati
- Department of Internal Medicine, NYU Langone Health, New York, NY, USA
| | - Kevin Lin
- Department of Internal Medicine, NYU Langone Health, New York, NY, USA
| | - Seth A Gross
- Division of Gastroenterology, NYU Langone Health, New York, NY, USA
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Ganesh N, Hanauer SB, Dulai PS. The importance of predicting patient responses to monoclonal antibodies for Crohn's disease. Expert Opin Biol Ther 2023; 23:941-949. [PMID: 37623370 DOI: 10.1080/14712598.2023.2252339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/15/2023] [Accepted: 08/23/2023] [Indexed: 08/26/2023]
Abstract
INTRODUCTION Crohn's disease (CD) is a chronic immune-mediated inflammatory bowel disease that results in relapsing and remitting symptoms but progressive transmural bowel damage leading to significant morbidity. CD results from dysregulation of the immune system related to genetic and environmental factors. While the use of monoclonal antibodies targeting cytokines and adhesion molecules has been shown to improve outcomes in CD patients, their widespread use has been limited due to high costs as well as variable access. Here, we summarize the factors that have been shown to correlate with responsiveness to biologic agents for use in practice. AREAS COVERED We summarize the current literature regarding factors that have been shown to influence patient response to various biologic agents including: patient-related factors (e.g. age, gender, weight smoking history); disease-specific factors (e.g. disease duration, location/extension, behavior/phenotype, severity); genetic markers; transcription factors, and the gut microbiome. Finally, we review the utility of prediction models and present data supporting the use of recently developed decision support tools. EXPERT OPINION Clinical decision support tools developed by machine learning are currently available for the selection of biologic agents in CD patients. We expect these models to become an integral tool for clinicians in the treatment of CD in the coming years.
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Affiliation(s)
- Nisha Ganesh
- Department of Medicine, Division of Gastroenterology and Hepatology, Northwestern University, Chicago, IL, United States of America
| | - Stephen B Hanauer
- Department of Medicine, Division of Gastroenterology and Hepatology, Northwestern University, Chicago, IL, United States of America
| | - Parambir S Dulai
- Department of Medicine, Division of Gastroenterology and Hepatology, Northwestern University, Chicago, IL, United States of America
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Barkun AN, von Renteln D, Sadri H. Cost-effectiveness of Artificial Intelligence-Aided Colonoscopy for Adenoma Detection in Colon Cancer Screening. J Can Assoc Gastroenterol 2023; 6:97-105. [PMID: 37273970 PMCID: PMC10235593 DOI: 10.1093/jcag/gwad014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/06/2023] Open
Abstract
Background and Aims Artificial intelligence-aided colonoscopy significantly improves adenoma detection. We assessed the cost-effectiveness of the GI Genius technology, an artificial intelligence-aided computer diagnosis for polyp detection (CADe), in improving colorectal cancer outcomes, adopting a Canadian health care perspective. Methods A Markov model with 1-year cycles and a lifetime horizon was used to estimate incremental cost-effectiveness ratio comparing CADe to conventional colonoscopy polyp detection amongst patients with a positive faecal immunochemical test. Outcomes were life years (LYs) and quality-adjusted life years (QALY) gained. The analysis applied costs associated with health care resource utilization, including procedures and follow-ups, from a provincial payer's perspective using 2022 Canadian dollars. Effectiveness and cost data were sourced from the literature and publicly available databases. Extensive probabilistic and deterministic sensitivity analyses were performed, assessing model robustness. Results Life years and QALY gains for the CADe and conventional colonoscopy groups were 19.144 versus 19.125 and 17.137 versus 17.113, respectively. CADe and conventional colonoscopies' overall per-case costs were $2990.74 and $3004.59, respectively. With a willingness-to-pay pre-set at $50,000/QALY, the incremental cost-effectiveness ratio was dominant for both outcomes, showing that CADe colonoscopy is cost-effective. Deterministic sensitivity analysis confirmed that the model was sensitive to the incidence risk ratio of adenoma per colonoscopy for large adenomas. Probabilistic sensitivity analysis showed that the CADe strategy was cost-effective in up to 73.4% of scenarios. Conclusion The addition of CADe solution to colonoscopy is a dominant, cost-effective strategy when used in faecal immunochemical test-positive patients in a Canadian health care setting.
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Affiliation(s)
- Alan N Barkun
- Correspondence: Alan N. Barkun, MD, CM, MSc, Division of Gastroenterology, McGill University Health Center, Montreal, Quebec, Canada; Clinical Epidemiology, McGill University, Montreal, Quebec, Canada, 1650 Cedar Avenue, D7.346, Montreal, Quebec H3G1A4, Canada, e-mail:
| | - Daniel von Renteln
- Division of Gastroenterology, the University of Montreal Hospital and University of Montreal Hospital Research Center, Montreal, Quebec, Canada
| | - Hamid Sadri
- Department of Health Economics and Outcomes Research, Medtronic Canada, Brampton, Ontario, Canada
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30
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Mansour NM. Artificial Intelligence in Colonoscopy. Curr Gastroenterol Rep 2023; 25:122-129. [PMID: 37129831 DOI: 10.1007/s11894-023-00872-x] [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] [Accepted: 04/03/2023] [Indexed: 05/03/2023]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) is a rapidly growing field in gastrointestinal endoscopy, and its potential applications are virtually endless, with studies demonstrating use of AI for early gastric cancer, inflammatory bowel disease, Barrett's esophagus, capsule endoscopy, as well as other areas in gastroenterology. Much of the early studies and applications of AI in gastroenterology have revolved around colonoscopy, particularly with regards to real-time polyp detection and characterization. This review will cover much of the existing data on computer-aided detection (CADe), computer-aided diagnosis (CADx), and briefly discuss some other interesting applications of AI for colonoscopy, while also considering some of the challenges and limitations that exist around the use of AI for colonoscopy. RECENT FINDINGS Multiple randomized controlled trials have now been published which show a statistically significant improvement when using AI to improve adenoma detection and reduce adenoma miss rates during colonoscopy. There is also a growing pool of literature showing that AI can be helpful for characterizing/diagnosing colorectal polyps in real time. AI has also shown promise in other areas of colonoscopy, including polyp sizing and automated measurement and monitoring of quality metrics during colonoscopy. AI is a promising tool that has the ability to shape the future of gastrointestinal endoscopy, with much of the early data showing significant benefits to use of AI during colonoscopy. However, there remain several challenges that may delay or hamper the widespread use of AI in the field.
