A novel method for estrous cycle staging using supervised object detection
Abstract The estrous cycle regulates reproductive events and hormone changes in female mammals and is analogous to the menstrual cycle in humans. Monitoring this cycle is necessary as it serves as a biomarker for overall health and is crucial for interpreting study results. The estrous cycle compris...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-01-01
|
| Series: | NPP-Digital Psychiatry and Neuroscience |
| Online Access: | https://doi.org/10.1038/s44277-024-00020-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841544633297928192 |
|---|---|
| author | Benjamin Babaev Saachi Goyal Tushar Arora Anita Autry Rachel A. Ross |
| author_facet | Benjamin Babaev Saachi Goyal Tushar Arora Anita Autry Rachel A. Ross |
| author_sort | Benjamin Babaev |
| collection | DOAJ |
| description | Abstract The estrous cycle regulates reproductive events and hormone changes in female mammals and is analogous to the menstrual cycle in humans. Monitoring this cycle is necessary as it serves as a biomarker for overall health and is crucial for interpreting study results. The estrous cycle comprises four stages influenced by fluctuating levels of hormones, mainly estradiol and progesterone. Tracking the cycle traditionally relies on vaginal cytology, which categorizes stages based on three epithelial cell concentrations. However, this method has limitations, including time-consuming training and variability among researchers. This study assesses the feasibility and reliability of two previous image classification models as well as introducing an alternative method of machine learning to address the challenges posed by manual vaginal cytology and image classification. An object detection-based machine learning model, Object Detection Estrous Staging (ODES), was employed to identify cell types throughout the estrous cycle in mice. A dataset of 730 vaginal cytology images with four different stains was used, with 335 images for training, 45 for validation, and 350 for testing. A novel, accurate set of rules for classification was derived by analyzing training images. ODES achieved an average accuracy of 80% in classifying cycle stages, comparable to human accuracy (66%) and previous image classification models (41–79%). The efficiency of ODES, processing 100 test images in just 2.67 minutes, makes it a valuable tool for large-scale neuropsychiatric studies involving female rodents and also encourages the integration of this variable into neurological and psychiatric research. These results demonstrate that ODES offers a fast, reliable, and accessible method for estrous cycle monitoring, potentially improving how researchers approach sex-based variables in neuropsychiatric studies. |
| format | Article |
| id | doaj-art-f6687c7cb486432f879e44f88e10565b |
| institution | Kabale University |
| issn | 2948-1570 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | NPP-Digital Psychiatry and Neuroscience |
| spelling | doaj-art-f6687c7cb486432f879e44f88e10565b2025-01-12T12:26:30ZengSpringerNPP-Digital Psychiatry and Neuroscience2948-15702025-01-01311910.1038/s44277-024-00020-xA novel method for estrous cycle staging using supervised object detectionBenjamin Babaev0Saachi Goyal1Tushar Arora2Anita Autry3Rachel A. Ross4Macaulay Honors College at The City College of New York/CUNYDominick P. Purpura Department of Neuroscience, Albert Einstein College of MedicineDominick P. Purpura Department of Neuroscience, Albert Einstein College of MedicineDominick P. Purpura Department of Neuroscience, Albert Einstein College of MedicineDominick P. Purpura Department of Neuroscience, Albert Einstein College of MedicineAbstract The estrous cycle regulates reproductive events and hormone changes in female mammals and is analogous to the menstrual cycle in humans. Monitoring this cycle is necessary as it serves as a biomarker for overall health and is crucial for interpreting study results. The estrous cycle comprises four stages influenced by fluctuating levels of hormones, mainly estradiol and progesterone. Tracking the cycle traditionally relies on vaginal cytology, which categorizes stages based on three epithelial cell concentrations. However, this method has limitations, including time-consuming training and variability among researchers. This study assesses the feasibility and reliability of two previous image classification models as well as introducing an alternative method of machine learning to address the challenges posed by manual vaginal cytology and image classification. An object detection-based machine learning model, Object Detection Estrous Staging (ODES), was employed to identify cell types throughout the estrous cycle in mice. A dataset of 730 vaginal cytology images with four different stains was used, with 335 images for training, 45 for validation, and 350 for testing. A novel, accurate set of rules for classification was derived by analyzing training images. ODES achieved an average accuracy of 80% in classifying cycle stages, comparable to human accuracy (66%) and previous image classification models (41–79%). The efficiency of ODES, processing 100 test images in just 2.67 minutes, makes it a valuable tool for large-scale neuropsychiatric studies involving female rodents and also encourages the integration of this variable into neurological and psychiatric research. These results demonstrate that ODES offers a fast, reliable, and accessible method for estrous cycle monitoring, potentially improving how researchers approach sex-based variables in neuropsychiatric studies.https://doi.org/10.1038/s44277-024-00020-x |
| spellingShingle | Benjamin Babaev Saachi Goyal Tushar Arora Anita Autry Rachel A. Ross A novel method for estrous cycle staging using supervised object detection NPP-Digital Psychiatry and Neuroscience |
| title | A novel method for estrous cycle staging using supervised object detection |
| title_full | A novel method for estrous cycle staging using supervised object detection |
| title_fullStr | A novel method for estrous cycle staging using supervised object detection |
| title_full_unstemmed | A novel method for estrous cycle staging using supervised object detection |
| title_short | A novel method for estrous cycle staging using supervised object detection |
| title_sort | novel method for estrous cycle staging using supervised object detection |
| url | https://doi.org/10.1038/s44277-024-00020-x |
| work_keys_str_mv | AT benjaminbabaev anovelmethodforestrouscyclestagingusingsupervisedobjectdetection AT saachigoyal anovelmethodforestrouscyclestagingusingsupervisedobjectdetection AT tushararora anovelmethodforestrouscyclestagingusingsupervisedobjectdetection AT anitaautry anovelmethodforestrouscyclestagingusingsupervisedobjectdetection AT rachelaross anovelmethodforestrouscyclestagingusingsupervisedobjectdetection AT benjaminbabaev novelmethodforestrouscyclestagingusingsupervisedobjectdetection AT saachigoyal novelmethodforestrouscyclestagingusingsupervisedobjectdetection AT tushararora novelmethodforestrouscyclestagingusingsupervisedobjectdetection AT anitaautry novelmethodforestrouscyclestagingusingsupervisedobjectdetection AT rachelaross novelmethodforestrouscyclestagingusingsupervisedobjectdetection |