Pedro Cardoso, Miguel Mascarenhas, João Afonso, Tiago Ribeiro, Francisco Mendes, Miguel Martins, Patrícia Andrade, Hélder Cardoso, Miguel Mascarenhas Saraiva, João P S Ferreira, Guilherme Macedo
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Recent advances in artificial intelligence (AI) have made it possible to automatically select the most relevant frames in CE.</p><p><strong>Objectives: </strong>In this proof-of-concept study, our objective was to develop an automated scoring system using CE images to objectively grade inflammation.</p><p><strong>Design: </strong>Pan-enteric CE videos (PillCam Crohn's) performed in CD patients between 09/2020 and 01/2023 were retrospectively reviewed and LS, CECDAI, and ELIAKIM scores were calculated.</p><p><strong>Methods: </strong>We developed a convolutional neural network-based automated score consisting of the percentage of positive frames selected by the algorithm (for small bowel and colon separately). We correlated clinical data and the validated scores with the artificial intelligence-generated score (AIS).</p><p><strong>Results: </strong>A total of 61 patients were included. The median LS was 225 (0-6006), CECDAI was 6 (0-33), ELIAKIM was 4 (0-38), and SB_AIS was 0.5659 (0-29.45). We found a strong correlation between SB_AIS and LS, CECDAI, and ELIAKIM scores (Spearman's <i>r</i> = 0.751, <i>r</i> = 0.707, <i>r</i> = 0.655, <i>p</i> = 0.001). We found a strong correlation between LS and ELIAKIM (<i>r</i> = 0.768, <i>p</i> = 0.001) and a very strong correlation between CECDAI and LS (<i>r</i> = 0.854, <i>p</i> = 0.001) and CECDAI and ELIAKIM scores (<i>r</i> = 0.827, <i>p</i> = 0.001).</p><p><strong>Conclusion: </strong>Our study showed that the AI-generated score had a strong correlation with validated scores indicating that it could serve as an objective and efficient method for evaluating inflammation in CD patients. As a preliminary study, our findings provide a promising basis for future refining of a CE score that may accurately correlate with prognostic factors and aid in the management and treatment of CD patients.</p>","PeriodicalId":48770,"journal":{"name":"Therapeutic Advances in Gastroenterology","volume":null,"pages":null},"PeriodicalIF":3.9000,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11135072/pdf/","citationCount":"0","resultStr":"{\"title\":\"Deep learning and minimally invasive inflammatory activity assessment: a proof-of-concept study for development and score correlation of a panendoscopy convolutional network.\",\"authors\":\"Pedro Cardoso, Miguel Mascarenhas, João Afonso, Tiago Ribeiro, Francisco Mendes, Miguel Martins, Patrícia Andrade, Hélder Cardoso, Miguel Mascarenhas Saraiva, João P S Ferreira, Guilherme Macedo\",\"doi\":\"10.1177/17562848241251569\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Capsule endoscopy (CE) is a valuable tool for assessing inflammation in patients with Crohn's disease (CD). 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引用次数: 0
摘要
背景：胶囊内镜（CE）是评估克罗恩病（CD）患者炎症的重要工具。目前评估炎症的标准是经过验证的评分（和临床实验室值），如刘易斯评分（LS）、胶囊内镜克罗恩病活动指数（CECDAI）和ELIAKIM。人工智能（AI）的最新进展使得在 CE 中自动选择最相关的帧成为可能：在这项概念验证研究中，我们的目标是开发一种自动评分系统，利用 CE 图像对炎症进行客观分级：设计：回顾性审查了 2020 年 9 月至 2023 年 1 月期间对 CD 患者进行的泛肠道 CE 视频（PillCam Crohn's），并计算了 LS、CECDAI 和 ELIAKIM 分数：我们开发了一种基于卷积神经网络的自动评分，该评分由算法（分别针对小肠和结肠）选择的阳性框架百分比组成。我们将临床数据和经过验证的评分与人工智能生成的评分（AIS）进行了关联：结果：共纳入 61 名患者。LS中位数为225（0-6006），CECDAI中位数为6（0-33），ELIAKIM中位数为4（0-38），SB_AIS中位数为0.5659（0-29.45）。我们发现 SB_AIS 与 LS、CECDAI 和 ELIAKIM 分数之间存在很强的相关性（Spearman's r = 0.751，r = 0.707，r = 0.655，p = 0.001）。我们发现，LS 和 ELIAKIM 之间存在很强的相关性（r = 0.768，p = 0.001），CECDAI 和 LS 之间存在很强的相关性（r = 0.854，p = 0.001），CECDAI 和 ELIAKIM 分数之间存在很强的相关性（r = 0.827，p = 0.001）：我们的研究表明，AI 生成的评分与有效评分有很强的相关性，这表明它可以作为评估 CD 患者炎症的一种客观有效的方法。作为一项初步研究，我们的发现为今后完善 CE 评分提供了一个很好的基础，该评分可能与预后因素准确相关，有助于 CD 患者的管理和治疗。
Deep learning and minimally invasive inflammatory activity assessment: a proof-of-concept study for development and score correlation of a panendoscopy convolutional network.
Background: Capsule endoscopy (CE) is a valuable tool for assessing inflammation in patients with Crohn's disease (CD). The current standard for evaluating inflammation are validated scores (and clinical laboratory values) like Lewis score (LS), Capsule Endoscopy Crohn's Disease Activity Index (CECDAI), and ELIAKIM. Recent advances in artificial intelligence (AI) have made it possible to automatically select the most relevant frames in CE.
Objectives: In this proof-of-concept study, our objective was to develop an automated scoring system using CE images to objectively grade inflammation.
Design: Pan-enteric CE videos (PillCam Crohn's) performed in CD patients between 09/2020 and 01/2023 were retrospectively reviewed and LS, CECDAI, and ELIAKIM scores were calculated.
Methods: We developed a convolutional neural network-based automated score consisting of the percentage of positive frames selected by the algorithm (for small bowel and colon separately). We correlated clinical data and the validated scores with the artificial intelligence-generated score (AIS).
Results: A total of 61 patients were included. The median LS was 225 (0-6006), CECDAI was 6 (0-33), ELIAKIM was 4 (0-38), and SB_AIS was 0.5659 (0-29.45). We found a strong correlation between SB_AIS and LS, CECDAI, and ELIAKIM scores (Spearman's r = 0.751, r = 0.707, r = 0.655, p = 0.001). We found a strong correlation between LS and ELIAKIM (r = 0.768, p = 0.001) and a very strong correlation between CECDAI and LS (r = 0.854, p = 0.001) and CECDAI and ELIAKIM scores (r = 0.827, p = 0.001).
Conclusion: Our study showed that the AI-generated score had a strong correlation with validated scores indicating that it could serve as an objective and efficient method for evaluating inflammation in CD patients. As a preliminary study, our findings provide a promising basis for future refining of a CE score that may accurately correlate with prognostic factors and aid in the management and treatment of CD patients.
期刊介绍：
Therapeutic Advances in Gastroenterology is an open access journal which delivers the highest quality peer-reviewed original research articles, reviews, and scholarly comment on pioneering efforts and innovative studies in the medical treatment of gastrointestinal and hepatic disorders. The journal has a strong clinical and pharmacological focus and is aimed at an international audience of clinicians and researchers in gastroenterology and related disciplines, providing an online forum for rapid dissemination of recent research and perspectives in this area.
The editors welcome original research articles across all areas of gastroenterology and hepatology.
The journal publishes original research articles and review articles primarily. Original research manuscripts may include laboratory, animal or human/clinical studies – all phases. Letters to the Editor and Case Reports will also be considered.
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