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Affiliation(s)
- Nabil M Mansour
- Section of Gastroenterology and Hepatology, Baylor College of Medicine, 7200 Cambridge St., Suite 8B, Houston, TX, 77030, USA.
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Lopez-Bernal D, Balderas D, Ponce P, Rojas M, Molina A. Implications of Artificial Intelligence Algorithms in the Diagnosis and Treatment of Motor Neuron Diseases-A Review. Life (Basel) 2023; 13:life13041031. [PMID: 37109560 PMCID: PMC10146231 DOI: 10.3390/life13041031] [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: 12/22/2022] [Revised: 02/17/2023] [Accepted: 03/29/2023] [Indexed: 04/29/2023] Open
Abstract
Motor neuron diseases (MNDs) are a group of chronic neurological disorders characterized by the progressive failure of the motor system. Currently, these disorders do not have a definitive treatment; therefore, it is of huge importance to propose new and more advanced diagnoses and treatment options for MNDs. Nowadays, artificial intelligence is being applied to solve several real-life problems in different areas, including healthcare. It has shown great potential to accelerate the understanding and management of many health disorders, including neurological ones. Therefore, the main objective of this work is to offer a review of the most important research that has been done on the application of artificial intelligence models for analyzing motor disorders. This review includes a general description of the most commonly used AI algorithms and their usage in MND diagnosis, prognosis, and treatment. Finally, we highlight the main issues that must be overcome to take full advantage of what AI can offer us when dealing with MNDs.
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Affiliation(s)
- Diego Lopez-Bernal
- Tecnologico de Monterrey, National Department of Research, Puente 222, Del. Tlalpan, Mexico City 14380, Mexico
| | - David Balderas
- Tecnologico de Monterrey, National Department of Research, Puente 222, Del. Tlalpan, Mexico City 14380, Mexico
| | - Pedro Ponce
- Tecnologico de Monterrey, National Department of Research, Puente 222, Del. Tlalpan, Mexico City 14380, Mexico
| | - Mario Rojas
- Tecnologico de Monterrey, National Department of Research, Puente 222, Del. Tlalpan, Mexico City 14380, Mexico
| | - Arturo Molina
- Tecnologico de Monterrey, National Department of Research, Puente 222, Del. Tlalpan, Mexico City 14380, Mexico
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Ruan W, Narula P, Fishman DS. Upskilling Pediatric Ileocolonoscopy. Gastrointest Endosc Clin N Am 2023; 33:253-265. [PMID: 36948745 DOI: 10.1016/j.giec.2022.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2023]
Abstract
Upskilling in ileocolonoscopy is an important aspect of pediatric endoscopic practice as it enables endoscopists to learn additional skills through education and training to improve outcomes. With the advent of technologies, endoscopy is continuously evolving. Many devices can be applied to improve endoscopy quality and ergonomics. In addition, techniques such as dynamic position change can be employed to increase procedural efficiency and completeness. Key to upskilling is enhancing endoscopists' cognitive, technical and nontechnical skills and the concept of "training the trainer" to ensure trainers have the requisite skills to teach endoscopy effectively. This chapter details aspects of upskilling pediatric ileocolonoscopy.
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Affiliation(s)
- Wenly Ruan
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA; Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Texas Children's Hospital, Houston, TX, USA
| | - Priya Narula
- Department of Paediatric Gastroenterology, Sheffield Children's NHS Foundation, TrustWestern Bank, Sheffield S10 2TH, United Kingdom
| | - Douglas S Fishman
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA; Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Texas Children's Hospital, Houston, TX, USA.
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Novak I, Bass LM. Gastrointestinal Bleeding in Children: Current Management, Controversies, and Advances. Gastrointest Endosc Clin N Am 2023; 33:401-421. [PMID: 36948753 DOI: 10.1016/j.giec.2022.11.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2023]
Abstract
Upper gastrointestinal bleeding (UGIB) in children has many causes, with its prevalence varying by age. Often presenting as hematemesis or melena, the initial treatment is stabilization of the patient, including protection of the airway, fluid resuscitation, and a transfusion hemoglobin threshold of 7 g/L. Endoscopy should be performed with the goal of using combinations of therapies to treat a bleeding lesion, generally involving epinephrine injection along with either cautery, hemoclips, or hemospray. This review discusses the diagnosis and treatment of variceal and non-variceal gastrointestinal bleeding in children with a focus on current advances in the treatment of severe UGIB.
