Dataset Information

Artificial intelligence-assisted endoscopic detection of esophageal neoplasia in early stage: The next step?

ABSTRACT: Esophageal cancer (EC) is a common malignant tumor of the digestive tract and originates from the epithelium of the esophageal mucosa. It has been confirmed that early EC lesions can be cured by endoscopic therapy, and the curative effect is equivalent to that of surgical operation. Upper gastrointestinal endoscopy is still the gold standard for EC diagnosis. The accuracy of endoscopic examination results largely depends on the professional level of the examiner. Artificial intelligence (AI) has been applied in the screening of early EC and has shown advantages; notably, it is more accurate than less-experienced endoscopists. This paper reviews the application of AI in the field of endoscopic detection of early EC, including squamous cell carcinoma and adenocarcinoma, and describes the relevant progress. Although up to now most of the studies evaluating the clinical application of AI in early EC endoscopic detection are focused on still images, AI-assisted real-time detection based on live-stream video may be the next step.

SUBMITTER: Liu Y

PROVIDER: S-EPMC8047537 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Artificial intelligence-assisted endoscopic detection of esophageal neoplasia in early stage: The next step?

Liu Yong Y

World journal of gastroenterology 20210401 14

Esophageal cancer (EC) is a common malignant tumor of the digestive tract and originates from the epithelium of the esophageal mucosa. It has been confirmed that early EC lesions can be cured by endoscopic therapy, and the curative effect is equivalent to that of surgical operation. Upper gastrointestinal endoscopy is still the gold standard for EC diagnosis. The accuracy of endoscopic examination results largely depends on the professional level of the examiner. Artificial intelligence (AI) has ...[more]

PMID: 33911463

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Project description:BackgroundThe use of artificial intelligence (AI) assisted white light imaging (WLI) detection systems for superficial esophageal squamous cell carcinoma (SESCC) is limited by training with images from one specific endoscopy platform.MethodsIn this study, we developed an AI system with a convolutional neural network (CNN) model using WLI images from Olympus and Fujifilm endoscopy platforms. The training dataset consisted of 5892 WLI images from 1283 patients, and the validation dataset included 4529 images from 1224 patients. We assessed the diagnostic performance of the AI system and compared it with that of endoscopists. We analyzed the system's ability to identify cancerous imaging characteristics and investigated the efficacy of the AI system as an assistant in diagnosis.ResultsIn the internal validation set, the AI system's per-image analysis had a sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of 96.64%, 95.35%, 91.75%, 90.91%, and 98.33%, respectively. In patient-based analysis, these values were 90.17%, 94.34%, 88.38%, 89.50%, and 94.72%, respectively. The diagnostic results in the external validation set were also favorable. The CNN model's diagnostic performance in recognizing cancerous imaging characteristics was comparable to that of expert endoscopists and significantly higher than that of mid-level and junior endoscopists. This model was competent in localizing SESCC lesions. Manual diagnostic performances were significantly improved with the assistance by AI system, especially in terms of accuracy (75.12% vs. 84.95%, p = 0.008), specificity (63.29% vs. 76.59%, p = 0.017) and PPV (64.95% vs. 75.23%, p = 0.006).ConclusionsThe results of this study demonstrate that the developed AI system is highly effective in automatically recognizing SESCC, displaying impressive diagnostic performance, and exhibiting strong generalizability. Furthermore, when used as an assistant in the diagnosis process, the system improved manual diagnostic performance.

Project description:BackgroundNasopharyngeal carcinoma (NPC) is a common malignancy in southern China, and often underdiagnosed due to reliance on physician expertise. Artificial intelligence (AI) can enhance diagnostic accuracy and efficiency using large datasets and advanced algorithms.MethodsNasal endoscopy videos with white light imaging (WLI) and narrow-band imaging (NBI) modes from 707 patients treated at one center in China from June 2020 to December 2022 were prospectively collected. A total of 8816 frames were obtained through standardized data procedures. Nasopharyngeal Carcinoma Diagnosis Segmentation Network Framework (NPC-SDNet) was developed and internally tested based on these frames. Two hundred frames were randomly selected to compare the diagnostic performance between NPC-SDNet and rhinologists. Two external testing sets with 2818 images from other hospitals validated the robustness and generalizability of the model. This study was registered at clinicaltrials.gov (NCT04547673).FindingsThe diagnostic accuracy, precision, recall, and specificity of NPC-SDNet using WLI were 95.0% (95% CI: 94.1%-96.2%), 93.5% (95% CI: 90.2%-95.2%), 97.2% (95% CI: 96.2%-98.3%), and 93.5% (95% CI: 91.7%-94.0%), respectively, and using NBI were 95.8% (95% CI: 94.0%-96,8%), 93.1% (95% CI: 91.0%-95.6%), 96.0% (95% CI: 95.7%-96.8%), and 97.2% (95% CI: 97.1%-97.4%), respectively. Segmentation performance was also robust, with mean Intersection over Union scores of 83.4% (95% CI: 81.8%-85.6%; NBI) and 83.7% (95% CI: 85.1%-90.1%; WLI). In head-to-head comparisons with rhinologists, NPC-SDNet achieved a diagnostic accuracy of 94.0% (95% CI: 91.5%-95.8%) and processed 1000 frames per minute, outperforming clinicians (68.9%-88.2%) across different expertise levels. External validation further supported the reliability of NPC-SDNet, with area under the receiver operating characteristic curve (AUC) values of 0.998 and 0.977 in NBI images, 0.977 and 0.970 in WLI images.InterpretationNPC-SDNet demonstrates excellent real-time diagnostic and segmentation accuracy, offering a promising tool for enhancing the precision of NPC diagnosis.FundingThis work was supported by National Key R&D Program of China (2020YFC1316903), the National Natural Science Foundation of China (NSFC) grants (81900918, 82020108009), Natural Science Foundation of Guangdong Province (2022A1515010002), Key-Area Research and Development of Guangdong Province (2023B1111040004, 2020B1111190001), and Key Clinical Technique of Guangzhou (2023P-ZD06).

