4 Ways AI Optimizes Healthcare Imaging Analytics

Jun 21, 2021 9:25:56 AM

AI Imaging Analytics

The radiology wing of hospital systems and diagnostic centers generates an abundance of sensitive data. However, they often lack the analytics infrastructure to access and parse the data efficiently. To make the most of this available big data, radiologists are leveraging AI-based imaging analytics.

Usually, medical image data generated from high-definition scans of the human body is voluminous. Relying exclusively on human effort to parse through all of it could lead to burnout. A tired radiologist looking at their 100th image that day could introduce human error due to laxity. AI solutions could help overcome many of these problems.

According to a report compiled by KLAS Research, US healthcare companies are increasingly expressing an interest in AI-based medical image analytics software, while only 17% are actively piloting such projects. This interest is slowly growing towards critical mass. 

By 2021-end, a surge in demand for such AI-powered software solutions is anticipated. This type of software will accelerate the process of revealing cardiovascular abnormalities, metabolic brain changes from diseases like Alzheimer’s, cancer detection, surgical planning, and reevaluation of ongoing treatment.

How can AI be incorporated into the medical imaging workflow?

AI, machine learning (ML), and deep learning (DL) methodologies can help enhance any component of the standard medical imaging workflow. They can improve imaging tools, provide insights about imaging data, help in Picture Archiving and Communication Systems (PACS), and can potentially render appropriate diagnoses.

AI innovation in the medical industry is a work in progress. Medical technology pioneers are making substantial innovations using machine learning techniques such as the following:

  • Classification: In this ML method, data is categorized into a given number of classes over a Convolutional Neural Network (CNN) architecture. Classification can help identify whether, for example, a lung X-Ray shows pneumonia by assigning several labels to parts of the image and weeding out parts that fall into the smallest classes.

  • Segmentation: This technique helps the physician identify tumors and determine the size of the anomaly. It detects anomalies and ascertains their sizes by identifying and aggregating specific pixels that contain them.

  • Localization: This machine learning methodology helps to narrow down the particular area of the image that contains the abnormality. Oncologists can use this methodology to detect the presence of cancerous masses more easily by having to analyze a much smaller dataset. 

Retrofitting AI to optimize imaging analytics software

Growing data complexity and more extensive datasets introduce new challenges. Some of the following functionalities of AI help ease the process of medical imaging-

  • Deep learning neural networks for medical imaging

Deep learning neural networks in AI are designed based on the human brain and consist of several layers. The input layer receives raw data such as Computed Tomography (CT) scan volumes. The deeper layers parse specific data features pertaining to the diagnostic task at hand. 

Using this data, the neural network learns the detection of abnormal features to analyze them and provide the best interpretation. The ultimate goal for the network algorithm is to learn to differentiate between features such as a lung nodule from a healthy lung or find abnormalities in muscles, bones, and cartilage.

  • Reducing the radiologists' burden

Basing the underlying AI algorithms on extensive clinical data from CT scans and Magnetic Resonance Imaging (MRI) scans, the software automatically turns the findings into a quantitative report. 

This helps radiologists interpret images faster and reduces human error and time involved in documenting the results of complex and ever-increasing medical imaging data. The burden of repetitive and routine tasks is reduced, freeing up the radiologist to extend their expertise in providing comprehensive diagnoses for nuanced cases.

  • More personalized treatment plans

A study published in the Journal of Medical Imaging showed that radiologists could leverage deep learning to more accurately evaluate lung cancer types from CT scans. This enabled the diagnosticians to provide more personalized insights for different patients and improve their treatment plans. 

In this manner, ML methodologies support the growing concept of precision medicine. This type of personalized medicine requires insights from imaging-derived biomarkers which typically need large datasets. Only DL analytics-based software can thoroughly parse these datasets.

  • AI-powered enterprise workflows

AI already plays a significant role in the areas of clinical workflow, image acquisition, and reconstruction. But the bigger challenges in enterprise healthcare setups include bringing all the data from disparate departmental silos together, facilitating clear communication of diagnoses to patients, automatic computations, and report generation. 

The development of large-scale imaging workflow paradigms leads to the complication of information flow across modern and legacy PACS. Additionally, this creates non-interoperable data silos, and makes fetching specific patient data harder.

How do AI enterprise applications ease the imaging analytics workflow?

Enterprise imaging solutions must combine massively scalable data management, interoperability, and rapid application development potential to provide the most suitable technology platforms for imaging analytics. 

Here are some enterprise workflow solutions that AI can help with:

  • Scheduling: Reduce communication breakdowns and operational inefficiencies while creating orders for medical imaging and scheduling patient-expert consultation.

  • Image calibration: Automated alignment and angulation of scan images help save time and reduce variability, leading to reproducible and reliable results.

  • Quantification: Automated detection and labeling of anatomical structures with the facility to add, remove and isolate them with reduced user interaction.

  • Report generation: Using deep learning to quantify and highlight abnormalities across dedicated body examinations for faster report generation in addition to providing interactive decision support for radiologists.

ALSO READ: 5 Innovative Use Cases of Machine Learning in Healthcare

AI-powered imaging analytics is here to stay

A common misconception makes the rounds of medical circles that AI will eat up jobs and eventually replace radiologists altogether. This is pure speculation with no basis in reality.

An AI model or algorithm designed for the limited scope of imaging analysis would not be able to provide a holistic diagnosis such as that provided by a trained medical expert. However, this is going to be a great support in the timely diagnosis, prevention, and treatment of a problem. That is why the adoption of AI and ML in medical imaging analytics software is a trend that is catching on fast. The demand for such software is surely going to grow further.

In today's ever-innovating medical industry, we help our health-tech clients maximize working capital utilization and identify patterns. You can book a consultation with us to effectively leverage AI and ML in imaging analytics and make your day-to-day diagnostic processes smarter while having to invest less time, money, and effort.

Allen Victor

Written by Allen Victor

Writes content around viral technologies and strives to make them accessible for the layman. Follow his simplistic thought pieces that focus on software solutions for industry-specific pressure points.