Development of birth weight estimation model for Ethiopian population from sonographic evaluation

BACKGROUND

Fetal birth weight (FBW) estimation involves predicting the weight of a fetus prior to delivery. This prediction serves as a crucial input for ensuring effective, accurate, and appropriate obstetric planning, management, and decision-making. Typically, there are two methods used to estimate FBW: the clinical method (which involves measuring fundal height and performing abdominal palpation) or sonographic evaluation. The accuracy of clinical method estimation relies heavily on the experience of the clinician. Sonographic evaluation involves utilizing various mathematical models to estimate FBW, primarily relying on fetal biometry. However, these models often demonstrate estimation errors that exceed acceptable levels, which can result in inadequate labor and delivery management planning. One source of this estimation error is sociodemographic variations between population groups in different countries. Additionally, inter- and intra-observer variability during fetal biometry measurement also contributes to errors in FBW estimation.

METHODS

In this research, a novel mathematical model was proposed through multiple regression analysis to predict FBW with an accepted level of estimation error. To develop the model, population data consisting of fetal biometry, fetal ultrasound images, obstetric variables, and maternal sociodemographic factors (age, marital status, ethnicity, educational status, occupational status, income, etc.) of the mother were collected. Two approaches were used to develop the mathematical model. The first method was based on fetal biometry data measured by a physician and the second used fetal biometry data measured using an image processing algorithm. The image processing algorithm comprises preprocessing, segmentation, feature extraction, and fetal biometry measurement.

RESULTS

The model developed using the two approaches were tested to assess their performance in estimating FBW, and they achieved mean percentage errors of 7.53% and 5.89%, respectively. Based on these results, the second model was chosen as the final model.

CONCLUSION

The findings indicate that the developed model can estimate FBW with an acceptable level of error for the Ethiopian population. Furthermore, this model outperforms existing models for FBW estimation. The proposed approach has the potential to reduce infant and maternal mortality rates by providing accurate fetal birth weight estimates for informed obstetric planning.

Automated volumetric determination of high R2 * regions in substantia nigra: A feasibility study of quantifying substantia nigra atrophy in progressive supranuclear palsy

The establishment of an unbiased protocol for the automated volumetric measurement of iron-rich regions in the substantia nigra (SN) is clinically important for diagnosing neurodegenerative diseases exhibiting midbrain atrophy, such as progressive supranuclear palsy (PSP). This study aimed to automatically quantify the volume and surface properties of the iron-rich 3D regions in the SN using the quantitative MRI-R₂ ^* map. Three hundred and sixty-seven slices of R₂ ^* map and susceptibility-weighted imaging (SWI) at 3-T MRI from healthy control (HC) individuals and Parkinson's disease (PD) patients were used to train customized U-net++ convolutional neural network based on expert-segmented masks. Age- and sex-matched participants were selected from HC, PD, and PSP groups to automate the volumetric determination of iron-rich areas in the SN. Dice similarity coefficient values between expert-segmented and detected masks from the proposed network were 0.91 ± 0.07 for R₂ ^* maps and 0.89 ± 0.08 for SWI. Reductions in iron-rich SN volume from the R₂ ^* map (SWI) were observed in PSP with area under the receiver operating characteristic curve values of 0.96 (0.89) and 0.98 (0.92) compared with HC and PD, respectively. The mean curvature of the PSP showed SN deformation along the side closer to the red nucleus. We demonstrated the automated volumetric measurement of iron-rich regions in the SN using deep learning can quantify the SN atrophy in PSP compared with PD and HC.

Classification of Parkinson’s Disease Using EMG Signals from Different Upper Limb Movements Based on Multiclass Support Vector Machine

Parkinson’s disease (PD) is the second most common neurodegenerative disease that affects a wide range of productive individuals worldwide. The common approach to diagnose PD is through clinical assessment of the patient, which is highly subjective and time consuming. Electromyography (EMG) can be taken as a cheap way of PD diagnosis. However, highly experienced experts are required to interpret the signals. The manual procedures are complex, time-consuming, and prone to error resulting in misdiagnosis. In this research, an automatic system for detection and classification of PD stages using EMG signals acquired from different upper limb movements is proposed. In addition, effective upper limb movement for the identification of PD has been investigated. The data required for training and testing the system was collected from flexor carpi radialis and biceps brachii muscles of 15 PD patients and 10 healthy control subjects at Jimma University Medical Center. The raw EMG signal was preprocessed and frequency and time-domain features were extracted. A multiclass support vector machine model was then trained for four-class classification (normal, early, moderate, and advanced PD levels). The performance of the system was evaluated using different performance evaluators and a promising result has been obtained. 90%, 91.7%, 95%, and 96.6% overall classification accuracies were obtained for elbow flexion by 90-degrees without load, elbow flexion by 90-degrees with load, touching the shoulder, and wrist pronation, respectively. A user-friendly interface has been also developed for ease of use of the automatic PD classification system.