Assessing fetal lung maturity: Integration of ultrasound radiomics and deep learning

Wanming Chen, Baohui Zeng, Xiaoyan Ling, Chen Chen, Jichuang Lai, Jianru Lin, Xihong Liu, Huien Zhou, Xinmin Guo

Abstract

This study built a model to forecast the maturity of lungs by blending radiomics and deep learning methods. We examined ultrasound images from 263 pregnancies in the pregnancy stages. Utilizing the GE VOLUSON E8 system we captured images to extract and analyze radiomic features. These features were integrated with clinical data by means of deep learning algorithms such as DenseNet121 to enhance the accuracy of assessing fetal lung maturity. This combined model was validated by receiver operating characteristic (ROC) curve, calibration diagram, as well as decision curve analysis (DCA). We discovered that the accuracy and reliability of the diagnosis indicated that this method significantly improves the level of prediction of fetal lung maturity. This novel non-invasive diagnostic technology highlights the potential advantages of integrating diverse data sources to enhance prenatal care and infant health. The study lays groundwork, for validation and refinement of the model across various healthcare settings.

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Laube M and Thome UH. Y It Matters-Sex Differences in

Fetal Lung Development. Biomolecules. 2022; 12(3):

Liu D, Jiang Q, Xu Z, Li L and Lyu G. Evaluating fetal lung

development at various gestational weeks using two

dimensional shear wave elastography. Quant Imaging

Med Surg. 2024; 14(8): 5373-5384.

Wall J and Coates A. Prenatal imaging and postnatal

presentation,

diagnosis

and management of

congenital lung malformations. Curr Opin Pediatr.

; 26(3): 315-319.

Gordon MC, Narula K, O'Shaughnessy R and Barth WH, Jr.

Complications of third-trimester amniocentesis using

continuous ultrasound guidance. Obstet Gynecol.

; 99(2): 255-259.

Stark CM, Smith RS, Lagrandeur RM, Batton DG and Lorenz

RP. Need for urgent delivery after third-trimester

amniocentesis. Obstet Gynecol. 2000; 95(1): 48-50.

Zaffino P, Moccia S, De Momi E and Spadea MF. A Review

on Advances in Intra-operative Imaging for Surgery

and Therapy: Imagining the Operating Room of the

Future. Ann Biomed Eng. 2020; 48(8): 2171-2191.

Ahmed B and Konje JC. Fetal lung maturity assessment: A

historic perspective and Non - invasive assessment

using an automatic quantitative ultrasound analysis (a

potentially useful clinical tool). Eur J Obstet Gynecol

Reprod Biol. 2021; 258343-347.

Palacio M, Bonet-Carne E, Cobo T, Perez-Moreno A, Sabrià

J, Richter J, Kacerovsky M, Jacobsson B, García

Posada RA, Bugatto F, Santisteve R, Vives À, Parra

Cordero M, Hernandez-Andrade E, Bartha JL,

Carretero-Lucena P, Tan KL, Cruz-Martínez R,

Burke M, Vavilala S, Iruretagoyena I, Delgado JL,

Schenone M, Vilanova J, Botet F, Yeo GSH, Hyett J,

Deprest J, Romero R and Gratacos E. Prediction of

neonatal respiratory morbidity by quantitative

ultrasound lung texture analysis: a multicenter study.

Am J Obstet Gynecol. 2017; 217(2): 196.e191

e114.

Whitworth M, Bricker L and Mullan C. Ultrasound for fetal

assessment in early pregnancy. Cochrane Database

Syst Rev. 2015; 2015(7): Cd007058.

Dias T, Sairam S and Kumarasiri S. Ultrasound diagnosis

of fetal renal abnormalities. Best Pract Res Clin

Obstet Gynaecol. 2014; 28(3): 403-415.

Wataganara T, Ebrashy A, Aliyu LD, Moreira de Sa RA,

Pooh R, Kurjak A, Sen C, Adra A and Stanojevic M.

Fetal magnetic resonance imaging and ultrasound. J

Perinat Med. 2016; 44(5): 533-542.

Seyer Cagatan A, Taiwo Mustapha M, Bagkur C, Sanlidag

T and Ozsahin DU. An Alternative Diagnostic

Method for C. neoformans: Preliminary Results of

Deep-Learning Based Detection Model. Diagnostics

(Basel). 2022; 13(1):

Chen J, Huang Q, Chen Y, Qian L and Yu C-SJA.

Enhancing Nucleus Segmentation with HARU-Net:

A Hybrid Attention Based Residual U-Blocks

Network. 2023; abs/2308.03382

Jiang Y, Yang M, Wang S, Li X and Sun Y. Emerging role

of deep learning-based artificial intelligence in tumor

pathology. Cancer Commun (Lond). 2020; 40(4):

-166.

Esteva A, Robicquet A, Ramsundar B, Kuleshov V,

DePristo M, Chou K, Cui C, Corrado G, Thrun S and

Dean J. A guide to deep learning in healthcare. Nat

Med. 2019; 25(1): 24-29.

Shen D, Wu G and Suk HI. Deep Learning in Medical

Image Analysis. Annual review of biomedical

engineering. 2017; 19221-248.

LeCun Y, Bengio Y and Hinton G. Deep learning. Nature.

; 521(7553): 436-444.

Otter DW, Medina JR and Kalita JK. A Survey of the

Usages of Deep Learning for Natural Language

Processing. IEEE transactions on neural networks

and learning systems. 2021; 32(2): 604-624.

