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1. Real-Time Brain Tumor Detection in Intraoperative Ultrasound Using YOLO11: From Model Training to Deployment in the Operating Room

Author

Cepeda, Santiago, Esteban-Sinovas, Olga, Romero, Roberto, Singh, Vikas, Shetty, Prakash, Moiyadi, Aliasgar, Zemmoura, Ilyess, Giammalva, Giuseppe Roberto, Del Bene, Massimiliano, Barbotti, Arianna, DiMeco, Francesco, West, Timothy R., Nahed, Brian V., Arrese, Ignacio, Hornero, Roberto, and Sarabia, Rosario

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Intraoperative ultrasound (ioUS) is a valuable tool in brain tumor surgery due to its versatility, affordability, and seamless integration into the surgical workflow. However, its adoption remains limited, primarily because of the challenges associated with image interpretation and the steep learning curve required for effective use. This study aimed to enhance the interpretability of ioUS images by developing a real-time brain tumor detection system deployable in the operating room. We collected 2D ioUS images from the Brain Tumor Intraoperative Database (BraTioUS) and the public ReMIND dataset, annotated with expert-refined tumor labels. Using the YOLO11 architecture and its variants, we trained object detection models to identify brain tumors. The dataset included 1,732 images from 192 patients, divided into training, validation, and test sets. Data augmentation expanded the training set to 11,570 images. In the test dataset, YOLO11s achieved the best balance of precision and computational efficiency, with a mAP@50 of 0.95, mAP@50-95 of 0.65, and a processing speed of 34.16 frames per second. The proposed solution was prospectively validated in a cohort of 15 consecutively operated patients diagnosed with brain tumors. Neurosurgeons confirmed its seamless integration into the surgical workflow, with real-time predictions accurately delineating tumor regions. These findings highlight the potential of real-time object detection algorithms to enhance ioUS-guided brain tumor surgery, addressing key challenges in interpretation and providing a foundation for future development of computer vision-based tools for neuro-oncological surgery.

Published
2025

3. Postoperative glioblastoma segmentation: Development of a fully automated pipeline using deep convolutional neural networks and comparison with currently available models

Author

Cepeda, Santiago, Romero, Roberto, Garcia-Perez, Daniel, Blasco, Guillermo, Luppino, Luigi Tommaso, Kuttner, Samuel, Arrese, Ignacio, Solheim, Ole, Eikenes, Live, Karlberg, Anna, Perez-Nunez, Angel, Escudero, Trinidad, Hornero, Roberto, and Sarabia, Rosario

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Accurately assessing tumor removal is paramount in the management of glioblastoma. We developed a pipeline using MRI scans and neural networks to segment tumor subregions and the surgical cavity in postoperative images. Our model excels in accurately classifying the extent of resection, offering a valuable tool for clinicians in assessing treatment effectiveness.

Published
2024

4. Spin Hall Nano-Oscillator Empirical Electrical Model for Optimal On-chip Detector Design

Author

Fiorelli, Rafaella, Rajabali, Mona, Méndez-Romero, Roberto, Kumar, Akash, Litvinenko, Artem, Serrano-Gotarredona, Teresa, Moradi, Farshad, Åkerman, Johan, Linares-Barranco, Bernabé, and Peralías, Eduardo

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Materials Science, Physics - Applied Physics
Abstract

As nascent nonlinear oscillators, nano-constriction spin Hall nano-oscillators (SHNOs) represent a promising potential for integration into more complicated systems such as neural networks, magnetic field sensors, and radio frequency (RF) signal classification, their tunable high-frequency operating regime, easy synchronization, and CMOS compatibility can streamline the process. To implement SHNOs in any of these networks, the electrical features of a single device are needed before designing the signal detection CMOS circuitry. This study centers on presenting an empirical electrical model of the SHNO based on a comprehensive characterization of the output impedance of a single SHNO, and its available output power in the range of 2-10 GHz at various bias currents., Comment: 6 pages

Published
2024
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5. VMAP: Vaginal Microbiome Atlas during Pregnancy