Project description:AimsElectronic health records (EHR) linked to Digital Imaging and Communications in Medicine (DICOM), biological specimens, and deep learning (DL) algorithms could potentially improve patient care through automated case detection and surveillance. We hypothesized that by applying keyword searches to routinely stored EHR, in conjunction with AI-powered automated reading of DICOM echocardiography images and analysing biomarkers from routinely stored plasma samples, we were able to identify heart failure (HF) patients.Methods and resultsWe used EHR data between 1993 and 2021 from Tayside and Fife (~20% of the Scottish population). We implemented a keyword search strategy complemented by filtering based on International Classification of Diseases (ICD) codes and prescription data to EHR data set. We then applied DL for the automated interpretation of echocardiographic DICOM images. These methods were then integrated with the analysis of routinely stored plasma samples to identify and categorize patients into HF with reduced ejection fraction (HFrEF), HF with preserved ejection fraction (HFpEF), and controls without HF. The final diagnosis was verified through a manual review of medical records, measured natriuretic peptides in stored blood samples, and by comparing clinical outcomes among groups. In our study, we selected the patient cohort through an algorithmic workflow. This process started with 60 850 EHR data and resulted in a final cohort of 578 patients, divided into 186 controls, 236 with HFpEF, and 156 with HFrEF, after excluding individuals with mismatched data or significant valvular heart disease. The analysis of baseline characteristics revealed that compared with controls, patients with HFrEF and HFpEF were generally older, had higher BMI, and showed a greater prevalence of co-morbidities such as diabetes, COPD, and CKD. Echocardiographic analysis, enhanced by DL, provided high coverage, and detailed insights into cardiac function, showing significant differences in parameters such as left ventricular diameter, ejection fraction, and myocardial strain among the groups. Clinical outcomes highlighted a higher risk of hospitalization and mortality for HF patients compared with controls, with particularly elevated risk ratios for both HFrEF and HFpEF groups. The concordance between the algorithmic selection of patients and manual validation demonstrated high accuracy, supporting the effectiveness of our approach in identifying and classifying HF subtypes, which could significantly impact future HF diagnosis and management strategies.ConclusionsOur study highlights the feasibility of combining keyword searches in EHR, DL automated echocardiographic interpretation, and biobank resources to identify HF subtypes.

Project description:In the gastroenterology field, the impact of artificial intelligence was investigated for the purposes of diagnostics, risk stratification of patients, improvement in quality of endoscopic procedures and early detection of neoplastic diseases, implementation of the best treatment strategy, and optimization of patient prognosis. Computer-assisted diagnostic systems to evaluate upper endoscopy images have recently emerged as a supporting tool in endoscopy due to the risks of misdiagnosis related to standard endoscopy and different expertise levels of endoscopists, time-consuming procedures, lack of availability of advanced procedures, increasing workloads, and development of endoscopic mass screening programs. Recent research has tended toward computerized, automatic, and real-time detection of lesions, which are approaches that offer utility in daily practice. Despite promising results, certain studies might overexaggerate the diagnostic accuracy of artificial systems, and several limitations remain to be overcome in the future. Therefore, additional multicenter randomized trials and the development of existent database platforms are needed to certify clinical implementation. This paper presents an overview of the literature and the current knowledge of the usefulness of different types of machine learning systems in the assessment of premalignant and malignant esophageal lesions via conventional and advanced endoscopic procedures. This study makes a presentation of the artificial intelligence terminology and refers also to the most prominent recent research on computer-assisted diagnosis of neoplasia on Barrett's esophagus and early esophageal squamous cell carcinoma, and prediction of invasion depth in esophageal neoplasms. Furthermore, this review highlights the main directions of future doctor-computer collaborations in which machines are expected to improve the quality of medical action and routine clinical workflow, thus reducing the burden on physicians.

Dataset Information

Artificial intelligence-assisted endoscopic detection of esophageal neoplasia in early stage: The next step?

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Artificial intelligence-assisted endoscopic detection of esophageal neoplasia in early stage: The next step?

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