According to a study, artificial intelligence (AI) and machine learning (ML) can detect and diagnose Polycystic Ovary Syndrome(PCOS)effectively. It is a common hormone disorder among women. The researchers reviewed the published scientific studies that use AI/ML to analyze data to diagnose and classify PCOS and found that AI/ML-based programs could detect PCOS successfully. 

PCOS is one of the most widespread endocrinological anomalies that affects one out of ten pre-menopausal reproductive women worldwide. It is associated with an excessive rise in male androgen hormone in the female body that causes a persistent disruption in hormone levels and adversely affects normal ovarian functions. It results in the growth of numerous cysts inside the ovary. 

PCOS is one of the causes of anovulatory infertility as well as is related to a range of metabolic and psychological disorders that include irregular menstrual periods, abrupt obesity, hirsutism, type 2 diabetes, increased depression, thyroid abnormalities, sexual dissatisfaction, etc. that lowers the quality of a healthy way of life. 

pcos detection using deep learning<br />

Studies revealed that women suffering from PCOS are at huge risk of endometrial and ovarian cancer, which leads to death if it is not detected early. Evidence suggests that if a well-standardized diagnostic approach can identify women with PCOS early, the condition can be recovered by appropriate, symptom-oriented, long-term, and dynamic treatments. 

The Rotterdam criteria for PCOS are three criteria that are used to diagnose PCOS by a wide spectrum of medical practitioners: hyperandrogenism, menstrual irregulations, and the existence of multiple cysts in ovary ultrasonography. Identification of numerous cysts using ultrasound scanning is a reliable method to detect PCOS. Due to reliance on the observer and significant image noise, medical analysis can be difficult and time-consuming, with the risk of human mistakes.

In underdeveloped countries, experienced radiologists are scarce for PCOS detection ultrasound. Women who are suffering from such conditions go undetected and untreated for a long time. So, researchers are working now to develop an effective PCOS detection approach that would use different modern computational techniques. 

The typical methodologies to detect PCOS use computational approaches that rely on many image processing techniques for feature extraction and traditional machine learning strategies for image classification, which is a tedious process with lower performance. Many researchers have performed deep learning approaches to detect PCOS from ultrasound images using the Convolutional Neural Network(CNN). 

PCOS detection using deep learning algorithms attains a high level of accuracy in categorizing images. Still, they have the limitation of consuming a lot of computing complexity and taking time to execute, which becomes a barrier to using them in practical applications. As such, an integrated or extended PCOS detection using machine learning approach may enhance the prediction performance and reduce the computational complexity of predicting PCOS image data generator.

Importance of Machine Learning in the Medical Industry

PCOS detection using machine learning

Applying deep learning models for PCOS detection in ultrasound images holds immense potential in the medical industry. 

Accurate and Quicker Diagnosis

Automation and artificial intelligence in diagnosis help reduce human error and speed up the diagnostic process. 

Early detection and Treatment of PCOS-related symptoms

PCOS detection test allows for early intervention and PCOS treatment. 

Reduced manual effort

Automated analysis reduces the manual workload for healthcare professionals.

Standardized diagnostic procedures

Consistent and standardized analysis aids in providing more uniform healthcare services.

The Future of Healthcare And Medical Data Annotation

The future of PCOS detection and data annotation<br />

Machine learning, artificial intelligence, IoT, Robotic process automation, and other technologies generate huge datasets. The data augmentation techniques are applied to increase the size and diversity of the PCOS detection dataset. Healthcare companies are working with medical data annotation companies to enhance the model’s performance. 

The healthcare industry has many strict regulations regarding the privacy of patient medical records and product safety standards. Low-quality data sets adversely affect the reliability and accuracy of AI and increase the risk of non-compliance. 

AI developers need quality data from different sources to build an effective AI model. It could be images, videos, audio files, or text. They ensure it is correctly annotated using best-in-class annotation tools and labeled with informative tags to provide some reference to the machine. The manual or automatic data labeling process makes it easy to read and understand the data by ML algorithms, called data annotation or data labeling. 

Annotation Box provides medical annotation services that have trained medical experts who provide specialized annotation services across the healthcare sector. We assist AI algorithms with quality training data to develop automated healthcare systems. We have a network of radiologists, pathologists, ophthalmologists, dermatologists, and general physicians to provide the annotators with the assistance for medical video annotation that goes into healthcare AI.

Types of medical annotation

Image Medical Annotation

Specialized medical annotators label images such as X-rays, CT scans, MRIs, and ultrasounds. Bounding box, polygon, and semantic segmentation are some of the image annotation techniques that are employed.

Medical Audio Annotation

Audio annotation converts audio files to text format for healthcare professionals and machine learning models to interpret. Most patient medical records are documented in audio format by the healthcare physician, who then annotates them to text format. Some methods for audio annotation include speech-to-text transcription and audio classification, among others.

Video annotation

Annotated video clips from the medical field, such as surgery videos, are used to train the machine model. Studies have shown that some AI surgical intelligence outperforms that of physicians.

Text Annotation

At Annotation Box, patient records and medical reports are labeled for various automation and record management processes. Entity recognition is a type of text annotation widely used for data extraction.


AI has been proven to be the best method for detecting polycystic ovarian syndrome (PCOS) by segmenting and classifying using ultrasound images of the ovary. AI and ML may pave the road for better healthcare services. Machine learning applications support significant change, particularly in businesses like healthcare that deal with data identification, image recognition, prediction, and identification.

Frequently Asked Questions

How to cure PCOS permanently?

While PCOS cannot be cured permanently, treatment along with certain supplements, practices, and dietary changes may help you manage it. 

What is the prediction of PCOS?

Hormone levels and obesity were significant positive predictors of PCOS diagnosis across models; gravidity and positive bHCG were negative predictors. Machine learning algorithms predict PCOS based on a large at-risk population.

How is polycystic syndrome diagnosed?

For detection of polycystic, you must meet two of the following diagnostic criteria to diagnose PCOS:

  1. Irregular ovulation is indicated by irregular menstrual cycle.
  2. Increased androgen levels or a blood test confirming you have increased levels.
  3. Multiple small cysts on the ovaries.

Is early detection of PCOS possible?

PCOS cannot be diagnosed until 2–3 years after a girl’s first menstrual cycle. It can take up to 2 years after the first period for any girl’s cycle to become regular. Still, many girls with PCOS can get pregnant if they have sex.

What is automated detection of polycystic ovarian syndrome using follicle recognition?

Firstly, an adaptive morphological filter filters the input ovary ultrasound image. A modified labeled watershed algorithm is then used to extract the contours of targets. A clustering method is applied to identify the expected follicular cysts.
Gabrielly Correia