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Affiliation(s)
- Inna Novak
- Department of Pediatrics, Division of Pediatric Gastroenterology, Hepatology and Nutrition, Children's Hospital at Montefiore, Albert Einstein College of Medicine, 3415 Bainbridge Avenue, Bronx, NY 10467, USA.
| | - Lee M Bass
- Division of Gastroenterology, Hepatology and Nutrition, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, 225 E Chicago Avenue, Chicago, IL 60611, USA
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Zhu JQ, Wang ML, Li Y, Zhang W, Li LJ, Liu L, Zhang Y, Han CJ, Tie CW, Wang SX, Wang GQ, Ni XG. Convolutional neural network based anatomical site identification for laryngoscopy quality control: A multicenter study. Am J Otolaryngol 2023; 44:103695. [PMID: 36473265 DOI: 10.1016/j.amjoto.2022.103695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 09/26/2022] [Accepted: 11/19/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVES Video laryngoscopy is an important diagnostic tool for head and neck cancers. The artificial intelligence (AI) system has been shown to monitor blind spots during esophagogastroduodenoscopy. This study aimed to test the performance of AI-driven intelligent laryngoscopy monitoring assistant (ILMA) for landmark anatomical sites identification on laryngoscopic images and videos based on a convolutional neural network (CNN). MATERIALS AND METHODS The laryngoscopic images taken from January to December 2018 were retrospectively collected, and ILMA was developed using the CNN model of Inception-ResNet-v2 + Squeeze-and-Excitation Networks (SENet). A total of 16,000 laryngoscopic images were used for training. These were assigned to 20 landmark anatomical sites covering six major head and neck regions. In addition, the performance of ILMA in identifying anatomical sites was validated using 4000 laryngoscopic images and 25 videos provided by five other tertiary hospitals. RESULTS ILMA identified the 20 anatomical sites on the laryngoscopic images with a total accuracy of 97.60 %, and the average sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 100 %, 99.87 %, 97.65 %, and 99.87 %, respectively. In addition, multicenter clinical verification displayed that the accuracy of ILMA in identifying the 20 targeted anatomical sites in 25 laryngoscopic videos from five hospitals was ≥95 %. CONCLUSION The proposed CNN-based ILMA model can rapidly and accurately identify the anatomical sites on laryngoscopic images. The model can reflect the coverage of anatomical regions of the head and neck by laryngoscopy, showing application potential in improving the quality of laryngoscopy.
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Affiliation(s)
- Ji-Qing Zhu
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mei-Ling Wang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Ying Li
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Wei Zhang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Li-Juan Li
- Department of Otorhinolaryngology, The People's Hospital of Wenshan Prefecture, Wenshan, Yunnan, China
| | - Lin Liu
- Department of Otolaryngology-Head and Neck Surgery, Dalian Municipal Friendship Hospital, Dalian, Liaoning, China
| | - Yan Zhang
- Department of Otorhinolaryngology, Chongqing Traditional Chinese Medicine Hospital, Chongqing, China
| | - Cai-Juan Han
- Department of Otolaryngology-Head and Neck Surgery, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, Shandong, China
| | - Cheng-Wei Tie
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shi-Xu Wang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Gui-Qi Wang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Xiao-Guang Ni
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
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Vodanović M, Subašić M, Milošević D, Savić Pavičin I. Artificial Intelligence in Medicine and Dentistry. Acta Stomatol Croat 2023; 57:70-84. [PMID: 37288152 PMCID: PMC10243707 DOI: 10.15644/asc57/1/8] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 03/01/2023] [Indexed: 09/14/2023] Open
Abstract
INTRODUCTION Artificial intelligence has been applied in various fields throughout history, but its integration into daily life is more recent. The first applications of AI were primarily in academia and government research institutions, but as technology has advanced, AI has also been applied in industry, commerce, medicine and dentistry. OBJECTIVE Considering that the possibilities of applying artificial intelligence are developing rapidly and that this field is one of the areas with the greatest increase in the number of newly published articles, the aim of this paper was to provide an overview of the literature and to give an insight into the possibilities of applying artificial intelligence in medicine and dentistry. In addition, the aim was to discuss its advantages and disadvantages. CONCLUSION The possibilities of applying artificial intelligence to medicine and dentistry are just being discovered. Artificial intelligence will greatly contribute to developments in medicine and dentistry, as it is a tool that enables development and progress, especially in terms of personalized healthcare that will lead to much better treatment outcomes.
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Affiliation(s)
- Marin Vodanović
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
- University Hospital Centre Zagreb, Croatia
| | - Marko Subašić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - Denis Milošević
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - Ivana Savić Pavičin
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
- University Hospital Centre Zagreb, Croatia
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Artificial Intelligence-The Rising Star in the Field of Gastroenterology and Hepatology. Diagnostics (Basel) 2023; 13:diagnostics13040662. [PMID: 36832150 PMCID: PMC9955763 DOI: 10.3390/diagnostics13040662] [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: 12/05/2022] [Revised: 01/31/2023] [Accepted: 02/07/2023] [Indexed: 02/12/2023] Open
Abstract
Artificial intelligence (AI) is a term that covers a multitude of techniques that are used in a manner that tries to reproduce human intelligence. AI is helpful in various medical specialties that use imaging for diagnostic purposes, and gastroenterology is no exception. In this field, AI has several applications, such as detecting and classifying polyps, detecting the malignancy in polyps, diagnosing Helicobacter pylori infection, gastritis, inflammatory bowel disease, gastric cancer, esophageal neoplasia, and pancreatic and hepatic lesions. The aim of this mini-review is to analyze the currently available studies regarding AI in the field of gastroenterology and hepatology and to discuss its main applications as well as its main limitations.
