Ejection fraction is an important method of mortality prediction among cardiac patients and a low ejection fraction number suggests problems with the heart's pumping function, and may be associated with heart failure. An estimated 6.2 million Americans suffer from heart failure, according to federal statistics. The American Heart Association predicts that more than eight million will have the condition by 2030. When tested on 100 patients, the Eko DUO device combined with an AI model was able to detect ejection fraction 35% with an area under the curve (AUC) of 0.90, which is comparable to previously published research in Nature Medicine. These findings could help identify patients with a low ejection fraction during routine physical examinations, facilitating rapid clinical recognition of those requiring further testing. This marks the first time that a point of care device with a single lead ECG combined with an AI algorithm identified low ejection fraction in patients.
Deep learning has vast ranging applications and its application in the healthcare industry always fascinates me. As a keen learner and a Kaggle noob, I decided to work on the Malaria Cells dataset to get some hands-on experience and learn how to work with Convolutional Neural Networks, Keras and images on the Kaggle platform. One of the many things I like about Kaggle is the immense knowledge it holds in the form of Kernels and Discussions. Taking cues and references from various kernels and experts really helped me get better at producing highly accurate results. Do look at other kernels and understand their approach to gain more insights for your own development and knowledge building.
Everything without exception any individual has ever done in all of history, all of it possible, by 2050, by smart machines. However, there are a lot of things that AI is doing already today. From reading to identifying objects, AI is everywhere. Let's look at the things AI is doing exceptionally good. Enabling individuals to chat with machines is a long-standing dream for human-computer collaboration.
"Jack, it'll take an hour of your time and it can save your life. "Come on, Sally, I feel fine." "Yeah, okay, but feeling fine does not necessarily mean you are fine. He not only said he felt fine, he actually did a bunch of push-ups right in the middle of his talk!" "Well, yes, but I'm not Randy Pausch and I don't have cancer or anything else wrong. "The whole point of Advanced Diagnosis Via Intelligent Learning is to find likely issues before the person feels anything is wrong. Look, if you don't want to listen to me, chat with S6. See what pearls of wisdom he might have."
A cytopathologist at SRL Diagnostics' Central Reference Laboratory in Mumbai, screens a Pap smear sample for the screening of cervical cancer under his microscope. His trained eyes work with an apparent effortlessness. However, there is an unspoken urgency in his actions as he strives to complete the set of samples for the day. Along with his team of five members, he screens about 200 slides for cervical cancer every day, apart from another 100 slides for diagnosing other types of cancers. SRL Diagnostics, the largest diagnostics laboratory company in India, has been witnessing an increase in the demand for cervical cancer screening.
Scientists at the TSU Laboratory of Biophotonics, working with Tomsk National Research Medical Center (TNIMC) oncologists, have developed a new approach to the diagnosis of adenocarcinoma, a malignant tumor of the prostate gland, that uses artificial intelligence to identify oncopathology and determine the stage of the disease. Using machine learning, a computer model was taught to distinguish between healthy tissues and pathology with 100 percent accuracy. The gold standard for the diagnosis of cancer is histology, during which tissue from a patient is examined for malignant changes. So that the samples can be stored for a long time, they are dehydrated and packed in paraffin. Then experts make thin sections and examine these slides under a microscope.
Hologic is bringing artificial intelligence to breast cancer imaging with its latest product. The Marlborough, MA-based company has won a nod for the 3DQuorum Imaging Technology, powered by Genius AI. These analytics identify clinically relevant regions of interest and preserve important features during the reconstruction of the SmartSlices. This helps to expedite read time by reducing the number of images for radiologists to review, without compromising image quality, sensitivity or accuracy. With 3DQuorum technology, the number of 3D images to review is reduced by two-thirds, saving an average of one hour per eight hours of daily image interpretation time.
Recently, Hisham Daoud and Magdy Bayoumi of the University of Louisiana at Lafayette have introduced a completely new Artificial Intelligence (AI) system that predicts epilepsy seizures. According to the World Health Organization's reports, around 50 million people around the world are suffering from epilepsy and 70% of those patients can control the seizures through medications. The new AI technology shows 99.6% accurate results, and the best thing about it is that it predicts the attacks an hour before it happens. In this way, the patient can gear up for it and take medications that can prevent its occurrence. Having enough time to control the attack is what a patient needs.