About

Douglas G Simpson is a professor in the Department of Statistics at the University of Illinois Urbana-Champaign and an affiliate professor in the Beckman Institute for Advanced Science and Technology. His research interests include applied and computational statistics, biostatistics and bioinformatics, quantitative image analysis, machine learning, and functional data, as well as the general theory of robust and semiparametric statistical methods. He has served as Associate Editor of the Journal of the American Statistical Association, Biometrics, and Chemometrics and Intelligent Laboratory Systems, and has been a member of the Biostatistical Research and Design (BMRD) Study Section of the National Institutes of Health. Additionally, he has held leadership roles such as Chair of the Department of Statistics at the University of Illinois from 2000 to 2019 and Associate Director of the Institute for Mathematical and Statistical Innovation from 2020 to 2022. Dr. Simpson is recognized as a Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the American Association for the Advancement of Science. His educational background includes a PhD and MS in Statistics from the University of North Carolina at Chapel Hill and a BA in Mathematics from Carleton College.

Research topics

• Medicine
• Computer Science
• Internal medicine
• Obstetrics
• Artificial Intelligence
• Radiology
• Surgery
• Applied mathematics
• Mathematics
• Statistics
• Gynecology

Selected publications

• Repeatability and reproducibility of quantitative ultrasound cervical measurements in women at risk for preterm birth

  Discover Imaging. · 2025-11-19

  articleOpen accessSenior author

  Purpose: Transvaginal Quantitative Ultrasound (QUS) has the potential to enhance preterm birth risk monitoring during pregnancy but evidence on the reliability of commonly used QUS parameters in vivo is lacking. This study assesses intra-sonographer repeatability and inter-sonographer reproducibility of six different QUS measurements of the human cervix during pregnancy: Attenuation Coefficient (AC), Lizzi Feleppa (LF) Intercept, Midband and Slope, and Envelope Kappa and Mu. Methods: This prospective study was approved by the institutional review board at the University of Illinois, Chicago. Informed consent was obtained from all participants, who were selected from pregnant women enrolled in the single-center study, "QUS Technology for Identifying At-Risk Women for Spontaneous Preterm Birth." They received a standard clinical transvaginal ultrasound scan followed by two research scans at 20 ± 2 and 24 ± 2 weeks of gestation. During one or both research scans, they underwent two independent examinations (same-sonographer or cross-sonographer). QUS measurements were computed from ultrasound radiofrequency (RF) data. Variation attributable to transducers, phantoms, and sonographers was evaluated by linear mixed model analysis. QUS parameters were averaged over four acquisitions from each examination. Repeatability and reproducibility were evaluated using the coefficient of variation (CoV), intraclass correlation coefficient (ICC), and Bland-Altman analysis. Results: > 0.05). AC, LF Midband, LF Slope, Kappa, and Mu displayed moderate re-examination (intra-sonographer) repeatability (CoV: 11.9%-12.9%, ICC: 0.62-0.69). LF Intercept had poor repeatability (CoV: 6.9%, ICC: 0.38). AC and LF Midband also displayed moderate inter-sonographer reproducibility (CoV: 10.4%-13.9%, ICC: 0.61-0.63). LF Intercept had marginal reproducibility (CoV: 7.6%, ICC: 0.51). Kappa and mu had poor reproducibility (CoV: 6.9%-14.5%, ICC 0.27-0.38). Conclusion: Averaged in vivo transvaginal QUS measurements of AC and LF Midband have potential for development as noninvasive clinical measurements with moderate reproducibility for auxiliary monitoring of the progress of pregnancy. The other QUS parameters evaluated in this study require further refinement before they can be recommended for clinical use. Supplementary Information: The online version contains supplementary material available at 10.1007/s44352-025-00020-3.

