A machine learning technology to improve the risk of non-invasive prenatal tests

BACKGROUND: Pedodontists and general practitioners may need support in planning the early orthodontic treatment of patients with mixed dentition, especially in borderline cases. The use of machine learning algorithms is required to be able to consistently make treatment decisions for such cases. OBJECTIVE: This study aimed to use machine learning algorithms to facilitate the process of deciding whether to choose serial extraction or expansion of maxillary and mandibular dental arches for early treatment of borderline patients suffering from moderate to severe crowding. METHODS: The dataset of 116 patients who were previously treated by senior orthodontists and divided into two groups according to their treatment modalities were examined. Machine Learning algorithms including Multilayer Perceptron, Linear Logistic Regression, k-nearest Neighbors, Naïve Bayes, and Random Forest were trained on this dataset. Several metrics were used for the evaluation of accuracy, precision, recall, and kappa statistic. RESULTS: The most important 12 features were determined with the feature selection algorithm. While all algorithms achieved over 90% accuracy, Random Forest yielded 95% accuracy, with high reliability values (kappa = 0.90). CONCLUSION: The employment of machine learning methods for the treatment decision with or without extraction in the early treatment of patients in the mixed dentition can be particularly useful for pedodontists and general practitioners.

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Predicting mixed venous oxygen saturation (SvO2) impairment in COPD patients using clinical-CT radiomics data: A preliminary study

An,

Liu,

et al. 2024

THC

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BACKGROUND: Chronic obstructive pulmonary disease (COPD) is one of the most common chronic airway diseases in the world. OBJECTIVE: To predict the degree of mixed venous oxygen saturation (SvO2) impairment in patients with COPD by modeling using clinical-CT radiomics data and to provide reference for clinical decision-making. METHODS: A total of 236 patients with COPD diagnosed by CT and clinical data at Xiangyang No. 1 People’s Hospital (n= 157) and Xiangyang Central Hospital (n= 79) from June 2018 to September 2021 were retrospectively analyzed. The patients were divided into group A (SvO⩾2 62%, N= 107) and group B (SvO<2 62%, N= 129). We set up training set and test set at a ratio of 7/3 and time cutoff spot; In training set, Logistic regression was conducted to analyze the differences in general data (e.g. height, weight, systolic blood pressure), laboratory indicators (e.g. arterial oxygen saturation and pulmonary artery systolic pressure), and CT radiomics (radscore generated using chest CT texture parameters from 3D slicer software and LASSO regression) between these two groups. Further the risk factors screened by the above method were used to establish models for predicting the degree of hypoxia in COPD, conduct verification in test set and create a nomogram. RESULTS: Univariate analysis demonstrated that age, smoking history, drinking history, systemic systolic pressure, digestive symptoms, right ventricular diameter (RV), mean systolic pulmonary artery pressure (sPAP), cardiac index (CI), pulmonary vascular resistance (PVR), 6-min walking distance (6MWD), WHO functional classification of pulmonary hypertension (WHOPHFC), the ratio of forced expiratory volume in the first second to the forced vital capacity (FEV1%), and radscore in group B were all significantly different from those in group A (P< 0.05). Multivariate regression demonstrated that age, smoking history, digestive symptoms, 6MWD, and radscore were independent risk factors for SvO2 impairment. The combined model established based on the abovementioned indicators exhibited a good prediction effect [AUC: 0.903; 95%CI (0.858–0.937)], higher than the general clinical model [AUC: 0.760; 95%CI (0.701–0.813), P< 0.05] and laboratory examination-radiomics model [AUC: 0.868; 95%CI (0.818–0.908), P= 0.012]. The newly created nomogram may be helpful for clinical decision-making and benefit COPD patients. CONCLUSION: SvO2 is an important indicator of hypoxia in COPD, and it is highly related to age, 6MWD, and radscore. The combined model is helpful for early identification of SvO2 impairment and adjustment of COPD treatment strategies.

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A machine learning technology to improve the risk of non-invasive prenatal tests

Cited by 3 publications

References 28 publications

Applying machine learning in screening for Down Syndrome in both trimesters for diverse healthcare scenarios

Applying machine learning in screening for Down Syndrome in both trimesters for diverse healthcare scenarios

Machine learning based orthodontic treatment planning for mixed dentition borderline cases suffering from moderate to severe crowding: An experimental research study

Predicting mixed venous oxygen saturation (SvO2) impairment in COPD patients using clinical-CT radiomics data: A preliminary study

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