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Artificial intelligence (AI) — the ability of a machine to perform cognitive tasks to achieve a particular goal based on provided data — is revolutionizing and reshaping our health-care systems. The current availability of ever-increasing computational power, highly developed pattern recognition algorithms and advanced image processing software working at very high speeds has led to the emergence of computer-based systems that are trained to perform complex tasks in bioinformatics, medical imaging and medical robotics. Accessibility to ‘big data’ enables the ‘cognitive’ computer to scan billions of bits of unstructured information, extract the relevant information and recognize complex patterns with increasing confidence. Computer-based decision-support systems based on machine learning (ML) have the potential to revolutionize medicine by performing complex tasks that are currently assigned to specialists to improve diagnostic accuracy, increase efficiency of throughputs, improve clinical workflow, decrease human resource costs and improve treatment choices. These characteristics could be especially helpful in the management of prostate cancer, with growing applications in diagnostic imaging, surgical interventions, skills training and assessment, digital pathology and genomics. Medicine must adapt to this changing world, and urologists, oncologists, radiologists and pathologists, as high-volume users of imaging and pathology, need to understand this burgeoning science and acknowledge that the development of highly accurate AI-based decision-support applications of ML will require collaboration between data scientists, computer researchers and engineers.
Applications of machine learning (ML) to prostate cancer care are rapidly growing owing to the many technological platforms involved in its diagnosis, prognosis and treatment.
In diagnostic imaging, ML is applied to perform low-level image analysis tasks such as prostate segmentation and fusion of different modalities (for example MRI, CT and ultrasonography) and high-level inference and prediction tasks such as prostate cancer detection and characterization.
ML algorithms are able to enhance prostate cancer treatment by augmenting the surgeon’s display with information such as cancer localization during robotic procedures and other image-guided interventions and could be used towards autonomous manipulation of tools for assistance in the operating room.
Computer-assisted diagnosis of prostate cancer in histopathological slides could be achieved by ML in order to optimize accuracy, reproducibility and throughput and to further enhance health-care delivery by enabling the use of customized precision-care pathways.
ML methods are used to identify genes or groups of genes for which expression specificity to predict outcomes of prostate cancer is high and could be used for screening, developing diagnostic tools, determining optimal individualized treatment and producing targeted drug regimens.
Collaboration between urologists, data scientists, computer researchers and engineers is required to ensure that artificial intelligence (AI)-based decision-support applications are properly trained, operated and regulated.
New artificial intelligence (AI) helps radiologists more accurately read breast cancer screening images through deep learning models.
The model read and interpreted the findings of digital breast tomosynthesis (DBT) images, three-dimensional mammography that takes multiple pictures of the breast to detect possible cancers. Results showed that the deep learning tool was able to improve the accuracy of detection and cut reading times in half.
Sensitivity, a measure of the test’s ability to correctly identify those with the disease, increased from 77 percent to 85 percent when AI was employed. The test’s ability to correctly identify those without the disease also improved when the deep learning tool was used, raising the specificity from 63 percent to 69 percent.
The time required by radiologists to read and interpret the findings was cut from over a minute to 30 seconds with the assistance of the AI. Such a decline in reading time means radiologists will be able to interpret more images in the same amount of time and diagnosis more individuals efficiently.
The recall rate when the deep learning model was used declined by nearly eight percent. Recall rate is a measure of the proportion of women who completed follow-up exams after DBT imaging and had benign findings. A lower recall rate signifies false-positive findings and means fewer women will be seeing providers for unnecessary follow-up testing.
“Overall, readers were able to increase their sensitivity by 8 percent, lower their recall rate by 7 percent and cut their reading time in half when using AI concurrently while reading DBT cases compared to reading without using AI,” said Emily Conant, MD, lead author of the study, in an earlier news release.
The deep learning system used artificial intelligence to mine large amounts of DBT imaging data. Researchers trained the system using a large DBT dataset to identify suspicious findings in the images. The tool was then tested against 24 radiologists, 13 of whom were breast subspecialists. The radiologists read 260 DBT exams with and without AI assistance. The accuracy of each test’s read was compared, and the results were published in Radiology: Artificial Intelligence.
The accuracy of the deep learning system depends on the size of the datasets it is fed. Improved efficacy will increase the technology’s usefulness in clinical practice by continuing to cut down reading times and false-positive rates.
“The results of this study suggest that both improved efficiency and accuracy could be achieved in clinical practice using an effective AI system,” stated Conant.
Evidence suggests that DBT screening methods improve the accuracy of cancer detection when compared to more traditional screening tests such as digital mammography. Similarly, DBT tests also reduce the number of false-positive test results and unnecessary follow-ups as compared to digital mammography.
Despite both the improved accuracy and decreased false-positive readings, DBT tests are not widely adopted as a part of the standard of care primarily because the results take longer to interpret. The multiple images captured in a DBT screening test require radiologists to spend more time looking at each picture before drawing conclusions.
Employing AI strategies that cut down reading time and improve the tests accuracy could help support the argument of DBT’s adoption as a part of the standard of care.
(Source:Google)