A study published in The Lancet Digital Health by UPMC and University of Pittsburgh researchers demonstrates the highest accuracy to date in recognizing and characterizing prostate cancer using an artificial intelligence (AI) program. The AI technology used in the study is from of Ibex Medical Analytics. "Humans are good at recognizing anomalies, but they have their own biases or past experience," said senior author Rajiv Dhir, M.D., M.B.A., chief pathologist and vice chair of pathology at UPMC Shadyside and professor of biomedical informatics at Pitt. "Machines are detached from the whole story. To train the AI to recognize prostate cancer, Dhir and his colleagues provided images from more than a million parts of stained tissue slides taken from patient biopsies. Each image was labeled by expert pathologists to teach the AI how to discriminate between healthy and abnormal tissue. The algorithm was then tested on a separate set of 1,600 slides taken from 100 consecutive patients seen at UPMC for suspected prostate cancer. During testing, the AI demonstrated 98% sensitivity and 97% specificity at detecting prostate cancer -- significantly higher than previously reported for algorithms working from tissue slides. Also, this is the first algorithm to extend beyond cancer detection, reporting high performance for tumor grading, sizing and invasion of the surrounding nerves. These all are clinically important features required as part of the pathology report. AI also flagged six slides that were not noted by the expert pathologists. But Dhir explained that this doesn't necessarily mean that the machine is superior to humans. For example, in the course of evaluating these cases, the pathologist could have simply seen enough evidence of malignancy elsewhere in that patient's samples to recommend treatment. For less experienced pathologists, though, the algorithm could act as a failsafe to catch cases that might otherwise be missed. "Algorithms like this are especially useful in lesions that are atypical," Dhir said. "A nonspecialized person may not be able to make the correct assessment.

Prostate biopsy with cancer probability (blue is low, red is high). This case was originally diagnosed as benign but changed to cancer upon further review. The AI accurately detected cancer in this tricky case. A study published today (July 27, 2020) in The Lancet Digital Health by UPMC and University of Pittsburgh researchers demonstrates the highest accuracy to date in recognizing and characterizing prostate cancer using an artificial intelligence (AI) program. "Humans are good at recognizing anomalies, but they have their own biases or past experience," said senior author Rajiv Dhir, M.D., M.B.A., chief pathologist and vice chair of pathology at UPMC Shadyside and professor of biomedical informatics at Pitt. "Machines are detached from the whole story. To train the AI to recognize prostate cancer, Dhir and his colleagues provided images from more than a million parts of stained tissue slides taken from patient biopsies. Each image was labeled by expert pathologists to teach the AI how to discriminate between healthy and abnormal tissue. The algorithm was then tested on a separate set of 1,600 slides taken from 100 consecutive patients seen at UPMC for suspected prostate cancer. During testing, the AI demonstrated 98% sensitivity and 97% specificity at detecting prostate cancer -- significantly higher than previously reported for algorithms working from tissue slides. Also, this is the first algorithm to extend beyond cancer detection, reporting high performance for tumor grading, sizing, and invasion of the surrounding nerves. These all are clinically important features required as part of the pathology report. AI also flagged six slides that were not noted by the expert pathologists. But Dhir explained that this doesn't necessarily mean that the machine is superior to humans. For example, in the course of evaluating these cases, the pathologist could have simply seen enough evidence of malignancy elsewhere in that patient's samples to recommend treatment. For less experienced pathologists, though, the algorithm could act as a failsafe to catch cases that might otherwise be missed. "Algorithms like this are especially useful in lesions that are atypical," Dhir said. "A nonspecialized person may not be able to make the correct assessment.

"Humans are good at recognizing anomalies, but they have their own biases or past experience," said senior author Rajiv Dhir, M.D., M.B.A., chief pathologist and vice chair of pathology at UPMC Shadyside and professor of biomedical informatics at Pitt. "Machines are detached from the whole story. To train the AI to recognize prostate cancer, Dhir and his colleagues provided images from more than a million parts of stained tissue slides taken from patient biopsies. Each image was labeled by expert pathologists to teach the AI how to discriminate between healthy and abnormal tissue. The algorithm was then tested on a separate set of 1,600 slides taken from 100 consecutive patients seen at UPMC for suspected prostate cancer. During testing, the AI demonstrated 98% sensitivity and 97% specificity at detecting prostate cancer -- significantly higher than previously reported for algorithms working from tissue slides. Also, this is the first algorithm to extend beyond cancer detection, reporting high performance for tumor grading, sizing and invasion of the surrounding nerves. These all are clinically important features required as part of the pathology report. AI also flagged six slides that were not noted by the expert pathologists. But Dhir explained that this doesn't necessarily mean that the machine is superior to humans. For example, in the course of evaluating these cases, the pathologist could have simply seen enough evidence of malignancy elsewhere in that patient's samples to recommend treatment. For less experienced pathologists, though, the algorithm could act as a failsafe to catch cases that might otherwise be missed. "Algorithms like this are especially useful in lesions that are atypical," Dhir said. "A nonspecialized person may not be able to make the correct assessment.

