Cell-free DNA in maternal blood and artificial intelligence: accurate prenatal detection of fetal congenital heart defects

Bahado‐Singh, Ray O.; Friedman, Perry; Talbot, Ciara; Aydas, Buket; Southekal, Siddesh; Mishra, Nitish K.; Guda, Chittibabu; Yılmaz, Ali; Radhakrishna, Uppala; Vishweswaraiah, Sangeetha

doi:10.1016/j.ajog.2022.07.062

Cited by 8 publications

(3 citation statements)

References 52 publications

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“…A total of 6 different AI systems, like random forest, support vector machine and deep learning were used for cross validation since data set size was small (12 cases and 26 controls). Using a combination of nucleotide markers, they achieved an AUC of 0.97, sensitivity of 98% and specificity of 94% [ 13 ].…”

Section: Resultsmentioning

confidence: 99%

Diagnostic AI and Cardiac Diseases

Ozsahin

Ozgocmen²,

Balcioglu³

et al. 2022

Diagnostics

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(1) Background: The purpose of this study is to review and highlight recent advances in diagnostic uses of artificial intelligence (AI) for cardiac diseases, in order to emphasize expected benefits to both patients and healthcare specialists; (2) Methods: We focused on four key search terms (Cardiac Disease, diagnosis, artificial intelligence, machine learning) across three different databases (Pubmed, European Heart Journal, Science Direct) between 2017–2022 in order to reach relatively more recent developments in the field. Our review was structured in order to clearly differentiate publications according to the disease they aim to diagnose (coronary artery disease, electrophysiological and structural heart diseases); (3) Results: Each study had different levels of success, where declared sensitivity, specificity, precision, accuracy, area under curve and F1 scores were reported for every article reviewed; (4) Conclusions: the number and quality of AI-assisted cardiac disease diagnosis publications will continue to increase through each year. We believe AI-based diagnosis should only be viewed as an additional tool assisting doctors’ own judgement, where the end goal is to provide better quality of healthcare and to make getting medical help more affordable and more accessible, for everyone, everywhere.

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Section: Resultsmentioning

confidence: 99%

Diagnostic AI and Cardiac Diseases

Ozsahin

Ozgocmen²,

Balcioglu³

et al. 2022

Diagnostics

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“…4 Indeed, the concept of PM has now been seeded in other unrelated disciplines. 5 Circulating cell-free DNA (cfDNA) refers to DNA that is present in the bloodstream and that exists outside of the cells. 6 Recent publications have suggested the value of epigenomic analysis for the elucidation of cancer pathogenesis and for accurate minimally invasive detection 7,8 using cfDNA.…”

Section: Introductionmentioning

confidence: 99%

“…Since its establishment as an NIH priority area, there has been a global explosion in publications related to precision oncology 4 . Indeed, the concept of PM has now been seeded in other unrelated disciplines 5 …”

Section: Introductionmentioning

confidence: 99%

Precision oncology: Artificial intelligence, circulating cell‐free DNA, and the minimally invasive detection of pancreatic cancer—A pilot study

Bahado‐Singh,

Turkoglu,

Aydas

et al. 2023

Cancer Medicine

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BackgroundPancreatic cancer (PC) is among the most lethal cancers. The lack of effective tools for early detection results in late tumor detection and, consequently, high mortality rate. Precision oncology aims to develop targeted individual treatments based on advanced computational approaches of omics data. Biomarkers, such as global alteration of cytosine (CpG) methylation, can be pivotal for these objectives. In this study, we performed DNA methylation profiling of pancreatic cancer patients using circulating cell‐free DNA (cfDNA) and artificial intelligence (AI) including Deep Learning (DL) for minimally invasive detection to elucidate the epigenetic pathogenesis of PC.MethodsThe Illumina Infinium HD Assay was used for genome‐wide DNA methylation profiling of cfDNA in treatment‐naïve patients. Six AI algorithms were used to determine PC detection accuracy based on cytosine (CpG) methylation markers. Additional strategies for minimizing overfitting were employed. The molecular pathogenesis was interrogated using enrichment analysis.ResultsIn total, we identified 4556 significantly differentially methylated CpGs (q‐value < 0.05; Bonferroni correction) in PC versus controls. Highly accurate PC detection was achieved with all 6 AI platforms (Area under the receiver operator characteristics curve [0.90–1.00]). For example, DL achieved AUC (95% CI): 1.00 (0.95–1.00), with a sensitivity and specificity of 100%. A separate modeling approach based on logistic regression‐based yielded an AUC (95% CI) 1.0 (1.0–1.0) with a sensitivity and specificity of 100% for PC detection. The top four biological pathways that were epigenetically altered in PC and are known to be linked with cancer are discussed.ConclusionUsing a minimally invasive approach, AI, and epigenetic analysis of circulating cfDNA, high predictive accuracy for PC was achieved. From a clinical perspective, our findings suggest that that early detection leading to improved overall survival may be achievable in the future.

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Future perspectives part I

Steegers

2024

Textbook of Obstetrics and Gynaecology

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Cell-free DNA in maternal blood and artificial intelligence: accurate prenatal detection of fetal congenital heart defects

Cited by 8 publications

References 52 publications

Diagnostic AI and Cardiac Diseases

Diagnostic AI and Cardiac Diseases

Precision oncology: Artificial intelligence, circulating cell‐free DNA, and the minimally invasive detection of pancreatic cancer—A pilot study

Future perspectives part I

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