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Advances in Analgosedation and Periprocedural Care for Gastrointestinal Endoscopy. Life (Basel) 2023; 13:life13020473. [PMID: 36836830 PMCID: PMC9962362 DOI: 10.3390/life13020473] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/28/2023] [Accepted: 02/06/2023] [Indexed: 02/10/2023] Open
Abstract
The number and complexity of endoscopic gastrointestinal diagnostic and therapeutic procedures is globally increasing. Procedural analgosedation during gastrointestinal endoscopic procedures has become the gold standard of gastrointestinal endoscopies. Patient satisfaction and safety are important for the quality of the technique. Currently there are no uniform sedation guidelines and protocols for specific gastrointestinal endoscopic procedures, and there are several challenges surrounding the choice of an appropriate analgosedation technique. These include categories of patients, choice of drug, appropriate monitoring, and medical staff providing the service. The ideal analgosedation technique should enable the satisfaction of the patient, their maximum safety and, at the same time, cost-effectiveness. Although propofol is the gold standard and the most used general anesthetic for endoscopies, its use is not without risks such as pain at the injection site, respiratory depression, and hypotension. New studies are looking for alternatives to propofol, and drugs like remimazolam and ciprofol are in the focus of researchers' interest. New monitoring techniques are also associated with them. The optimal technique of analgosedation should provide good analgesia and sedation, fast recovery, comfort for the endoscopist, patients' safety, and will have financial benefits. The future will show whether these new drugs have succeeded in these goals.
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Hou Y, Zhang L, Chen X, Wang Y, Jiang T, Qi Q, Zhang C, Shi C. Study on brain function of the frontal lobe in patients with functional gastroduodenal disease by near-infrared functional imaging. Biomed Pharmacother 2023; 158:114182. [PMID: 36916402 DOI: 10.1016/j.biopha.2022.114182] [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: 11/08/2022] [Revised: 12/27/2022] [Accepted: 12/28/2022] [Indexed: 01/03/2023] Open
Abstract
OBJECTIVE functional gastroduodenal disease is the main type of functional gastrointestinal disease in the clinical department of Gastroenterology and psychosomatic medicine at present, which accounts for a large proportion of outpatients in gastroenterology. The main manifestations are epigastric pain, dyspepsia, belching, chronic nausea, and vomiting. The purpose of this study is to explore the changes in brain function in patients with functional gastroduodenal diseases through experiments to reveal the possible central etiology and development process. METHODS the functional changes of the prefrontal lobe in patients with functional gastroduodenal diseases and normal controls were detected and analyzed by near-infrared brain imaging. At the same time, SCL-90 was used to evaluate the mental health status of patients with functional gastroduodenal diseases and normal controls. The changes in the autonomic nerve system in patients and normal controls were detected and compared by heart rate variability trend chart. RESULTS the activity of left prefrontal lobe areas s8-d8, s10-d4, s10-d10 and s10-d15 in patients with functional gastroduodenal disease was significantly lower than normal controls (p < 0.05). The SCL-90 scale showed that there were significant differences between patients with functional gastroduodenal disease and normal controls, especially in depression, compulsion, anxiety, somatization, interpersonal sensitivity and hostility (p < 0.05). There was no significant difference in lf/hf values detected by the HRV trend chart (p > 0.05). CONCLUSION the function of the left frontal lobe is decreased in patients with functional gastroduodenal disease. The autonomic nervous system may be related to the connection system between the brain center and internal organs.
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Affiliation(s)
- Yanhong Hou
- Department of medical psychology and Department of Gastroenterology, the eighth medical center of the General Hospital of the Chinese people's Liberation Army, Beijing 100091, China
| | - Lin Zhang
- Department of medical psychology and Department of Gastroenterology, the eighth medical center of the General Hospital of the Chinese people's Liberation Army, Beijing 100091, China.
| | - Xiaofei Chen
- Department of medical psychology and Department of Gastroenterology, the eighth medical center of the General Hospital of the Chinese people's Liberation Army, Beijing 100091, China
| | - Yujing Wang
- Department of medical psychology and Department of Gastroenterology, the eighth medical center of the General Hospital of the Chinese people's Liberation Army, Beijing 100091, China
| | - Tong Jiang
- Department of medical psychology and Department of Gastroenterology, the eighth medical center of the General Hospital of the Chinese people's Liberation Army, Beijing 100091, China
| | - Qinjiazi Qi
- Department of medical psychology and Department of Gastroenterology, the eighth medical center of the General Hospital of the Chinese people's Liberation Army, Beijing 100091, China
| | - Chuanxiao Zhang
- Department of medical psychology and Department of Gastroenterology, the eighth medical center of the General Hospital of the Chinese people's Liberation Army, Beijing 100091, China
| | - Chao Shi
- Department of medical psychology and Department of Gastroenterology, the eighth medical center of the General Hospital of the Chinese people's Liberation Army, Beijing 100091, China