  PublisherOA PDFDOI

• Automated Field of Interest Determination for Quantitative Ultrasound Analyses of Cervical Tissues: Toward Real-time Clinical Translation in Spontaneous Preterm Birth Risk Assessment

  Ultrasound in Medicine & Biology · 2024-09-12 · 4 citations

  articleOpen access
  PublisherOA PDFDOI

• Trajectory of Postpartum Cervical Remodeling in Women Delivering Full-Term and Spontaneous Preterm: Sensitivity to Quantitative Ultrasound Biomarkers

  Ultrasound in Medicine & Biology · 2024-09-04 · 3 citations

  articleOpen access

  OBJECTIVE: Women with a history of spontaneous preterm birth (sPTB) face an increased risk of recurrence. Yet, the factors contributing to the increased risk are unknown, hampering the development of targeted interventions. Noninvasive quantitative ultrasound (QUS) has been validated in the characterization of cervical tissue and has the potential to provide information about postpartum cervical remodeling. The objective of this study was to determine the postpartum cervical remodeling trajectories of women over 12 mo post-delivery and to determine whether there were differences between women who delivered full-term and spontaneous preterm that were sensitive to QUS biomarkers. METHODS: Data were collected prospectively from 55 women: 41 who delivered full-term and 14 who delivered spontaneously preterm at 6 wk, 3, 6, 9 and 12 mo (±2 wk) postpartum. Data from QUS biomarkers: Attenuation Coefficient; Backscatter Coefficient; Shear Wave Speed; and Lizzi-Feleppa Slope, Intercept and Midband were analyzed from the acquired radiofrequency data using a Siemens S2000 ultrasound system with a transvaginal MC 9-4 MHz probe. The biomarkers were analyzed using descriptive statistics and linear mixed-effects models. RESULTS: QUS biomarkers, Backscatter Coefficient and Lizzi-Feleppa Intercept showed significant differences during the year after delivery between women who had a full-term birth and sPTB (p < 0.05), suggesting that there are differences in the cervical remodeling trajectories between the two groups. All QUS biomarkers demonstrated significant variations between the full-term birth and sPTB groups over time (p < 0.05), indicating ongoing cervical remodeling for both groups during the 12-mo postpartum period. CONCLUSION: QUS biomarkers identified cervical microstructure differences and trajectories in the year after delivery between women who delivered full-term and spontaneous preterm.

  PublisherOA PDFDOI

• Enhanced identification of women at risk for preterm birth via quantitative ultrasound: a prospective cohort study

  American Journal of Obstetrics & Gynecology MFM · 2023 · 16 citations

  • Medicine
  • Obstetrics
  • Surgery

  BACKGROUND: Historically, clinicians have relied on medical risk factors and clinical symptoms for preterm birth risk assessment. In nulliparous women, clinicians may rely solely on reported symptoms to assess for the risk of preterm birth. The routine use of ultrasound during pregnancy offers the opportunity to incorporate quantitative ultrasound scanning of the cervix to potentially improve assessment of preterm birth risk. OBJECTIVE: This study aimed to investigate the efficiency of quantitative ultrasound measurements at relatively early stages of pregnancy to enhance identification of women who might be at risk for spontaneous preterm birth. STUDY DESIGN: A prospective cohort study of pregnant women was conducted with volunteer participants receiving care from the University of Illinois Hospital in Chicago, Illinois. Participants received a standard clinical screening followed by 2 research screenings conducted at 20±2 and 24±2 weeks. Quantitative ultrasound scans were performed during research screenings by registered diagnostic medical sonographers using a standard cervical length approach. Quantitative ultrasound features were computed from calibrated raw radiofrequency backscattered signals. Full-term birth outcomes and spontaneous preterm birth outcomes were included in the analysis. Medically indicated preterm births were excluded from the analysis. Using data from each visit, logistic regression with Akaike information criterion feature selection was conducted to derive predictive models for each time frame based on historical clinical and quantitative ultrasound features. Model evaluations included a likelihood ratio test of quantitative ultrasound features, cross-validated receiver operating characteristic curve analysis, sensitivity, and specificity. RESULTS: On the basis of historical clinical features alone, the best predictive model had an estimated receiver operating characteristic area under the curve of 0.56±0.03. By the time frame of Visit 1, a predictive model using both historical clinical and quantitative ultrasound features provided a modest improvement in the area under the curve (0.63±0.03) relative to that of the predictive model using only historical clinical features. By the time frame of Visit 2, the predictive model using historical clinical and quantitative ultrasound features provided significant improvement (likelihood ratio test, P<.01), with an area under the curve of 0.69±0.03. CONCLUSION: Accurate identification of women at risk for spontaneous preterm birth solely through historical clinical features has been proven to be difficult. In this study, a history of preterm birth was the most significant historical clinical predictor of preterm birth risk, but the historical clinical predictive model performance was not statistically significantly better than the no-skill level. According to our study results, including quantitative ultrasound yields a statistically significant improvement in risk prediction as the pregnancy progresses.