Author

Parraga-Leo, Antonio, Oskotsky, Tomiko T, Oskotsky, Boris, Wibrand, Camilla, Roldan, Alennie, Tang, Alice S, Ha, Connie WY, Wong, Ronald J, Minot, Samuel S, Andreoletti, Gaia, Kosti, Idit, Theis, Kevin R, Ng, Sherrianne, Lee, Yun S, Diaz-Gimeno, Patricia, Bennett, Phillip R, MacIntyre, David A, Lynch, Susan V, Romero, Roberto, Tarca, Adi L, Stevenson, David K, Aghaeepour, Nima, Golob, Jonathan L, and Sirota, Marina

Subjects
Health Services and Systems, Health Sciences, Human Genome, Contraception/Reproduction, Pregnancy, Genetics, Maternal Health, Pediatric, Women's Health, Microbiome, Good Health and Well Being, bioinformatics, 16S rRNA sequencing, microbiome, visualization, data integration, Health services and systems
Abstract

ObjectivesTo enable interactive visualization of the vaginal microbiome across the pregnancy and facilitate discovery of novel insights and generation of new hypotheses.Material and methodsVaginal Microbiome Atlas during Pregnancy (VMAP) was created with R shiny to generate visualizations of structured vaginal microbiome data from multiple studies.ResultsVMAP (http://vmapapp.org) visualizes 3880 vaginal microbiome samples of 1402 pregnant individuals from 11 studies, aggregated via open-source tool MaLiAmPi. Visualized features include diversity measures, VALENCIA community state types, and composition (phylotypes, taxonomy) that can be filtered by various categories.DiscussionThis work represents one of the largest and most geographically diverse aggregations of the vaginal microbiome in pregnancy to date and serves as a user-friendly resource to further analyze vaginal microbiome data and better understand pregnancies and associated outcomes.ConclusionVMAP can be obtained from https://github.com/msirota/vmap.git and is currently deployed as an online app for non-R users.

Published
2024

6. New labor curves of dilation and station to improve the accuracy of predicting labor progress.

Author

Hamilton, Emily, Zhoroev, Tilekbek, Warrick, Philip, Tarca, Adi, Garite, Thomas, Caughey, Aaron, Melillo, Jason, Prasad, Mona, Neilson, Duncan, Singson, Peter, McKay, Kimberlee, and Romero, Roberto

Subjects
Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD), artificial intelligence, cervical dilation, dystocia, epidural anesthesia, failure to progress in labor, fetal descent, labor disorders, labor progression, machine learning, mixed-effects, multifactor, multivariable, partogram, prediction error, rupture of membranes, station, Humans, Female, Pregnancy, Labor Stage, First, Adult, Labor, Induced, Longitudinal Studies, Machine Learning, Cesarean Section, Cohort Studies, Labor, Obstetric, Time Factors, Young Adult
Abstract

BACKGROUND: The diagnosis of failure to progress, the most common indication for intrapartum cesarean delivery, is based on the assessment of cervical dilation and station over time. Labor curves serve as references for expected changes in dilation and fetal descent. The labor curves of Friedman, Zhang et al, and others are based on time alone and derived from mothers with spontaneous labor onset. However, labor induction is now common, and clinicians also consider other factors when assessing labor progress. Labor curves that consider the use of labor induction and other factors that influence labor progress have the potential to be more accurate and closer to clinical decision-making. OBJECTIVE: This study aimed to compare the prediction errors of labor curves based on a single factor (time) or multiple clinically relevant factors using two modeling methods: mixed-effects regression, a standard statistical method, and Gaussian processes, a machine learning method. STUDY DESIGN: This was a longitudinal cohort study of changes in dilation and station based on data from 8022 births in nulliparous women with a live, singleton, vertex-presenting fetus ≥35 weeks of gestation with a vaginal delivery. New labor curves of dilation and station were generated with 10-fold cross-validation. External validation was performed using a geographically independent group. Model variables included time from the first examination in the 20 hours before delivery; dilation, effacement, and station recorded at the previous examination; cumulative contraction counts; and use of epidural anesthesia and labor induction. To assess model accuracy, differences between each models predicted value and its corresponding observed value were calculated. These prediction errors were summarized using mean absolute error and root mean squared error statistics. RESULTS: Dilation curves based on multiple parameters were more accurate than those derived from time alone. The mean absolute error of the multifactor methods was better (lower) than those of the single-factor methods (0.826 cm [95% confidence interval, 0.820-0.832] for the multifactor machine learning and 0.893 cm [95% confidence interval, 0.885-0.901] for the multifactor mixed-effects method and 2.122 cm [95% confidence interval, 2.108-2.136] for the single-factor methods; P