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Positano V, Meloni A, Santarelli MF, Pistoia L, Spasiano A, Cuccia L, Casini T, Gamberini MR, Allò M, Bitti PP, Pepe A, Cademartiri F. Deep Learning Staging of Liver Iron Content From Multiecho MR Images. J Magn Reson Imaging 2023; 57:472-484. [PMID: 35713339 DOI: 10.1002/jmri.28300] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 05/27/2022] [Accepted: 05/31/2022] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND MRI represents the most established liver iron content (LIC) evaluation approach by estimation of liver T2* value, but it is dependent on the choice of the measurement region and the software used for image analysis. PURPOSE To develop a deep-learning method for unsupervised classification of LIC from magnitude T2* multiecho MR images. STUDY TYPE Retrospective. POPULATION/SUBJECTS A total of 1069 thalassemia major patients enrolled in the core laboratory of the Myocardial Iron Overload in Thalassemia (MIOT) network, which were included in the training (80%) and test (20%) sets. Twenty patients from different MRI vendors included in the external test set. FIELD STRENGTH/SEQUENCE A5 T, T2* multiecho magnitude images. ASSESSMENT Four deep-learning convolutional neural networks (HippoNet-2D, HippoNet-3D, HippoNet-LSTM, and an ensemble network HippoNet-Ensemble) were used to achieve unsupervised staging of LIC using five classes (normal, borderline, middle, moderate, severe). The training set was employed to construct the deep-learning model. The performance of the LIC staging model was evaluated in the test set and in the external test set. The model's performances were assessed by evaluating the accuracy, sensitivity, and specificity with respect to the ground truth labels obtained by T2* measurements and by comparison with operator-induced variability originating from different region of interest (ROI) placements. STATISTICAL TESTS The network's performances were evaluated by single-class accuracy, specificity, and sensitivity and compared by one-way repeated measures analysis of variance (ANOVA) and one-way ANOVA. RESULTS HippoNet-Ensemble reached an accuracy significantly higher than the other networks, and a sensitivity and specificity higher than HippoNet-LSTM. Accuracy, sensitivity, and specificity values for the LIC stages were: normal: 0.96/0.93/0.97, borderline: 0.95/0.85/0.98, mild: 0.96/0.88/0.98, moderate: 0.95/0.89/0.97, severe: 0.97/0.95/0.98. Correctly staging of cases was in the range of 85%-95%, depending on the LIC class. Multiclass accuracy was 0.90 against 0.92 for the interobserver variability. DATA CONCLUSION The proposed HippoNet-Ensemble network can perform unsupervised LIC staging and achieves good prognostic performance. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Vincenzo Positano
- Department of Radiology, Fondazione G. Monasterio CNR-Regione Toscana, Pisa, Italy.,U.O.C. Bioingegneria, Fondazione G. Monasterio CNR-Regione Toscana, Pisa, Italy
| | - Antonella Meloni
- Department of Radiology, Fondazione G. Monasterio CNR-Regione Toscana, Pisa, Italy.,U.O.C. Bioingegneria, Fondazione G. Monasterio CNR-Regione Toscana, Pisa, Italy
| | | | - Laura Pistoia
- Department of Radiology, Fondazione G. Monasterio CNR-Regione Toscana, Pisa, Italy
| | - Anna Spasiano
- Unità Operativa Semplice Dipartimentale Malattie Rare del Globulo Rosso, Azienda Ospedaliera di Rilievo Nazionale "A. Cardarelli", Napoli, Italy
| | - Liana Cuccia
- Unità Operativa Complessa Ematologia con Talassemia, ARNAS Civico "Benfratelli-Di Cristina", Palermo, Italy
| | - Tommaso Casini
- Centro Talassemie ed Emoglobinopatie, Ospedale "Meyer", Firenze, Italy
| | - Maria Rita Gamberini
- U. O. di Day Hospital della Talassemia e delle Emoglobinopatie. Dipartimento della Riproduzione e dell'Accrescimento, Azienda Ospedaliero-Universitaria S. Anna, Cona - Ferrara, Italy
| | - Massimo Allò
- Ematologia Microcitemia, Ospedale San Giovanni di Dio - ASP Crotone, Crotone, Italy
| | - Pier Paolo Bitti
- Servizio Immunoematologia e Medicina Trasfusionale - Dipartimento dei Servizi, Presidio Ospedaliero "San Francesco" ASL Nuoro, Nuoro, Italy
| | - Alessia Pepe
- Institute of Radiology, Department of Medicine, University of Padua, Padua, Italy
| | - Filippo Cademartiri
- Department of Radiology, Fondazione G. Monasterio CNR-Regione Toscana, Pisa, Italy
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The Use of Artificial Intelligence in the Diagnosis and Classification of Thyroid Nodules: An Update. Cancers (Basel) 2023; 15:cancers15030708. [PMID: 36765671 PMCID: PMC9913834 DOI: 10.3390/cancers15030708] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 01/20/2023] [Accepted: 01/20/2023] [Indexed: 01/27/2023] Open
Abstract
The incidence of thyroid nodules diagnosed is increasing every year, leading to a greater risk of unnecessary procedures being performed or wrong diagnoses being made. In our paper, we present the latest knowledge on the use of artificial intelligence in diagnosing and classifying thyroid nodules. We particularly focus on the usefulness of artificial intelligence in ultrasonography for the diagnosis and characterization of pathology, as these are the two most developed fields. In our search of the latest innovations, we reviewed only the latest publications of specific types published from 2018 to 2022. We analyzed 930 papers in total, from which we selected 33 that were the most relevant to the topic of our work. In conclusion, there is great scope for the use of artificial intelligence in future thyroid nodule classification and diagnosis. In addition to the most typical uses of artificial intelligence in cancer differentiation, we identified several other novel applications of artificial intelligence during our review.
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Stawinski PM, Dziadkowiec KN, Kuo LA, Echavarria J, Saligram S. Barrett's Esophagus: An Updated Review. Diagnostics (Basel) 2023; 13:diagnostics13020321. [PMID: 36673131 PMCID: PMC9858189 DOI: 10.3390/diagnostics13020321] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/05/2022] [Accepted: 11/09/2022] [Indexed: 01/18/2023] Open
Abstract
Barrett’s esophagus (BE) is a change in the distal esophageal mucosal lining, whereby metaplastic columnar epithelium replaces squamous epithelium of the esophagus. This change represents a pre-malignant mucosal transformation which has a known association with the development of esophageal adenocarcinoma. Gastroesophageal reflux disease is a risk factor for BE, other risk factors include patients who are Caucasian, age > 50 years, central obesity, tobacco use, history of peptic stricture and erosive gastritis. Screening for BE remains selective based on risk factors, a screening program in the general population is not routinely recommended. Diagnosis of BE is established with a combination of endoscopic recognition, targeted biopsies, and histologic confirmation of columnar metaplasia. We aim to provide a comprehensive review of the epidemiology, pathogenesis, screening and advanced techniques of detecting and eradicating Barrett’s esophagus.