  PublisherOA PDFDOI

• Evaluating postpartum cervical remodeling with quantitative ultrasound technology

  The Journal of the Acoustical Society of America · 2023-03-01

  article

  Having a history of a previous spontaneous preterm birth (sPTB) is the strongest risk factor for recurrent sPTB. It is unknown if there are differences in postpartum cervical remodeling between women who have delivered spontaneous preterm (sPT) and full-term. No studies have evaluated the role of postpartum remodeling between the two groups. Quantitative ultrasound (QUS) is a noninvasive ultrasound technology used to quantify tissue microstructure and function. QUS biomarkers were used to evaluate postpartum cervical microstructure in women who delivered sPT and full-term. Data were collected from 54 women:14 who delivered sPT and 40 who delivered full-term. Transvaginal QUS scans were performed at 6 weeks (±2 weeks) after delivery. Attenuation coefficient (AC), backscatter coefficient (BSC), and shear wave speed (SWS) QUS biomarkers were collected. BSC was significantly higher at six weeks postpartum in women who delivered sPT versus full-term (p = 0.01), while the AC approached statistical significance (p = 0.09). QUS biomarker BSC was able to identify cervical microstructure differences at six weeks postpartum between women who delivered sPT and full-term. QUS technology may improve our understanding of postpartum cervical remodeling and has the potential to noninvasively direct precision-health approaches for recurrent sPTB. [Work supported by NIH 5F31NR019716.]

  PublisherDOI

• Predicting spontaneous PTB risk is improved when quantitative ultrasound data are included with clinical data

  American Journal of Obstetrics and Gynecology · 2023-01-01

  articleOpen access
  PublisherOA PDFDOI

• Quantitative ultrasound for preterm birth risk prediction—Part 1: Statistical evaluation

  The Journal of the Acoustical Society of America · 2023-03-01

  article

  Hypothesis: Predicting the spontaneous preterm birth (sPTB) risk level is enhanced when using both historical clinical (HC) data and quantitative ultrasound (QUS) data compared to using only HC data. HC data defined herein includebirth history prior to that of the current pregnancy as well as, from the current pregnancy, a clinical cervical length assessment, and physical examination data. Study population included 248 full-term births (FTBs) and 26 sPTBs. Advanced statistical analyses were performed for supervised classification containing 53 scaled candidate features (48 QUS, 5 HC) using nested fivefold cross-validation of L1-penalized linear logistic regression with 1000 repetitions to identify potential predictors. Statistical models for HC data alone and HC + QUS data were compared with likelihood-ratio test, cross-validated receiver operating characteristic (ROC) area under the curve (AUC), sensitivity, and specificity. To assess performance, the ROC-AUC was estimated with 10-fold cross-validation logistic regression and 1000 repetitions. Averaged ROC curves plus AUCs were computed using threshold averaging. AUC confidence intervals and test statistics to test the two ROC curves’ differences were constructed via DeLong method. Combined HC and QUS data identified women at sPTB risk with better AUC (0.68; 95% CI, 0.57–0.78) than those of HC data alone (0.53; 95% CI, 0.40–0.66). [Work supported by NIHR01HD089935.]

