assessing-accuracy-fitness-smartwatch-data-for-cardiovascular

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health

This study aims to validate the accuracy of low-cost fitness smartwatches by comparing their data with gold-standard measurements for cardiovascular and ...

Universiti Malaya

The objective of the study is to investigate the effect of Nuchal Fold (NF) in predicting Fetal Growth Restriction (FGR) using machine learning (ML), to explain the model's results using model-agnostic interpretable techniques, and to compare the results with clinical guidelines. This study used second-trimester ultrasound biometry and Doppler velocimetry were used to construct six FGR (birthweight < 3rd centile) ML models. Interpretability analysis was conducted using Accumulated Local Effects (ALE) and Shapley Additive Explanations (SHAP). The results were compared with clinical guidelines based on the most optimal model. Support Vector Machine (SVM) exhibited the most consistent performance in FGR prediction. SHAP showed that the top contributors to identify FGR were Abdominal Circumference (AC), NF, Uterine RI (Ut RI), and Uterine PI (Ut PI). ALE showed that the cutoff values of Ut RI, Ut PI, and AC in differentiating FGR from normal were comparable with clinical guidelines (Errors between model and clinical; Ut RI: 15%, Ut PI: 8%, and AC: 11%). The cutoff value for NF to differentiate between healthy and FGR is 5.4 mm, where low NF may indicate FGR. The SVM model is the most stable in FGR prediction. ALE can be a potential tool to identify a cutoff value for novel parameters to differentiate between healthy and FGR.

Interpreting the role of nuchal fold for fetal growth restriction prediction using machine learning.

The idea of using artificial intelligence (AI) in medical practice has gained vast interest due to its potential to revolutionise healthcare systems. However, only some AI algorithms are utilised due to systems' uncertainties, besides the never-ending list of ethical and legal concerns. This paper intends to provide an overview of current AI challenges in medical imaging with an ultimate aim to foster better and effective communication among various stakeholders to encourage AI technology development. We identify four main challenges in implementing AI in medical imaging, supported with consequences and past events when these problems fail to mitigate. Among them is the creation of a robust AI algorithm that is fair, trustable and transparent. Another issue is on data governance, in which best practices in data sharing must be established to promote trust and protect the patients' privacy. Next, stakeholders, such as the government, technology companies and hospital management, should come to a consensus in creating trustworthy AI policies and regulatory frameworks, which is the fourth challenge, to support, encourage and spur innovation in digital AI healthcare technology. Lastly, we discussed the efforts of various organizations such as the World Health Organisation (WHO), American College of Radiology (ACR), European Society of Radiology (ESR) and Radiological Society of North America (RSNA), who are already actively pursuing ethical developments in AI. The efforts by various stakeholders will eventually overcome hurdles and the deployment of AI-driven healthcare applications in clinical practice will become a reality and hence lead to better healthcare services and outcomes.

Current challenges of implementing artificial intelligence in medical imaging.

To construct a national fetal growth chart using retrospective data and compared its diagnostic accuracy in predicting SGA at birth with existing international growth charts.

The accuracy of international and national fetal growth charts in detecting small-for-gestational-age infants using the Lambda-Mu-Sigma method.

The inherent "black box" nature of AI algorithms presents a substantial barrier to the widespread adoption of the technology in clinical settings, leading to a lack of trust among users. This review begins by examining the foundational stages involved in the interpretation of medical images by radiologists and clinicians, encompassing both type 1 (fast thinking - ability of the brain to think and act intuitively) and type 2 (slow analytical - slow analytical, laborious approach to decision-making) decision-making processes. The discussion then delves into current Explainable AI (XAI) approaches, exploring both inherent and post-hoc explainability for medical imaging applications and highlighting the milestones achieved. XAI in medicine refers to AI system designed to provide transparent, interpretable, and understandable reasoning behind AI predictions or decisions. Additionally, the paper showcases some commercial AI medical systems that offer explanations through features such as heatmaps. Opportunities, challenges and potential avenues for advancing the field are also addressed. In conclusion, the review observes that state-of-the-art XAI methods are not mature enough for implementation, as the explanations they provide are challenging for medical experts to comprehend. Deeper understanding of the cognitive mechanisms by medical professionals is important in aiming to develop more interpretable XAI methods.

Current status and future directions of explainable artificial intelligence in medical imaging.

The first objective is to develop a nuchal thickness reference chart. The second objective is to compare rule-based algorithms and machine learning models in predicting small-for-gestational-age infants.

Article	Year
Interpreting the role of nuchal fold for fetal growth restriction prediction using machine learning. Scientific reports\| PubMed ID: 35273269	2022
Current challenges of implementing artificial intelligence in medical imaging. Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)\| PubMed ID: 35714523	2022
The accuracy of international and national fetal growth charts in detecting small-for-gestational-age infants using the Lambda-Mu-Sigma method. Frontiers in surgery\| PubMed ID: 37114151	2023
Current status and future directions of explainable artificial intelligence in medical imaging. European journal of radiology\| PubMed ID: 39667118	2025
Enhancing Small-for-Gestational-Age Prediction: Multi-Country Validation of Nuchal Thickness, Estimated Fetal Weight, and Machine Learning Models. Prenatal diagnosis\| PubMed ID: 39817730	2025

Shier Nee Saw

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