Evidence map›Paper›PMID 41206485›Full record

ArticleBiology of reproduction2026

The utility of artificial intelligence and deep learning to automate and accelerate follicle counts in human ovarian tissue†.

Alena Arlova, Jacqueline C Yano Maher, Hannah Anvari, Taylor Badger, Sarina Hanfling, Ramya Balasubramanian, Hong Lou, Maria De La Luz Sierra, Bo Hyon Yun, Baris Turkbey and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biology of reproduction, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Alena ArlovaArtificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Jacqueline C Yano MaherEunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
Hannah AnvariNorthwestern University, Chicago, IL, USA.
Taylor BadgerEunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
Sarina HanflingEunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
Ramya BalasubramanianEunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
Hong LouEunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
Maria De La Luz SierraEunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
Bo Hyon YunYonsei University College of Medicine, Department of Obstetrics and Gynecology Seoul, Seodaemun, South Korea.
Baris TurkbeyArtificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Veronica Gomez-LoboEunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.ORCID 0000-0001-9396-2142
G Thomas BrownArtificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.ORCID 0000-0001-7383-3116

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Follicles comprised of oocytes and surrounding cells are essential for reproductive function. They are fixed before birth and decrease thereafter through a process of activation, growth, and apoptosis. The advent of ovarian tissue cryopreservation is critical for fertility preservation and has allowed us to evaluate follicle numbers and folliculogenesis in humans. However, current histopathological assessments are labor intensive and subject to interobserver variability. We developed an AI-based method that integrates deep learning segmentation and object detection to automate follicle counting in whole-slide images (WSIs) of ovarian tissue. Using 1857 WSIs from 47 patients and 8300 annotated follicles, our method employs DeepLabV3+ for segmentation and Faster R-CNN for object detection. Predictions from both models are merged, and performance metrics (Dice coefficient, sensitivity, and positive predictive value) were calculated pre- and post-failure analysis. The segmentation model achieved a Dice coefficient of 0.4939, while the object detection model achieved a COCOmetric score 0.27. The merged results of both models performed with a sensitivity of 0.92 and PPV of 0.95, after manual correction of annotations. Our AI-driven approach enhances follicle quantitation accuracy and reproducibility, representing a promising tool to support research and clinical decision-making in fertility preservation.

Indexed as

Artificial IntelligenceDeep LearningOvarian FollicleOvaryCell CountCryopreservationFemaleFertility PreservationHumansartificial intelligenceautomated diagnosisconvolutional neural networks (cnns)cryopreservationdeep learningfertility preservationovarian histologywhole-slide imaging (wsi)

Identifiers

PMID41206485
PMCPMC13369320

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.