Published
2024

8. Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research

Author

Golob, Jonathan L, Oskotsky, Tomiko T, Tang, Alice S, Roldan, Alennie, Chung, Verena, Ha, Connie WY, Wong, Ronald J, Flynn, Kaitlin J, Parraga-Leo, Antonio, Wibrand, Camilla, Minot, Samuel S, Oskotsky, Boris, Andreoletti, Gaia, Kosti, Idit, Bletz, Julie, Nelson, Amber, Gao, Jifan, Wei, Zhoujingpeng, Chen, Guanhua, Tang, Zheng-Zheng, Novielli, Pierfrancesco, Romano, Donato, Pantaleo, Ester, Amoroso, Nicola, Monaco, Alfonso, Vacca, Mirco, De Angelis, Maria, Bellotti, Roberto, Tangaro, Sabina, Kuntzleman, Abigail, Bigcraft, Isaac, Techtmann, Stephen, Bae, Daehun, Kim, Eunyoung, Jeon, Jongbum, Joe, Soobok, Community, The Preterm Birth DREAM, Theis, Kevin R, Ng, Sherrianne, Lee, Yun S, Diaz-Gimeno, Patricia, Bennett, Phillip R, MacIntyre, David A, Stolovitzky, Gustavo, Lynch, Susan V, Albrecht, Jake, Gomez-Lopez, Nardhy, Romero, Roberto, Stevenson, David K, Aghaeepour, Nima, Tarca, Adi L, Costello, James C, and Sirota, Marina

Subjects
Paediatrics, Biomedical and Clinical Sciences, Machine Learning and Artificial Intelligence, Pregnancy, Prevention, Human Genome, Genetics, Perinatal Period - Conditions Originating in Perinatal Period, Pediatric, Microbiome, Women's Health, Preterm, Low Birth Weight and Health of the Newborn, Reproductive health and childbirth, Good Health and Well Being, Female, Infant, Newborn, Humans, Premature Birth, Crowdsourcing, Phylogeny, Vagina, Microbiota, Preterm Birth DREAM Community, 16S harmonization, DREAM challenge, crowdsourced, machine learning, microbiome, predictive modeling, preterm birth, vaginal microbiome, Biomedical and clinical sciences
Abstract

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB;

Published
2024

14. Placental pathology is necessary to understand common pregnancy complications and achieve an improved taxonomy of obstetrical disease.

Author

Ernst, Linda, Morgan, Terry, Greene, Michael, Gyamfi-Bannerman, Cynthia, Louis, Judette, Maltepe, Emin, Mestan, Karen, Romero, Roberto, Stone, Joanne, Redline, Raymond, Roberts, Drucilla, and Parast, Mana

Subjects
acute chorioamnionitis, biomarker, chronic chorioamnionitis, chronic histiocytic intervillositis, cost-effectiveness, diagnosis, economic analysis, fetal death, fetal growth restriction, fetal vascular malperfusion, funisitis, immunohistochemistry, infection, inflammation, massive perivillous fibrin, maternal vascular malperfusion, placental abruption, preeclampsia, preterm birth, recurrent pregnancy loss, small for gestational age, stillbirth, thrombosis, villitis of unknown etiology, Humans, Pregnancy, Female, Placenta, Pregnancy Complications, Pregnancy Outcome
Abstract

The importance of a fully functioning placenta for a good pregnancy outcome is unquestioned. Loss of function can lead to pregnancy complications and is often detected by a thorough placental pathologic examination. Placental pathology has advanced the science and practice of obstetrics and neonatal-perinatal medicine by classifying diseases according to underlying biology and specific patterns of injury. Many past obstacles have limited the incorporation of placental findings into both clinical studies and day-to-day practice. Limitations have included variability in the nomenclature used to describe placental lesions, a shortage of perinatal pathologists fully competent to analyze placental specimens, and a troubling lack of understanding of placental diagnoses by clinicians. However, the potential use of placental pathology for phenotypic classification, improved understanding of the biology of adverse pregnancy outcomes, the development of treatment and prevention, and patient counseling has never been greater. This review, written partly in response to a recent critique published in a major obstetrics-gynecology journal, reexamines the role of placental pathology by reviewing current concepts of biology; explaining the most recent terminology; emphasizing the usefulness of specific diagnoses for obstetrician-gynecologists, neonatologists, and patients; previewing upcoming changes in recommendations for placental submission; and suggesting future improvements. These improvements should include further consideration of overall healthcare costs, cost-effectiveness, the clinical value added of placental assessment, improvements in placental pathology education and practice, and leveraging of placental pathology to identify new biomarkers of disease and evaluate novel therapies tailored to specific clinicopathologic phenotypes of both women and infants.

