Therapeutische Konsequenzen und Verläufe

Graf, Reinhard; Baumgartner, Florian; Lercher, Kurt

doi:10.1007/978-3-642-22034-0_10

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“…SMOPP is a nationwide cohort of unselected newborns from 27 Mongolian hospitals, coordinated at the National Center for Maternal and Child Health (NCMCH), a tertiary pediatric referral center in Ulaanbaatar 2 . The hips of all newborns were examined with GE ultrasound devices, operating with a 7–10 MHz linear array transducer following Graf’s suggestion of using transducers with a frequency of at least 5 MHz 15 . The examination was performed at a median age of one day and DDH was immediately treated with Tübingen flexion-abduction orthoses (Otto Bock HealthCare Deutschland GmbH, Duderstadt, Germany) and followed up with monthly ultrasound until two-sided mature hips were achieved.…”

Section: Methodsmentioning

confidence: 99%

Accuracy of Trained Physicians is Inferior to Deep Learning-Based Algorithm for Determining Angles in Ultrasound of the Newborn Hip

et al. 2020

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Purpose Sonographic diagnosis of developmental dysplasia of the hip allows treatment with a flexion-abduction orthosis preventing hip luxation. Accurate determination of alpha and beta angles according to Graf is crucial for correct diagnosis. It is unclear if algorithms could predict the angles. We aimed to compare the accuracy for users and automation reporting root mean squared errors (RMSE). Materials and Methods We used 303 306 ultrasound images of newborn hips collected between 2009 and 2016 in screening consultations. Trained physicians labelled every second image with alpha and beta angles during the consultations. A random subset of images was labeled with time and precision under lab conditions as ground truth. Automation predicted the two angles using a convolutional neural network (CNN). The analysis was focused on the alpha angle. Results Three methods were implemented, each with a different abstraction of the problem: (1) CNNs that directly learn the angles without any post-processing steps; (2) CNNs that return the relevant landmarks in the image to identify the angles; (3) CNNs that return the base line, bony roof line, and the cartilage roof line which are necessary to calculate the angles. The RMSE between physicians and ground truth were found to be 7.1° for alpha. The best CNN architecture was (2) landmark detection. The RMSE between landmark detection and ground truth was 3.9° for alpha. Conclusion The accuracy of physicians in their daily routine is inferior to deep learning-based algorithms for determining angles in ultrasound of the newborn hip. Similar methods could be used to support physicians.

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Section: Methodsmentioning

confidence: 99%