Automatic Probe Movement Guidance for Freehand Obstetric Ultrasound

Abstract

We present the first system that provides real-time probe movement guidance for acquiring standard planes in routine freehand obstetric ultrasound scanning. Such a system can contribute to the world-wide deployment of obstetric ultrasound scanning by lowering the required level of operator expertise. The system employs an artificial neural network that receives the ultrasound video signal and the motion signal of an inertial measurement unit (IMU) that is attached to the probe, and predicts a guidance signal. The network termed US-GuideNet predicts either the movement towards the standard plane position (goal prediction), or the next movement that an expert sonographer would perform (action prediction). While existing models for other ultrasound applications are trained with simulations or phantoms, we train our model with real-world ultrasound video and probe motion data from 464 routine clinical scans by 17 accredited sonographers. Evaluations for 3 standard plane types show that the model provides a useful guidance signal with an accuracy of 88.8 % for goal prediction and 90.9 % for action prediction.

The Use of Artificial Intelligence in Automation in the Fields of Gynaecology and Obstetrics – an Assessment of the State of Play

“…This analysis allows systems to predict which transducer movements are most likely to result in the creation of precise target planes in abnormality diagnostics 79 . The same research group were able to demonstrate, on the one hand, that their initial AI models were able to automatically recognise video content (sectional planes) and add appropriate captions and, on the other hand, that specially trained CNNs were able to evaluate combined data from a motion sensor and an ultrasound probe, converting them into signals to augment correct transducer guidance 80 , 81 .…”

“…Welches Potenzial neuronale Netze besitzen, zeigen auch aktuelle Daten einer britischen Arbeitsgruppe zur KI-basierten 2-D-Videoanalyse des Workflows erfahrener Untersucher, die Vorhersagen für die wahrscheinlichsten Schallkopfbewegungen zur Erstellung exakter Zielebenen im Rahmen der Fehlbildungsdiagnostik erlauben 79 . Initiale KI-Modelle der gleichen Arbeitsgruppe konnten zum einen zeigen, dass sie in der Lage waren, Videoinhalte (Schnittebenen) automatisiert zu erkennen und mit entsprechenden Untertiteln zu versehen, und zum anderen konnten speziell trainierte CNN kombinierte Daten eines Bewegungssensors und einer US-Sonde auswerten und in Signale zur augmentierten korrekten Schallkopfführung umwandeln 80 , 81 .…”

Image-Guided Navigation of a Robotic Ultrasound Probe for Autonomous Spinal Sonography Using a Shadow-aware Dual-Agent Framework

“…In order to develop more generic methods, a number of machine learning-based solutions have been proposed to learn US probe guidance in a data-driven manner. Some researchers used imitation learning (IL) algorithms to learn 3-DOF guidance of the probe orientation from expert demonstrations in fetal US imaging [27]. However, the ILbased methods usually rely on complete and accurate demonstrations, which may be intractable or expensive to obtain [28].…”

Image quality assessment for closed-loop computer-assisted lung ultrasound

“…When image quality is insufficient, users are instructed to repeat the acquisition at this location on the patient, following application-specific protocols. Although considered out of scope for this work, such a system may prompt users to repeat predefined protocols, for example making use of external assistance such as teleguidance or automated action suggestions [11,12]. The diagnosis assistance module uses images determined to be of sufficient quality to perform predefined tasks, such as inferring the likelihood of specific conditions.…”