Prostate biopsy with cancer probability (blue is low, red is high). This case was originally diagnosed as benign but changed to cancer upon further review. The AI accurately detected cancer in this tricky case. A study published today (July 27, 2020) in The Lancet Digital Health by UPMC and University of Pittsburgh researchers demonstrates the highest accuracy to date in recognizing and characterizing prostate cancer using an artificial intelligence (AI) program. "Humans are good at recognizing anomalies, but they have their own biases or past experience," said senior author Rajiv Dhir, M.D., M.B.A., chief pathologist and vice chair of pathology at UPMC Shadyside and professor of biomedical informatics at Pitt. "Machines are detached from the whole story. To train the AI to recognize prostate cancer, Dhir and his colleagues provided images from more than a million parts of stained tissue slides taken from patient biopsies. Each image was labeled by expert pathologists to teach the AI how to discriminate between healthy and abnormal tissue. The algorithm was then tested on a separate set of 1,600 slides taken from 100 consecutive patients seen at UPMC for suspected prostate cancer. During testing, the AI demonstrated 98% sensitivity and 97% specificity at detecting prostate cancer -- significantly higher than previously reported for algorithms working from tissue slides. Also, this is the first algorithm to extend beyond cancer detection, reporting high performance for tumor grading, sizing, and invasion of the surrounding nerves. These all are clinically important features required as part of the pathology report. AI also flagged six slides that were not noted by the expert pathologists. But Dhir explained that this doesn't necessarily mean that the machine is superior to humans. For example, in the course of evaluating these cases, the pathologist could have simply seen enough evidence of malignancy elsewhere in that patient's samples to recommend treatment. For less experienced pathologists, though, the algorithm could act as a failsafe to catch cases that might otherwise be missed. "Algorithms like this are especially useful in lesions that are atypical," Dhir said. "A nonspecialized person may not be able to make the correct assessment.

For the roughly 50 million people worldwide with epilepsy, the exchange of electrical signals between cells in their brain can sometimes go haywire and cause a seizure--often with little to no warning. Two researchers at the University of Louisiana at Lafayette have developed a new AI-powered model that can predict the occurrence of seizures up to one hour before onset with 99.6 percent accuracy. "Due to unexpected seizure times, epilepsy has a strong psychological and social effect on patients," explains Hisham Daoud, a researcher who co-developed the new model. Detecting seizures ahead of time could greatly improve the quality of life for patients with epilepsy and provide them with enough time to take action, he says. Notably, seizures are controllable with medication in up to 70 percent of these patients.

Diagnosis is one of the most important parts of medicine since doctors can hardly do anything to cure a patient if they don't know what the illness is. When it comes to tumors, the process is lengthy and complicated, increasing the chances of death. With the help of artificial intelligence, doctors can now cut the time needed for diagnosing tissue by 90 percent with considerable accuracy. Normally, diagnosing something like a brain tumor takes about 30 to 40 minutes, during which, doctors would need to leave the operation room in order to put the samples through the rigorous process for analysis, Futurism reports. With the help of advanced AI, however, this time can be cut down to a measly 3 to 4 minutes.

Artificial intelligence can spot your face in a crowd of thousands with near-perfect accuracy, surpassing the ability of humans to do the same – but when confronted with a larger set of images, it's not quite up to par. In the MegaFace Challenge launched by the University of Washington, researchers are working to improve the capabilities of facial recognition algorithms at the million person scale. It's hoped that the competition will help to solve crucial problems in facial recognition, including the identification of a single person across different ages and poses. Artificial intelligence can spot your face in a crowd of thousands with near-perfect accuracy, surpassing the ability of humans to do the same – but when confronted with a larger set of images, it's not quite up to par Recently, facial recognition algorithms have proven their abilities to perform with near-perfect accuracy. But, these algorithms were trained on a dataset of just 13,000 images, and when confronted with a larger collection, accuracy dropped.

Helen of Troy may have had the face that launched a thousand ships, but even the best facial recognition algorithms may have had trouble finding her face in a crowd of one million strangers. The first benchmark test based on one million faces has shown how facial recognition algorithms from Google and other research groups around the world can still fall short in accurately identifying and verifying faces. Facial recognition algorithms that had previously performed with more than 95 percent accuracy on a popular benchmark test involving 13,000 faces saw significant drops in accuracy when faced with the new MegaFace Challenge involving one million faces. The best performer on one test, Google's FaceNet algorithm, dropped from near-perfect accuracy on five-figure datasets to 75 percent on the million-face test. Other top algorithms dropped from above 90-percent accuracy on the small datasets to below 60 percent on the MegaFace Challenge.