How accurate is the AI at classifying normal breast MRI scans | Technology

How accurate is the AI at classifying normal breast MRI scans | Technology
How accurate is the AI at classifying normal breast MRI scans?

How well does the AI classify normal breast MRI exams?

An Artificial Intelligence (AI) algorithm could classify 20% of breast MRI exams as normal without missing any cancer, New York City researchers say.  If used in clinical practice, AI could make breast MRI screening more efficient.

In a presentation at the International Society for Magnetic Resonance Imaging in Medicine (ISMRM) meeting in London, researchers at Memorial Sloan Kettering Cancer Center in New York City discussed how they developed a deep learning model designed to classify MRI scans. normal breast MRI to a special degree.  worklist that only requires an abbreviated review by a radiologist.

In tests, the algorithm worked well and would have generated an estimated 20% time savings for radiologists, according to presenter Arka Bhowmik, PhD.

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"We developed a deep learning tool that classifies the 20% of normal cases on an abbreviated worklist without missing any cancers in the test set," he said.

Breast MRI is used to screen for breast cancer in high-risk patients, but more than 98% of these exams are normal.  In the study presented at ISMRM 2022, researchers sought to use AI to rank completely normal exams on an abbreviated radiologist worklist;  they also compared the algorithm's performance to that of fellowship-trained radiologists.  They also wanted to estimate the projected time savings from using AI.

The researchers developed their deep learning model based on the use of the BI-RADS system to provide AI training tags.  BI-RADS 1 cases were considered to have no imaging findings, while the remaining BI-RADS scores (2-6) were considered to have imaging findings.

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The researchers retrospectively collected 16,020 contrast-enhanced breast axial MRI exams performed at their institution on 8,330 patients between 2013 and 2019. Of these, 12,911 (80%) exams were used for training, 1,627 (10%) were used for validation and 1482 (10%) were reserved for testing.  In addition, 50 exams were randomly selected from the test pool for a study of readers.

The test set included 1,467 exams without cancer and 15 with cancer.  The AI ​​algorithm detected all 15 cancer cases and classified the 20% of non-cancer exams into an abbreviated interpretation worklist for a radiologist.  The remaining 80% of the exams were graded for full interpretation by a radiologist.

The researchers calculated that the total projected reading time under this paradigm would have been reduced from 148 hours to 119 hours, a time savings of 20%.

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In a study of readers comparing radiologists' performance with the AI ​​algorithm, 42 of 50 MRI exams were free of cancer and eight included cancer.  The eight cancer exams were identified by both the radiologists and the AI ​​algorithm.

Of the 42 cancer-free exams, nine were classified according to an abbreviated worklist for radiologists and 33 were classified according to the complete radiologist's interpretation.  Radiologists ruled out 39 (92%) of the 42 cancer-free exams and marked three (7%) for biopsy.

In the next phase of their work, the researchers are now performing a multi-institutional validation of the algorithm.  They are also developing a more generalized end-to-end algorithm that includes an initial breast segmentation step, according to the authors.

Source: AuntMinnie.com, Direct News 99