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王 美, 朱 继, 李 莹, 铁 成, 王 士, 张 玮, 王 贵, 倪 晓. [Automatic anatomical site recognition of laryngoscopic images using convolutional neural network]. LIN CHUANG ER BI YAN HOU TOU JING WAI KE ZA ZHI = JOURNAL OF CLINICAL OTORHINOLARYNGOLOGY, HEAD, AND NECK SURGERY 2023; 37:6-12. [PMID: 36597361 PMCID: PMC10128350 DOI: 10.13201/j.issn.2096-7993.2023.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Indexed: 01/05/2023]
Abstract
Objective:To explore the automatic recognition and classification of 20 anatomical sites in laryngoscopy by an artificial intelligence(AI) quality control system using convolutional neural network(CNN). Methods: Laryngoscopic image data archived from laryngoscopy examinations at the Department of Endoscopy, Cancer Hospital, Chinese Academy of Medical Sciences from January to December 2018 were collected retrospectively, and a CNN model was constructed using Inception-ResNet-V2+SENet. Using 14000 electronic laryngoscope images as the training set, these images were classified into 20 specific anatomical sites including the whole head and neck, and their performance was tested by 2000 laryngoscope images and 10 laryngoscope videos. Results:The average time of the trained CNN model for recognition of each laryngoscopic image was(20.59 ± 1.55) ms, and the overall accuracy of recognition of 20 anatomical sites in laryngoscopic images was 97.75%(1955/2000), with average sensitivity, specificity, positive predictive value, and negative predictive value of 100%, 99.88%, 97.76%, and 99.88%, respectively. The model had an accuracy of ≥ 99% for the identification of 20 anatomical sites in laryngoscopic videos. Conclusion:This study confirms that the CNN-based AI system can perform accurate and fast classification and identification of anatomical sites in laryngoscopic pictures and videos, which can be used for quality control of photo documentation in laryngoscopy and shows potential application in monitoring the performance of laryngoscopy.
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Affiliation(s)
- 美玲 王
- 国家癌症中心 国家恶性肿瘤临床医学研究中心 中国医学科学院北京协和医学院肿瘤医院深圳医院内镜科(深圳,518116)Department of Endoscopy, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China
| | - 继庆 朱
- 国家癌症中心 国家恶性肿瘤临床医学研究中心 中国医学科学院北京协和医学院肿瘤医院内镜科Department of Endoscopy, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing
| | - 莹 李
- 国家癌症中心 国家恶性肿瘤临床医学研究中心 中国医学科学院北京协和医学院肿瘤医院深圳医院内镜科(深圳,518116)Department of Endoscopy, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China
| | - 成炜 铁
- 国家癌症中心 国家恶性肿瘤临床医学研究中心 中国医学科学院北京协和医学院肿瘤医院内镜科Department of Endoscopy, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing
| | - 士旭 王
- 国家癌症中心 国家恶性肿瘤临床医学研究中心 中国医学科学院北京协和医学院肿瘤医院内镜科Department of Endoscopy, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing
| | - 玮 张
- 国家癌症中心 国家恶性肿瘤临床医学研究中心 中国医学科学院北京协和医学院肿瘤医院深圳医院内镜科(深圳,518116)Department of Endoscopy, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China
| | - 贵齐 王
- 国家癌症中心 国家恶性肿瘤临床医学研究中心 中国医学科学院北京协和医学院肿瘤医院内镜科Department of Endoscopy, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing
| | - 晓光 倪
- 国家癌症中心 国家恶性肿瘤临床医学研究中心 中国医学科学院北京协和医学院肿瘤医院内镜科Department of Endoscopy, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing
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Ribolsi M, Andrisani G, Di Matteo FM, Cicala M. Achalasia, from diagnosis to treatment. Expert Rev Gastroenterol Hepatol 2023; 17:21-30. [PMID: 36588469 DOI: 10.1080/17474124.2022.2163236] [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] [Indexed: 01/03/2023]
Abstract
INTRODUCTION Achalasia is an uncommon esophageal motility disorder and is characterized by alterations of the motility of the esophageal body in conjunction with altered lower esophageal sphincter (LES) relaxation. The clinical presentation of patients with achalasia may be complex; however, the most frequent symptom is dysphagia. The management of patients with achalasia is often challenging, due to the heterogeneous clinical presentation. AREAS COVERED The diagnosis and management of achalasia has significantly improved in the last years due to the growing availability of high-resolution manometry (HRM) and the implementation in the therapeutic armamentarium of new therapeutic endoscopic procedures. Traditional therapeutic strategies include botulinum toxin injected to the LES and pneumatic balloon dilation. On the other hand, surgical treatments contemplate laparoscopic Heller myotomy and, less frequently, esophagectomy. Furthermore, in the last few years, per oral endoscopic myotomy (POEM) has been proposed as the main endoscopic therapeutic alternative to the laparoscopic Heller myotomy. EXPERT OPINION Diagnosis and treatment of achalasia still represent a challenging area. However, we believe that an accurate up-front evaluation is, nowadays, necessary in addressing patients with achalasia for a more accurate diagnosis as well as for the best treatment options.
