Ultrasounds have become somewhat of a staple in the process of prenatal care, offering a fascinating glimpse into the unborn world. For expectant parents, it’s a cherished first “meeting” with their child. For clinicians, these monochrome, two-dimensional images give invaluable insight into fetal development, such as identifying the baby’s sex or detecting potential abnormalities like heart defects or a cleft lip.
Wenn die Ärzte einen genaueren Blick benötigen, greifen sie manchmal auf die Magnetresonanztomographie (MRT) zurück. MRTs verwenden Magnetfelder, um detaillierte, geschichtete Bilder zu erzeugen, die zu einer 3D-Ansicht des Fötus zusammengefügt werden können. Obwohl die Interpretation dieser detaillierten 3D-MRT-Scans kompliziert sein kann, da unser natürliches visuelles System nicht in der Lage ist, komplexe volumetrische Daten zu verarbeiten, kann das maschinelle Lernen jetzt helfen.
Introducing “Fetal SMPL,” a new machine learning model designed by a cooperative team from the Computer Science and Artificial Intelligence Laboratory at MIT, Boston Children’s Hospital, and Harvard Medical School. This innovative model brings a new level precision to the process by creating more accurate 3-dimensional representations of fetal health through the modeling of their shape and movements.
Fetal SMPL is a derivative of SMPL, otherwise known as the Skinned Multi-Person Linear model – a 3D modeling framework initially developed for adult body shapes and poses. The researchers trained their fetal edition on over 20,000 MRI volumes; it learned to anticipate the size and position of fetuses, creating nearly sculpture-like 3D representations. Each model includes a sophisticated system of 23 interconnected joints, accurately mirroring the motion of the fetus.
Let’s talk precision. The predictions made by Fetal SMPL were off by an average of an almost unbelievable 3.1 millimeters, a size smaller than a grain of rice. This extraordinary level of detail empowers clinicians to take measurements of vital anatomical features such as the size of the head or abdomen, and subsequently compare these results to the standardizations for healthy development at particular gestational ages.
Um das System zu testen, verglich das Forschungsteam Fetal SMPL mit SMIL, einem anderen Modell, das zur Dokumentation des Wachstums von Säuglingen entwickelt wurde. Selbst nach den für einen fairen Vergleich notwendigen Anpassungen - das SMIL-Modell wurde um 75% verkleinert, um der Größe eines Fötus zu entsprechen - setzte sich Fetal SMPL gegen SMIL durch.
The accuracy of Fetal SMPL isn’t its only strength—it proved to be efficient too. The model reached a reliable alignment with the MRI data in a mere three iterations, demonstrating its strong performance.
Right now, Fetal SMPL is concentrating on the fetus’s exterior shape and skeletal structure, which is a substantial step forward in itself. However, this is just the beginning. The team aims to improve the model further by including the fetus’s internal structure — organs and muscles — that can contribute to monitoring critical entities such as lung and liver development. Should these plans materialize, it would revolutionize the model into a comprehensive volumetric representation, providing an even more profound insight into fetal health.
Fetal SMPL not only promises to enhance prenatal diagnostics but also potentially deepen our understanding of fetal evolution. It’s compatible with existing models for adults and infants, laying a robust foundation for extensive studies on human development. The implications of these advancements, needless to say, are significant and lead the way to potentially life-changing discoveries and innovations.
Die fetale SMPL befindet sich zwar noch im Anfangsstadium, könnte aber bei weiterer Erprobung und Verfeinerung zu einem integralen Bestandteil der Schwangerenvorsorge werden und sowohl Klinikern als auch werdenden Eltern ein klareres und umfassenderes Bild des Lebens vermitteln, das im wahrsten Sinne des Wortes im Entstehen ist.
Weitere Informationen über das Projekt und seine möglichen Auswirkungen finden Sie auf der Website Originalartikel von MIT News hier.
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