Published
2023

20. Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth.

Author

Tarca, Adi L, Pataki, Bálint Ármin, Romero, Roberto, Sirota, Marina, Guan, Yuanfang, Kutum, Rintu, Gomez-Lopez, Nardhy, Done, Bogdan, Bhatti, Gaurav, Yu, Thomas, Andreoletti, Gaia, Chaiworapongsa, Tinnakorn, DREAM Preterm Birth Prediction Challenge Consortium, Hassan, Sonia S, Hsu, Chaur-Dong, Aghaeepour, Nima, Stolovitzky, Gustavo, Csabai, Istvan, and Costello, James C

Subjects
DREAM Preterm Birth Prediction Challenge Consortium, aptamers, collaborative competition, human transcriptome arrays, machine learning, plasma proteomics, predictive modeling, preterm labor and delivery, spontaneous preterm birth, whole blood transcriptomics
Abstract

Identification of pregnancies at risk of preterm birth (PTB), the leading cause of newborn deaths, remains challenging given the syndromic nature of the disease. We report a longitudinal multi-omics study coupled with a DREAM challenge to develop predictive models of PTB. The findings indicate that whole-blood gene expression predicts ultrasound-based gestational ages in normal and complicated pregnancies (r = 0.83) and, using data collected before 37 weeks of gestation, also predicts the delivery date in both normal pregnancies (r = 0.86) and those with spontaneous preterm birth (r = 0.75). Based on samples collected before 33 weeks in asymptomatic women, our analysis suggests that expression changes preceding preterm prelabor rupture of the membranes are consistent across time points and cohorts and involve leukocyte-mediated immunity. Models built from plasma proteomic data predict spontaneous preterm delivery with intact membranes with higher accuracy and earlier in pregnancy than transcriptomic models (AUROC = 0.76 versus AUROC = 0.6 at 27-33 weeks of gestation).

Published
2021

23. Blockade of IL-6R prevents preterm birth and adverse neonatal outcomes

Author

Farias-Jofre, Marcelo, Romero, Roberto, Galaz, Jose, Xu, Yi, Miller, Derek, Garcia-Flores, Valeria, Arenas-Hernandez, Marcia, Winters, Andrew D., Berkowitz, Bruce A., Podolsky, Robert H., Shen, Yimin, Kanninen, Tomi, Panaitescu, Bogdan, Glazier, Catherine R., Pique-Regi, Roger, Theis, Kevin R., and Gomez-Lopez, Nardhy

Published
2023
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26. Genomic analysis of Enterococcus faecium strain RAOG174 associated with acute chorioamnionitis carried antibiotic resistance gene: is it time for precise microbiological identification for appropriate antibiotic use?

Author

Pongchaikul, Pisut, Romero, Roberto, Mongkolsuk, Paninee, Vivithanaporn, Pornpun, Wongsurawat, Thidathip, Jenjaroenpun, Piroon, Nitayanon, Perapon, Thaipisuttikul, Iyarit, Kamlungkuea, Threebhorn, Singsaneh, Arunee, Santanirand, Pitak, and Chaemsaithong, Piya

Published
2023
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30. Pregnancy-specific responses to COVID-19 revealed by high-throughput proteomics of human plasma

Author

Gomez-Lopez, Nardhy, Romero, Roberto, Escobar, María Fernanda, Carvajal, Javier Andres, Echavarria, Maria Paula, Albornoz, Ludwig L., Nasner, Daniela, Miller, Derek, Gallo, Dahiana M., Galaz, Jose, Arenas-Hernandez, Marcia, Bhatti, Gaurav, Done, Bogdan, Zambrano, Maria Andrea, Ramos, Isabella, Fernandez, Paula Andrea, Posada, Leandro, Chaiworapongsa, Tinnakorn, Jung, Eunjung, Garcia-Flores, Valeria, Suksai, Manaphat, Gotsch, Francesca, Bosco, Mariachiara, Than, Nandor Gabor, and Tarca, Adi L.

