AI-Enhanced evaluation of YouTube content on post-surgical incontinence following pelvic cancer treatment

IF 3.6 2区医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Ssm-Population Health Pub Date : 2024-05-04 DOI:10.1016/j.ssmph.2024.101677

Alvaro Manuel Rodriguez-Rodriguez , Marta De la Fuente-Costa , Mario Escalera-de la Riva , Borja Perez-Dominguez , Gustavo Paseiro-Ares , Jose Casaña , Maria Blanco-Diaz

{"title":"AI-Enhanced evaluation of YouTube content on post-surgical incontinence following pelvic cancer treatment","authors":"Alvaro Manuel Rodriguez-Rodriguez , Marta De la Fuente-Costa , Mario Escalera-de la Riva , Borja Perez-Dominguez , Gustavo Paseiro-Ares , Jose Casaña , Maria Blanco-Diaz","doi":"10.1016/j.ssmph.2024.101677","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>Several pelvic area cancers exhibit high incidence rates, and their surgical treatment can result in adverse effects such as urinary and fecal incontinence, significantly impacting patients' quality of life. Post-surgery incontinence is a significant concern, with prevalence rates ranging from 25 to 45% for urinary incontinence and 9–68% for fecal incontinence. Cancer survivors are increasingly turning to YouTube as a platform to connect with others, yet caution is warranted as misinformation is prevalent.</p></div><div><h3>Objective</h3><p>This study aims to evaluate the information quality in YouTube videos about post-surgical incontinence after pelvic area cancer surgery.</p></div><div><h3>Methods</h3><p>A YouTube search for \"<em>Incontinence after cancer surgery</em>\" yielded 108 videos, which were subsequently analyzed. To evaluate these videos, several quality assessment tools were utilized, including DISCERN, GQS, JAMA, PEMAT, and MQ-VET. Statistical analyses, such as descriptive statistics and intercorrelation tests, were employed to assess various video attributes, including characteristics, popularity, educational value, quality, and reliability. Also, artificial intelligence techniques like PCA, t-SNE, and UMAP were used for data analysis. HeatMap and Hierarchical Clustering Dendrogram techniques validated the Machine Learning results.</p></div><div><h3>Results</h3><p>The quality scales presented a high level of correlation one with each other (<em>p</em> < 0.01) and the Artificial Intelligence-based techniques presented clear clustering representations of the dataset samples, which were reinforced by the Heat Map and Hierarchical Clustering Dendrogram.</p></div><div><h3>Conclusions</h3><p>YouTube videos on \"<em>Incontinence after Cancer Surgery</em>\" present a \"<em>High</em>\" quality across multiple scales. The use of AI tools, like PCA, t-SNE, and UMAP, is highlighted for clustering large health datasets, improving data visualization, pattern recognition, and complex healthcare analysis.</p></div>","PeriodicalId":47780,"journal":{"name":"Ssm-Population Health","volume":"26 ","pages":"Article 101677"},"PeriodicalIF":3.6000,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352827324000788/pdfft?md5=887685caf3578fea52fd95dedb4380e9&pid=1-s2.0-S2352827324000788-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ssm-Population Health","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352827324000788","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}

引用次数: 0

Abstract

Background

Several pelvic area cancers exhibit high incidence rates, and their surgical treatment can result in adverse effects such as urinary and fecal incontinence, significantly impacting patients' quality of life. Post-surgery incontinence is a significant concern, with prevalence rates ranging from 25 to 45% for urinary incontinence and 9–68% for fecal incontinence. Cancer survivors are increasingly turning to YouTube as a platform to connect with others, yet caution is warranted as misinformation is prevalent.

Objective

This study aims to evaluate the information quality in YouTube videos about post-surgical incontinence after pelvic area cancer surgery.

Methods

A YouTube search for "Incontinence after cancer surgery" yielded 108 videos, which were subsequently analyzed. To evaluate these videos, several quality assessment tools were utilized, including DISCERN, GQS, JAMA, PEMAT, and MQ-VET. Statistical analyses, such as descriptive statistics and intercorrelation tests, were employed to assess various video attributes, including characteristics, popularity, educational value, quality, and reliability. Also, artificial intelligence techniques like PCA, t-SNE, and UMAP were used for data analysis. HeatMap and Hierarchical Clustering Dendrogram techniques validated the Machine Learning results.

Results

The quality scales presented a high level of correlation one with each other (p < 0.01) and the Artificial Intelligence-based techniques presented clear clustering representations of the dataset samples, which were reinforced by the Heat Map and Hierarchical Clustering Dendrogram.

Conclusions

YouTube videos on "Incontinence after Cancer Surgery" present a "High" quality across multiple scales. The use of AI tools, like PCA, t-SNE, and UMAP, is highlighted for clustering large health datasets, improving data visualization, pattern recognition, and complex healthcare analysis.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

对 YouTube 上有关盆腔癌症治疗术后尿失禁的内容进行人工智能增强评估

背景几种盆腔部位癌症的发病率很高，手术治疗会导致尿失禁和大便失禁等不良后果，严重影响患者的生活质量。手术后尿失禁是一个重大问题，尿失禁发生率为 25% 至 45%，大便失禁发生率为 9% 至 68%。癌症幸存者越来越多地将YouTube作为与他人交流的平台，但由于错误信息普遍存在，因此需要谨慎对待。 Objective This study aims to evaluate the information quality in YouTube videos about post-surgical incontinence after pelvic area cancer surgery.Methods在YouTube上搜索 "癌症手术后尿失禁"，共搜索到108个视频，随后对这些视频进行了分析。为了评估这些视频，我们使用了几种质量评估工具，包括 DISCERN、GQS、JAMA、PEMAT 和 MQ-VET。统计分析，如描述性统计和相互关系测试，被用来评估视频的各种属性，包括特征、受欢迎程度、教育价值、质量和可靠性。此外，还使用了 PCA、t-SNE 和 UMAP 等人工智能技术进行数据分析。热图和分层聚类树枝图技术验证了机器学习的结果。结果质量量表之间呈现出高度的相关性（p <0.01），基于人工智能的技术对数据集样本进行了清晰的聚类表示，热图和分层聚类树枝图加强了这种相关性。结论YouTube 上关于 "癌症手术后尿失禁 "的视频在多个量表中呈现出 "高 "质量。在对大型健康数据集进行聚类、改进数据可视化、模式识别和复杂的医疗分析时，PCA、t-SNE 和 UMAP 等人工智能工具的使用得到了强调。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Ssm-Population Health PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-

CiteScore

6.50

自引率

2.10%

发文量

298

审稿时长

101 days

期刊介绍： SSM - Population Health. The new online only, open access, peer reviewed journal in all areas relating Social Science research to population health. SSM - Population Health shares the same Editors-in Chief and general approach to manuscripts as its sister journal, Social Science & Medicine. The journal takes a broad approach to the field especially welcoming interdisciplinary papers from across the Social Sciences and allied areas. SSM - Population Health offers an alternative outlet for work which might not be considered, or is classed as ''out of scope'' elsewhere, and prioritizes fast peer review and publication to the benefit of authors and readers. The journal welcomes all types of paper from traditional primary research articles, replication studies, short communications, methodological studies, instrument validation, opinion pieces, literature reviews, etc. SSM - Population Health also offers the opportunity to publish special issues or sections to reflect current interest and research in topical or developing areas. The journal fully supports authors wanting to present their research in an innovative fashion though the use of multimedia formats.