Zhou LQ, Wang JY, Yu SY, Wu GG, Wei Q, Deng YB, Wu

XL, Cui XW and Dietrich CF. Artificial intelligence

in medical imaging of the liver. World J

Gastroenterol. 2019; 25(6): 672-682.

Hosny A, Parmar C, Quackenbush J, Schwartz LH and

Aerts H. Artificial intelligence in radiology. Nat Rev

Cancer. 2018; 18(8): 500-510.

Chen Z, Liu Z, Du M and Wang Z. Artificial Intelligence in

Obstetric Ultrasound: An Update and Future

Applications. Front Med (Lausanne). 2021; 8733468.

Moreno-Espinosa AL, Hawkins-Villarreal A, Coronado

Gutierrez D, Burgos-Artizzu XP, Martínez-Portilla

RJ, Peña-Ramirez T, Gallo DM, Hansson SR,

Gratacòs E and Palacio M. Prediction of Neonatal

Respiratory Morbidity Assessed by Quantitative

Ultrasound Lung Texture Analysis in Twin

Pregnancies. J Clin Med. 2022; 11(16):

Chen P, Chen Y, Deng Y, Wang Y, He P, Lv X and Yu J.

A preliminary study to quantitatively evaluate the

development of maturation degree for fetal lung

based on transfer learning deep model from

ultrasound images. Int J Comput Assist Radiol Surg.

; 15(8): 1407-1415.

Keerthi G and Abirami MSJMTA. Intelligent diagnosis of

fetal organs abnormal growth in ultrasound images

using an ensemble CNN-TLFEM model. 2024;

-81178.

Fiorentino MC, Villani FP, Di Cosmo M, Frontoni E and

Moccia S. A review on deep-learning algorithms for

fetal ultrasound-image analysis. Medical image

analysis. 2023; 83102629.

Avena-Zampieri CL, Hutter J, Rutherford M, Milan A, Hall

M, Egloff A, Lloyd DFA, Nanda S, Greenough A and

Story L. Assessment of the fetal lungs in utero. Am J

Obstet Gynecol MFM. 2022; 4(5): 100693.

Xiao S, Zhang J, Zhu Y, Zhang Z, Cao H, Xie M and Zhang

L. Application and Progress of Artificial Intelligence

in Fetal Ultrasound. J Clin Med. 2023; 12(9):

Xia TH, Tan M, Li JH, Wang JJ, Wu QQ and Kong DX.

Establish a normal fetal lung gestational age grading

model and explore the potential value of deep

learning algorithms in fetal lung maturity evaluation.

Chin Med J (Engl). 2021; 134(15): 1828-1837.

Rigatti SJ. Random Forest. Journal of insurance medicine

(New York, NY). 2017; 47(1): 31-39.

Karypidis E, Mouslech SG, Skoulariki K and Gazis AJA.

Comparison Analysis of Traditional Machine

Learning and Deep Learning Techniques for Data and

Image Classification. 2022; abs/2204.05983

Jiang Y, Luo J, Huang D, Liu Y and Li DD. Machine

Learning Advances in Microbiology: A Review of

Methods and Applications. Front Microbiol. 2022;

Zhou SK, Greenspan H, Davatzikos C, Duncan JS, van

Ginneken B, Madabhushi A, Prince JL, Rueckert D

and Summers RM. A review of deep learning in

medical imaging: Imaging traits, technology trends,

case studies with progress highlights, and future

promises. Proceedings of the IEEE Institute of Electrical and Electronics Engineers. 2021; 109(5):

-838.

Singh A, Sengupta S and Lakshminarayanan V. Explainable

Deep Learning Models in Medical Image Analysis.

Journal of imaging. 2020; 6(6):

Safety statement, 2000. International Society of Ultrasound

in Obstetrics and Gynecology (ISUOG). Ultrasound

Obstet Gynecol. 2000; 16(6): 594-596.

Pianykh OS. Digital Imaging and Communications in

Medicine (DICOM). In: 2012.

Banerjee V, Wang S, Drescher M, Russell R and Siddiqui

MM. Radiogenomics influence on the future of

prostate cancer risk stratification. Ther Adv Urol.

; 1417562872221125317.

Li S, Liu J, Wang Z, Cao Z, Yang Y, Wang B, Xu S, Lu L,

Iqbal Saripan M, Zhang X, Dong X and Wen DJRS.

Application of PET/CT-based deep learning

radiomics in head and neck cancer prognosis: a

systematic review. 2022

Nanni L, Ghidoni S and Brahnam SJPR. Handcrafted vs.

non-handcrafted features for computer vision

classification. 2017; 71158-172.

Zweig MH and Campbell G. Receiver-operating

characteristic (ROC) plots: a fundamental evaluation

tool in clinical medicine. Clin Chem. 1993; 39(4):

-577.

Khodadadi Shoushtari F, Dehkordi ANV and Sina S.

Quantitative and Visual Analysis of Data

Augmentation and Hyperparameter Optimization in

Deep Learning-Based Segmentation of Low-Grade

Glioma Tumors Using Grad-CAM. Ann Biomed

Eng. 2024; 52(5): 1359-1377.

Van Calster B, Wynants L, Verbeek JFM, Verbakel JY,

Christodoulou E, Vickers AJ, Roobol MJ and

Steyerberg EW. Reporting and Interpreting Decision

Curve Analysis: A Guide for Investigators. Eur Urol.

; 74(6): 796-804.

Tibshirani RJJotrsssb-m. Regression Shrinkage and

Selection via the Lasso. 1996; 58267-288.

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