  PublisherDOI

• Automated region of interest placement on cervical ultrasound images for assessing preterm birth risk

  The Journal of the Acoustical Society of America · 2023-03-01 · 1 citations

  article

  Spontaneous preterm birth (sPTB) is one of the leading causes of infant morbidity. Medical interventions can prevent death caused by preterm birth if the risk is predicted at early stages. Quantitative ultrasound (QUS) is found valuable for predicting sPTB risk with a limitation of requiring a medically trained image analyst to manually draw a region of interest (ROI) on the cervix of a B-mode ultrasound image. An automated ROI placement algorithm was designed and trained to reduce the reliance on human annotations. The algorithm utilized a deep neural network with an optimized U-Net architecture to locate cervical tissues. A total of 8670 ultrasound images with sonographer-drawn ROIs were used for algorithm training and testing, followed by several postprocessing steps to yield the final ROIs. Quantitative comparison between algorithm-generated and sonographer-drawn ROIs yielded an average pixel accuracy of 96% and a dice coefficient of 88%. In addition, the QUS’s attenuation coefficient (AC) and backscatter coefficient (BSC) obtained from the algorithm-generated ROIs were highly correlated to those obtained from the sonographer-drawn ROIs with a Pearson correlation coefficient of 0.93 and 0.85, respectively. The results support the feasibility of automating QUS imaging for sPTB risk assessment. [Work supported by NIHR01HD089935.]

  PublisherDOI

• Testing linear operator constraints in functional response regression with incomplete response functions

  Electronic Journal of Statistics · 2023-01-01 · 1 citations

  articleOpen accessSenior author

  Hypothesis testing procedures are developed to assess linear operator constraints in function-on-scalar regression when incomplete functional responses are observed. The approach enables statistical inferences about the shape and other aspects of the functional regression coefficients within a unified framework encompassing three incomplete sampling scenarios; (i) partially observed response functions as curve segments over random sub-intervals of the domain, (ii) discretely observed functional responses with additive measurement errors, and (iii) the composition of former two scenarios, where partially observed response segments are observed discretely with measurement error. The latter scenario has been little explored to date, although such structured data is increasingly common in applications. For statistical inference, deviations from the constraint space are measured via integrated L2-distance between estimates from the constrained and unconstrained model spaces. Large sample properties of the proposed test procedure are established, including the consistency, asymptotic distribution, and local power of the test statistic. The finite sample power and level of the proposed test are investigated in a simulation study covering a variety of scenarios. The proposed methodologies are illustrated by applications to U.S. obesity prevalence data, analyzing the functional shape of its trends over time, and motion analysis in a study of automotive ergonomics.

  PublisherOA PDFDOI

• Predicting Spontaneous Pre-term Birth Risk Is Improved When Quantitative Ultrasound Data Are Included With Historical Clinical Data

  Ultrasound in Medicine & Biology · 2023 · 11 citations

  • Medicine
  • Obstetrics
  • Gynecology
  PublisherDOI

Recent grants

• Generalized Regression Modeling of Ordinal and Bounded Response Data

  NSF · $84k · 2000–2004

Frequent coauthors

• William D. O’Brien

  University of Illinois Urbana-Champaign

  41 shared

• Raymond J. Carroll

  University of Technology Sydney

  17 shared

• Minge Xie

  16 shared

• James F. Zachary

  University of Illinois Urbana-Champaign

  16 shared

• Leon A. Frizzell

  University of Illinois Urbana-Champaign

  15 shared

• Aiguo Han

  Virginia Tech

  13 shared

• Barbara L. McFarlin

  University of Illinois Chicago

  13 shared

• Rita J. Miller

  University of Illinois Urbana-Champaign

  10 shared

Labs

• Douglas Simpson LabPI

Awards & honors

• Fellow, Institute of Mathematical Statistics (1998)
• Fellow, American Statistical Association (2000)
• Fellow, American Association for the Advancement of Science…