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基于人工智能的视频分析能否帮助评估贝利婴儿发展量表中各项的表现?

Can AI-Based Video Analysis Help Evaluate the Performance of the Items in the Bayley Scales of Infant Development?

作者信息

Ye Dong Hyun, Kim Tae Won, Kim Su Min, Seo Ji Won, Chang Jongyoon, Lee June-Goo, Ko Eun Jae

机构信息

Department of Rehabilitation Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea.

Department of Biomedical Engineering, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea.

出版信息

Children (Basel). 2025 Feb 25;12(3):276. doi: 10.3390/children12030276.

Abstract

To develop and evaluate a novel AI-based video analysis tool for the quantitative assessment of "Places Pegs in" and "Blue Board" tasks in the Bayley Scales of Infant Development (BSID-II). A prospective cohort study was conducted from February 2022 to December 2022, including children aged 12-42 months referred for suspected developmental delay. Participants were evaluated using the BSID-II, and their performances on the two tasks were video recorded and analyzed with the novel tool. Sensitivity and specificity were determined by comparing the tool's results to standard BSID-II assessments by therapists. Data collected included total time, number of trials, successful trials, and time and spatial intervals for each trial. Children were classified into typically developing (TD) (MDI ≥ 85) and developmental delay (DD) (MDI < 85) groups based on their mental developmental index (MDI). A total of 75 children participated in the study, and the mean values of MDI and PDI for the enrolled children were 88.9 ± 18.7 and 80.0 ± 16.7. The "Places Pegs in" had 86.5% sensitivity and 100% specificity; the "Blue Board" had 96.9% sensitivity and 89.5% specificity. Differences in cumulative successes over time were observed between age groups and TD and DD groups. The tool automatically calculated maximum successes at specific time points. The AI-based tool showed high predictive accuracy for BSID-II tasks in children aged 12-42 months, indicating potential utility for developmental assessments.

摘要

开发并评估一种基于人工智能的新型视频分析工具,用于定量评估贝利婴儿发育量表(BSID-II)中的“插钉子”和“蓝色木板”任务。2022年2月至2022年12月进行了一项前瞻性队列研究,纳入了因疑似发育迟缓而转诊的12至42个月大的儿童。使用BSID-II对参与者进行评估,并使用该新型工具对他们在这两项任务中的表现进行视频记录和分析。通过将该工具的结果与治疗师的标准BSID-II评估结果进行比较来确定敏感性和特异性。收集的数据包括总时间、试验次数、成功试验次数以及每次试验的时间和空间间隔。根据儿童的心理发育指数(MDI)将其分为正常发育(TD)组(MDI≥85)和发育迟缓(DD)组(MDI<85)。共有75名儿童参与了该研究,入选儿童的MDI和PDI平均值分别为88.9±18.7和80.0±16.7。“插钉子”任务的敏感性为86.5%,特异性为100%;“蓝色木板”任务的敏感性为96.9%,特异性为89.5%。观察到年龄组以及TD组和DD组之间随着时间推移累积成功次数的差异。该工具可自动计算特定时间点的最大成功次数。这种基于人工智能的工具对12至42个月大儿童的BSID-II任务显示出较高的预测准确性,表明其在发育评估中具有潜在用途。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6614/11941028/e6557ee40774/children-12-00276-g001.jpg

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