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Affiliation(s)
- Mentore Ribolsi
- Unit of Gastroenterology and Digestive Endoscopy, Campus Bio Medico University, Rome, Italy
| | | | | | - Michele Cicala
- Unit of Gastroenterology and Digestive Endoscopy, Campus Bio Medico University, Rome, Italy
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Galati JS, Duve RJ, O'Mara M, Gross SA. Artificial intelligence in gastroenterology: A narrative review. Artif Intell Gastroenterol 2022; 3:117-141. [DOI: 10.35712/aig.v3.i5.117] [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: 10/09/2022] [Revised: 11/21/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Artificial intelligence (AI) is a complex concept, broadly defined in medicine as the development of computer systems to perform tasks that require human intelligence. It has the capacity to revolutionize medicine by increasing efficiency, expediting data and image analysis and identifying patterns, trends and associations in large datasets. Within gastroenterology, recent research efforts have focused on using AI in esophagogastroduodenoscopy, wireless capsule endoscopy (WCE) and colonoscopy to assist in diagnosis, disease monitoring, lesion detection and therapeutic intervention. The main objective of this narrative review is to provide a comprehensive overview of the research being performed within gastroenterology on AI in esophagogastroduodenoscopy, WCE and colonoscopy.
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Affiliation(s)
- Jonathan S Galati
- Department of Medicine, NYU Langone Health, New York, NY 10016, United States
| | - Robert J Duve
- Department of Internal Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, United States
| | - Matthew O'Mara
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
| | - Seth A Gross
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
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Zacharakis G, Almasoud A. Using of artificial intelligence: Current and future applications in colorectal cancer screening. World J Gastroenterol 2022; 28:2778-2781. [PMID: 35979167 PMCID: PMC9260867 DOI: 10.3748/wjg.v28.i24.2778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/26/2022] [Accepted: 06/13/2022] [Indexed: 02/06/2023] Open
Abstract
Significant developments in colorectal cancer screening are underway and include new screening guidelines that incorporate considerations for patients aged 45 years, with unique features and new techniques at the forefront of screening. One of these new techniques is artificial intelligence which can increase adenoma detection rate and reduce the prevalence of colonic neoplasia.
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Affiliation(s)
- Georgios Zacharakis
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, Prince Sattam bin Abdulaziz University Hospital, Al Kharj 16277, Saudi Arabia
| | - Abdulaziz Almasoud
- Department of Gastroenterology and Hepatology, Prince Sultan Military Medical City, Riyadh 12233, Saudi Arabia
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Yoo BS, Houston KV, D'Souza SM, Elmahdi A, Davis I, Vilela A, Parekh PJ, Johnson DA. Advances and horizons for artificial intelligence of endoscopic screening and surveillance of gastric and esophageal disease. Artif Intell Med Imaging 2022; 3:70-86. [DOI: 10.35711/aimi.v3.i3.70] [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: 01/28/2022] [Revised: 05/18/2022] [Accepted: 06/20/2022] [Indexed: 02/06/2023] Open
Abstract
The development of artificial intelligence in endoscopic assessment of the gastrointestinal tract has shown progressive enhancement in diagnostic acuity. This review discusses the expanding applications for gastric and esophageal diseases. The gastric section covers the utility of AI in detecting and characterizing gastric polyps and further explores prevention, detection, and classification of gastric cancer. The esophageal discussion highlights applications for use in screening and surveillance in Barrett's esophagus and in high-risk conditions for esophageal squamous cell carcinoma. Additionally, these discussions highlight applications for use in assessing eosinophilic esophagitis and future potential in assessing esophageal microbiome changes.
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Affiliation(s)
- Byung Soo Yoo
- Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Kevin V Houston
- Department of Internal Medicine, Virginia Commonwealth University, Richmond, VA 23298, United States
| | - Steve M D'Souza
- Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Alsiddig Elmahdi
- Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Isaac Davis
- Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Ana Vilela
- Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Parth J Parekh
- Division of Gastroenterology, Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - David A Johnson
- Division of Gastroenterology, Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
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Brenner AR, Laoveeravat P, Carey PJ, Joiner D, Mardini SH, Jovani M. Artificial intelligence using advanced imaging techniques and cholangiocarcinoma: Recent advances and future direction. Artif Intell Gastroenterol 2022; 3:88-95. [DOI: 10.35712/aig.v3.i3.88] [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: 03/07/2022] [Revised: 04/16/2022] [Accepted: 05/08/2022] [Indexed: 02/06/2023] Open
Abstract
While cholangiocarcinoma represents only about 3% of all gastrointestinal tumors, it has a dismal survival rate, usually because it is diagnosed at a late stage. The utilization of Artificial Intelligence (AI) in medicine in general, and in gastroenterology has made gigantic steps. However, the application of AI for biliary disease, in particular for cholangiocarcinoma, has been sub-optimal. The use of AI in combination with clinical data, cross-sectional imaging (computed tomography, magnetic resonance imaging) and endoscopy (endoscopic ultrasound and cholangioscopy) has the potential to significantly improve early diagnosis and the choice of optimal therapeutic options, leading to a transformation in the prognosis of this feared disease. In this review we summarize the current knowledge on the use of AI for the diagnosis and management of cholangiocarcinoma and point to future directions in the field.
