Understanding Problems to Lead Change
The increased application of ultrasound both in diagnostic and guided applications is well documented and continues to rise.
This rapid increase is expected to continue to improve. With this, new and better software for image enhancements and noise reduction are much needed.
The Ultrasound is done using low-energy acoustic waves, that offers a relatively safe diagnostic imaging environment but also leads to difficulties penetrating thick layers of human tissue.
With approximately two-thirds of the US populations estimated to be obese, the abdominal scans are challenging.
The artificial intelligence-based ultrasound image processing may help to make accurate predictions and help better medical diagnosis. The software solution was needed to measure internal & external OS, identify the curve, and to identify the cervical landmarks to determining cervical length. The solution can be made comprehensive by integrating a machine learning model to learn from current prediction and enhance the quality and accuracy of predictions step by step.
Defining the Solid Roadmap
OSP started a deep analysis to define the system architecture in order to streamline the image processing with a minimum amount of time.
We decided to design the system in three different modules namely image processing, machine learning model and image prediction.
Our developers worked hard and finalized the technology stack for development including Python, Flask for API, Open CV, .NET MVC and more.
Image processing with an AI-based system with super accuracy was our major challenge. After hours of brainstorming and core analysis, our team decided to use speckle suppression to reduce speckle and improve edge information. OSP finalized a conceptual design of the entire project including the process flow, trained Machine Learning Model, a suitable framework and more.
Delivering On the Promise
Stick technique is used to reduce speckle, improve edge information, image smoothing and a linear projection operation is applied.
After determining the gray level range (80% pixels), identifying the location of internal Cervical os is becomes easy.
A stick technique is used to trace the course of the cervical canal to carry our the delineation of the cervical canal.
The image processing module can efficiently measure external cervical OS in a step-wise fashion.
The processed ultrasound images through the image processing module are passed onto a Machine Learning Model. The acquired data set is split into two separate parts. One part is used for the testing purpose and another for training the machine learning model to enhance the accuracy of future predictions. Based on the analysis an advanced framework is applied to run the model on Neural Network.
Building to Deliver Experiences
Once the training is completed, the trained machine learning model is cross verified using the test data.
A comparison is made between the predicted results and the test data results to determine the accuracy of the Model.
The trained model is deployed on the server with an API interface to send the image to be predicted to the engine.
The processes image again runs through the Prediction module to train the machine learning model.
The AI-based Ultrasound Image Analysis and Prediction system has the potential to process the image and automatically and make an accurate prediction of up to 75% accuracy. The Machine Learning Model is frequently retrained based on the new data that is received by the system to ensure reinforcement learning. The AI-driven image processing helps in diagnosis of threatened patterned labor (TPL) is based on clinical assessment of the characteristics of the cervix. Cervical assessment between 20 to 24 weeks is an effective tool for a screening identifying asymptomatic patients at increased risk of preterm delivery before 34 weeks. The cervical length predictions made by our AI-based system acts as a decision support system for ultrasound specialists and helps them to make better and accurate medical diagnosis.
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