Automated craniofacial biometry with 3D T2w fetal MRI DOI Creative Commons
Jacqueline Matthew, Alena Uus, Alexia Egloff

et al.

PLOS Digital Health, Journal Year: 2024, Volume and Issue: 3(12), P. e0000663 - e0000663

Published: Dec. 30, 2024

Evaluating craniofacial phenotype-genotype correlations prenatally is increasingly important; however, it subjective and challenging with 3D ultrasound. We developed an automated label propagation pipeline using motion- corrected, slice-to-volume reconstructed (SVR) fetal MRI for measurements. A literature review expert consensus identified 31 biometrics MRI. An atlas defined anatomical landmarks served as a template subject registration, auto-labelling, biometric calculation. assessed 108 healthy controls 24 fetuses Down syndrome (T21) in the third trimester (29-36 weeks gestational age, GA) to identify meaningful T21. Reliability reproducibility were evaluated 10 random datasets by four observers. Automated labels produced all 132 subjects 0.3% placement error rate. Seven measurements, including anterior base of skull length maxillary length, showed significant differences large effect sizes between T21 control groups (ANOVA, p<0.001). Manual measurements took 25-35 minutes per case, while extraction approximately 5 minutes. Bland-Altman plots agreement within manual observer ranges except mandibular width, which had higher variability. Extended GA growth charts (19-39 weeks), based on 280 fetuses, future research. This first atlas-based protocol SVR biometrics, accurately revealing morphological cohort. Future work should focus improving measurement reliability, larger clinical cohorts, technical advancements, enhance prenatal care phenotypic characterisation.

Language: Английский

Recent advances and applications of artificial intelligence in 3D bioprinting DOI
Hongyi Chen, Bin Zhang, Jie Huang

et al.

Biophysics Reviews, Journal Year: 2024, Volume and Issue: 5(3)

Published: July 19, 2024

3D bioprinting techniques enable the precise deposition of living cells, biomaterials, and biomolecules, emerging as a promising approach for engineering functional tissues organs. Meanwhile, recent advances in researchers to build vitro models with finely controlled complex micro-architecture drug screening disease modeling. Recently, artificial intelligence (AI) has been applied different stages bioprinting, including medical image reconstruction, bioink selection, printing process, both classical AI machine learning approaches. The ability handle datasets, make computations, learn from past experiences, optimize processes dynamically makes it an invaluable tool advancing bioprinting. review highlights current integration discusses future approaches harness synergistic capabilities developing personalized

Language: Английский

Citations

9
Advances and Challenges in 3D Bioprinted Cancer Models: Opportunities for Personalized Medicine and Tissue Engineering DOI Open Access
Sai Liu, Pan Jin

Polymers, Journal Year: 2025, Volume and Issue: 17(7), P. 948 - 948

Published: March 31, 2025

Cancer is the second leading cause of death worldwide, after cardiovascular disease, claiming not only a staggering number lives but also causing considerable health and economic devastation, particularly in less-developed countries. Therapeutic interventions are impeded by differences patient-to-patient responses to anti-cancer drugs. A personalized medicine approach crucial for treating specific patient groups includes using molecular genetic screens find appropriate stratifications patients who will respond (and those not) treatment regimens. However, information on which risk stratification method can be used hone cancer types likely responders agent remains elusive most cancers. Novel developments 3D bioprinting technology have been widely applied recreate relevant bioengineered tumor organotypic structures capable mimicking human tissue microenvironment or adequate drug high-throughput screening settings. Parts autogenously printed form tissues computer-aided design concept where multiple layers include different cell compatible biomaterials build configurations. Patient-derived stromal cells, together with material, extracellular matrix proteins, growth factors, create bioprinted models that provide possible platform new therapies advance. Both natural synthetic biopolymers encourage cells biological materials models/implants. These may facilitate physiologically cell-cell cell-matrix interactions heterogeneity resembling real tumors.

Language: Английский

Citations

0
Automated Craniofacial Biometry with 3D T2w Fetal MRI DOI Creative Commons
Jacqueline Matthew, Alena Uus, Alexia Egloff

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 14, 2024

ABSTRACT Objectives Evaluating craniofacial phenotype-genotype correlations prenatally is increasingly important; however, it subjective and challenging with 3D ultrasound. We developed an automated landmark propagation pipeline using motion-corrected, slice-to-volume reconstructed (SVR) fetal MRI for measurements. Methods A literature review expert consensus identified 31 biometrics MRI. An atlas defined anatomical landmarks served as a template subject registration, auto-labelling, biometric calculation. assessed 108 healthy controls 24 fetuses Down syndrome (T21) in the third trimester (29-36 weeks gestational age, GA) to identify meaningful T21. Reliability reproducibility were evaluated 10 random datasets by four observers. Results Automated labels produced all 132 subjects 0.03% placement error rate. Seven measurements, including anterior base of skull length maxillary length, showed significant differences large effect sizes between T21 control groups (ANOVA, p<0.001). Manual measurements took 25-35 minutes per case, while extraction approximately 5 minutes. Bland-Altman plots agreement within manual observer ranges except mandibular width, which had higher variability. Extended GA growth charts (19-39 weeks), based on 280 fetuses, future research. Conclusion This first atlas-based protocol SVR biometrics, accurately revealing morphological cohort. Future work should focus improving measurement reliability, larger clinical cohorts, technical advancements, enhance prenatal care phenotypic characterisation.

Language: Английский

Citations

0
Automated craniofacial biometry with 3D T2w fetal MRI DOI Creative Commons
Jacqueline Matthew, Alena Uus, Alexia Egloff

et al.

PLOS Digital Health, Journal Year: 2024, Volume and Issue: 3(12), P. e0000663 - e0000663

Published: Dec. 30, 2024

Evaluating craniofacial phenotype-genotype correlations prenatally is increasingly important; however, it subjective and challenging with 3D ultrasound. We developed an automated label propagation pipeline using motion- corrected, slice-to-volume reconstructed (SVR) fetal MRI for measurements. A literature review expert consensus identified 31 biometrics MRI. An atlas defined anatomical landmarks served as a template subject registration, auto-labelling, biometric calculation. assessed 108 healthy controls 24 fetuses Down syndrome (T21) in the third trimester (29-36 weeks gestational age, GA) to identify meaningful T21. Reliability reproducibility were evaluated 10 random datasets by four observers. Automated labels produced all 132 subjects 0.3% placement error rate. Seven measurements, including anterior base of skull length maxillary length, showed significant differences large effect sizes between T21 control groups (ANOVA, p<0.001). Manual measurements took 25-35 minutes per case, while extraction approximately 5 minutes. Bland-Altman plots agreement within manual observer ranges except mandibular width, which had higher variability. Extended GA growth charts (19-39 weeks), based on 280 fetuses, future research. This first atlas-based protocol SVR biometrics, accurately revealing morphological cohort. Future work should focus improving measurement reliability, larger clinical cohorts, technical advancements, enhance prenatal care phenotypic characterisation.

Language: Английский

Citations

0