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Affiliation(s)
- Aaron R Brenner
- Department of Internal Medicine, University of Kentucky College of Medicine, Lexington, KY 40536, United States
| | - Passisd Laoveeravat
- Division of Digestive Diseases and Nutrition, University of Kentucky College of Medicine, Lexington, KY 40536, United States
| | - Patrick J Carey
- Department of Internal Medicine, University of Kentucky College of Medicine, Lexington, KY 40536, United States
| | - Danielle Joiner
- Department of Internal Medicine, University of Kentucky College of Medicine, Lexington, KY 40536, United States
| | - Samuel H Mardini
- Division of Digestive Diseases and Nutrition, University of Kentucky College of Medicine, Lexington, KENTUCKY 40536, United States
| | - Manol Jovani
- Digestive Diseases and Nutrition, University of Kentucky Albert B. Chandler Hospital, Lexington, KY 40536, United States
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Bjørsum-Meyer T, Koulaouzidis A, Baatrup G. Comment on “Artificial intelligence in gastroenterology: A state-of-the-art review”. World J Gastroenterol 2022; 28:1722-1724. [PMID: 35581959 PMCID: PMC9048781 DOI: 10.3748/wjg.v28.i16.1722] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 01/07/2022] [Accepted: 03/16/2022] [Indexed: 02/06/2023] Open
Abstract
Colon capsule endoscopy (CCE) was introduced nearly two decades ago. Initially, it was limited by poor image quality and short battery time, but due to technical improvements, it has become an equal diagnostic alternative to optical colonoscopy (OC). Hastened by the coronavirus disease 2019 pandemic, CCE has been introduced in clinical practice to relieve overburdened endoscopy units and move investigations to out-patient clinics. A wider adoption of CCE would be bolstered by positive patient experience, as it offers a diagnostic investigation that is not inferior to other modalities. The shortcomings of CCE include its inability to differentiate adenomatous polyps from hyperplastic polyps. Solving this issue would improve the stratification of patients for polyp removal. Artificial intelligence (AI) has shown promising results in polyp detection and characterization to minimize incomplete CCEs and avoid needless examinations. Onboard AI appears to be a needed application to enable near-real-time decision-making in order to diminish patient waiting times and avoid superfluous subsequent OCs. With this letter, we discuss the potential and role of AI in CCE as a diagnostic tool for the large bowel.
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Affiliation(s)
- Thomas Bjørsum-Meyer
- Department of Surgery, Odense University Hospital, Svendborg 5700, Denmark
- Department of Clinical Research, University of Southern Denmark, Faculty of Health Science, Odense 5230, Denmark
| | - Anastasios Koulaouzidis
- Department of Clinical Research, University of Southern Denmark, Faculty of Health Science, Odense 5230, Denmark
| | - Gunnar Baatrup
- Department of Surgery, Odense University Hospital, Svendborg 5700, Denmark
- Department of Clinical Research, University of Southern Denmark, Faculty of Health Science, Odense 5230, Denmark
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Zhao PY, Han K, Yao RQ, Ren C, Du XH. Application Status and Prospects of Artificial Intelligence in Peptic Ulcers. Front Surg 2022; 9:894775. [PMID: 35784921 PMCID: PMC9244632 DOI: 10.3389/fsurg.2022.894775] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 05/31/2022] [Indexed: 02/05/2023] Open
Abstract
Peptic ulcer (PU) is a common and frequently occurring disease. Although PU seriously threatens the lives and health of global residents, the applications of artificial intelligence (AI) have strongly promoted diversification and modernization in the diagnosis and treatment of PU. This minireview elaborates on the research progress of AI in the field of PU, from PU's pathogenic factor Helicobacter pylori (Hp) infection, diagnosis and differential diagnosis, to its management and complications (bleeding, obstruction, perforation and canceration). Finally, the challenges and prospects of AI application in PU are prospected and expounded. With the in-depth understanding of modern medical technology, AI remains a promising option in the management of PU patients and plays a more indispensable role. How to realize the robustness, versatility and diversity of multifunctional AI systems in PU and conduct multicenter prospective clinical research as soon as possible are the top priorities in the future.
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Affiliation(s)
- Peng-yue Zhao
- Department of General Surgery, First Medical Center of the Chinese PLA General Hospital, Beijing, China
| | - Ke Han
- Department of Gastroenterology, First Medical Center of the Chinese PLA General Hospital, Beijing, China
| | - Ren-qi Yao
- Translational Medicine Research Center, Medical Innovation Research Division and Fourth Medical Center of the Chinese PLA General Hospital, Beijing, China
- Correspondence: Xiao-hui Du Chao Ren Ren-qi Yao
| | - Chao Ren
- Department of Pulmonary and Critical Care Medicine, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
- Correspondence: Xiao-hui Du Chao Ren Ren-qi Yao
| | - Xiao-hui Du
- Department of General Surgery, First Medical Center of the Chinese PLA General Hospital, Beijing, China
- Correspondence: Xiao-hui Du Chao Ren Ren-qi Yao
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Popa SL, Ismaiel A. Artificial intelligence applications in gastroenterology: steps ahead. Med Pharm Rep 2021; 94:S56-S59. [PMID: 38912404 PMCID: PMC11188025 DOI: 10.15386/mpr-2513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024] Open
Abstract
Artificial intelligence (AI) applications are used in gastroenterology for automatic imaging diagnostic methods such as ultrasonography, computer tomography, magnetic resonance imaging, but also in endoscopy, capsule endoscopy and biopsy followed by automatic digital pathology evaluation. The accuracy of AI-based systems is superior to human expertise. Furthermore, in reality, a very small percentage of the patients are being investigated by a human expert in endoscopy, so implementing AI in this investigation would only increase the diagnostic accuracy. The existence of an unimaginable number of digital images and different types of medical information made possible the analysis and training of convolutional neural network (CNN), which consists of multilayers of artificial neural networks (ANN) with step-by-step minimal processing, creating a fundamental resource for any AI-based system to learn by itself how to automatically perform medical tasks, which were performed only by human experts in the past. The main objectives for AI applications used in gastroenterology are to improve the medical procedures with enhanced precision, to reduce the number of medical errors and to perform repetitive tasks.
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Affiliation(s)
- Stefan L Popa
- 2 Medical Department, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Abdulrahman Ismaiel
- 2 Medical Department, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
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