Published
2023
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37. Meconium-stained amniotic fluid

Author

Gallo, Dahiana M., Romero, Roberto, Bosco, Mariachiara, Gotsch, Francesca, Jaiman, Sunil, Jung, Eunjung, Suksai, Manaphat, Ramón y Cajal, Carlos López, Yoon, Bo Hyun, and Chaiworapongsa, Tinnakorn

Published
2023
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47. Deep Learning-Based Glioma Segmentation of 2D Intraoperative Ultrasound Images: A Multicenter Study Using the Brain Tumor Intraoperative Ultrasound Database (BraTioUS).

Author

Cepeda, Santiago, Esteban-Sinovas, Olga, Singh, Vikas, Shetty, Prakash, Moiyadi, Aliasgar, Dixon, Luke, Weld, Alistair, Anichini, Giulio, Giannarou, Stamatia, Camp, Sophie, Zemmoura, Ilyess, Giammalva, Giuseppe Roberto, Del Bene, Massimiliano, Barbotti, Arianna, DiMeco, Francesco, West, Timothy Richard, Nahed, Brian Vala, Romero, Roberto, Arrese, Ignacio, and Hornero, Roberto

Subjects
GLIOMAS, COMPUTER-assisted image analysis (Medicine), NEUROSURGERY, RESEARCH funding, SURGICAL therapeutics, CONVOLUTIONAL neural networks, CANCER patients, RETROSPECTIVE studies, RESEARCH, MEDICAL records, ACQUISITION of data, DEEP learning, BRAIN tumors
Abstract

Simple Summary: This study explores the use of artificial intelligence to improve the accuracy of intraoperative ultrasound (ioUS) imaging for glioma segmentation during neurosurgery. By training a deep learning model on data from multiple centers, this research demonstrates the potential for automated tumor delineation, despite challenges such as image noise and variability. The model was tested on independent datasets and showed strong performance overall, although external validation highlighted areas for improvement. Notably, the model demonstrated generalizability across ioUS systems from different scanner types and manufacturers, underscoring its robustness in diverse clinical settings. These findings emphasize the feasibility of using AI to enhance ioUS imaging, paving the way for more precise and efficient tumor resections in clinical practice. Background: Intraoperative ultrasound (ioUS) provides real-time imaging during neurosurgical procedures, with advantages such as portability and cost-effectiveness. Accurate tumor segmentation has the potential to substantially enhance the interpretability of ioUS images; however, its implementation is limited by persistent challenges, including noise, artifacts, and anatomical variability. This study aims to develop a convolutional neural network (CNN) model for glioma segmentation in ioUS images via a multicenter dataset. Methods: We retrospectively collected data from the BraTioUS and ReMIND datasets, including histologically confirmed gliomas with high-quality B-mode images. For each patient, the tumor was manually segmented on the 2D slice with its largest diameter. A CNN was trained using the nnU-Net framework. The dataset was stratified by center and divided into training (70%) and testing (30%) subsets, with external validation performed on two independent cohorts: the RESECT-SEG database and the Imperial College NHS Trust London cohort. Performance was evaluated using metrics such as the Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), and 95th percentile Hausdorff distance (HD95). Results: The training cohort consisted of 197 subjects, 56 of whom were in the hold-out testing set and 53 in the external validation cohort. In the hold-out testing set, the model achieved a median DSC of 0.90, ASSD of 8.51, and HD95 of 29.08. On external validation, the model achieved a DSC of 0.65, ASSD of 14.14, and HD95 of 44.02 on the RESECT-SEG database and a DSC of 0.93, ASSD of 8.58, and HD95 of 28.81 on the Imperial-NHS cohort. Conclusions: This study supports the feasibility of CNN-based glioma segmentation in ioUS across multiple centers. Future work should enhance segmentation detail and explore real-time clinical implementation, potentially expanding ioUS's role in neurosurgical resection. [ABSTRACT FROM AUTHOR]

Published
2025
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