Automated Prediction of Infant Cognitive Development Risk by Video: A Pilot Study

Ji, Shengjie; Ma, Ding; Pan, Lunxin; Wang, Wen-An; Peng, Xin; Amos, Joan Toluwani; Ingabire, Honorine Niyigena; Li, Min; Wang, Ying; Yao, Dezhong; Ren, Pinyi

doi:10.1109/jbhi.2023.3266350

Cited by 7 publications

(1 citation statement)

References 58 publications

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“…For the purpose of determining the validity and reliability of the proposed framework, rigorous validation and evaluation are required. This can be accomplished by conducting comparison studies using current diagnostic techniques and utilizing huge datasets with a wide variety of aberrations [155]. The framework's performance is measured using performance metrics like sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC-ROC).…”

Section: Proposed Conceptual Frameworkmentioning

confidence: 99%

Ultrasound imaging based recognition of prenatal anomalies: a systematic clinical engineering review

Sriraam,

Chinta,

Suresh

et al. 2024

Prog. Biomed. Eng.

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For prenatal screening, ultrasound imaging allows for real-time observation of developing fetal anatomy. Understanding normal and aberrant forms through extensive fetal structural assessment enables for early detection and intervention. However, the reliability of anomaly diagnosis varies depending on operator expertise and device limits. First trimester scans in conjunction with circulating biochemical markers are critical in identifying high-risk pregnancies, but they also pose technical challenges. Recent engineering advancements in automated diagnosis, such as AI-based ultrasound image processing and multimodal data fusion, are developing to improve screening efficiency, accuracy, and consistency. Still, creating trust in these data-driven solutions is necessary for integration and acceptability in clinical settings. Transparency can be promoted by explainable AI (XAI) technologies that provide visual interpretations and illustrate the underlying diagnostic decision making process. An explanatory framework based on deeplearning is suggested to construct charts depicting anomaly screening results from ultrasound video feeds. AI modeling can then be applied to these charts to connect defects with probable deformations. Overall, engineering approaches that increaseimaging, automation, and interpretability hold enormous promise for altering traditional workflows and expanding diagnostic capabilities for better prenatal care.

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Section: Proposed Conceptual Frameworkmentioning

confidence: 99%