RESUMO
PURPOSE: Since it is fast, inexpensive and increasingly portable, ultrasound can be used for early detection of Developmental Dysplasia of the Hip (DDH) in infants at point-of-care. However, accurate interpretation\is highly dependent on scan quality. Poor-quality images lead to misdiagnosis, but inexperienced users may not even recognize the deficiencies in the images. Currently, users assess scan quality subjectively, based on image landmarks which are prone to human errors. Instead, we propose using Artificial Intelligence (AI) to automatically assess scan quality. METHODS: We trained separate Convolutional Neural Network (CNN) models to detect presence of each of four commonly used ultrasound landmarks in each hip image: straight horizontal iliac wing, labrum, os ischium and midportion of the femoral head. We used 100 3D ultrasound (3DUS) images for training and validated the technique on a set of 107 3DUS images also scored for landmarks by three non-expert readers and one expert radiologist. RESULTS: We got AI ≥ 85% accuracy for all four landmarks (ilium = 0.89, labrum = 0.94, os ischium = 0.85, femoral head = 0.98) as a binary classifier between adequate and inadequate scan quality. Our technique also showed excellent agreement with manual assessment in terms of Intraclass Correlation Coefficient (ICC) and Cohen's kappa coefficient (K) for ilium (ICC = 0.81, K = 0.56), os ischium (ICC = 0.89, K = 0.63) and femoral head (ICC = 0.83, K = 0.66), and moderate to good agreement for labrum (ICC = 0.65, K = 0.33). CONCLUSION: This new technique could ensure high scan quality and facilitate more widespread use of ultrasound in population screening of DDH.
RESUMO
When children walk on their toes for no known reason, the condition is called Idiopathic Toe Walking (ITW). Assessing the true severity of ITW can be difficult because children can alter their gait while under observation in clinic. The ability to monitor the foot angle during daily life outside of clinic may improve the assessment of ITW. A foot-worn, battery-powered inertial sensing device has been designed to monitor patients' foot angle during daily activities. The monitor includes a 3-axis accelerometer, 2-axis gyroscope, and a low-power microcontroller. The device is necessarily small, with limited battery capacity and processing power. Therefore a high-accuracy but low-complexity inertial sensing algorithm is needed. This paper compares several low-complexity algorithms' aptitude for foot-angle measurement: accelerometer-only measurement, finite impulse response (FIR) and infinite impulse response (IIR) complementary filtering, and a new dynamic predict-correct style algorithm developed using fuzzy c-means clustering. A total of 11 subjects each walked 20 m with the inertial sensing device fixed to one foot; 10 m with normal gait and 10 m simulating toe walking. A cross-validation scheme was used to obtain a low-bias estimate of each algorithm's angle measurement accuracy. The new predict-correct algorithm achieved the lowest angle measurement error: <5° mean error during normal and toe walking. The IIR complementary filtering algorithm achieved almost-as good accuracy with less computational complexity. These two algorithms seem to have good aptitude for the foot-angle measurement problem, and would be good candidates for use in a long-term monitoring device for toe-walking assessment.
