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Machine learning radically reduces workload of cell counting for disease diagnosis

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The use of machine learning to perform blood cell counts for diagnosis of disease instead of expensive and often less accurate cell analyzer machines has nevertheless been very labor-intensive as it takes an enormous amount of manual annotation work by humans in the training of the machine learning model. However, researchers at Benihang University have developed a new training method that automates much of this activity. Their new training scheme is described in a paper published in the journal Cyborg and Bionic Systems on April 9. The number and type of cells in the blood often play a crucial role in disease diagnosis, but the cell analysis techniques commonly used to perform such counting of blood cells--involving the detection and measurement of physical and chemical characteristics of cells suspended in fluid--are expensive and require complex preparations. Worse still, the accuracy of cell analyzer machines is only about 90 percent due to various influences such as temperature, pH, voltage, and magnetic field that can confuse the equipment.


MIT, Harvard scientists find AI can recognize race from X-rays -- and nobody knows how - The Boston Globe

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A doctor can't tell if somebody is Black, Asian, or white, just by looking at their X-rays. The study found that an artificial intelligence program trained to read X-rays and CT scans could predict a person's race with 90 percent accuracy. But the scientists who conducted the study say they have no idea how the computer figures it out. "When my graduate students showed me some of the results that were in this paper, I actually thought it must be a mistake," said Marzyeh Ghassemi, an MIT assistant professor of electrical engineering and computer science, and coauthor of the paper, which was published Wednesday in the medical journal The Lancet Digital Health. "I honestly thought my students were crazy when they told me."


SoundWatch

Communications of the ACM

We present SoundWatch, a smartwatch-based deep learning application to sense, classify, and provide feedback about sounds occurring in the environment.


Challenges, Experiments, and Computational Solutions in Peer Review

Communications of the ACM

While researchers are trained to do research, there is little training for peer review. Several initiatives and experiments have looked to address this challenge. Recently, the ICML 2020 conference adopted a method to select and then mentor junior reviewers, who would not have been asked to review otherwise, with a motivation of expanding the reviewer pool to address the large volume of submissions.43 An analysis of their reviews revealed that the junior reviewers were more engaged through various stages of the process as compared to conventional reviewers. Moreover, the conference asked meta reviewers to rate all reviews, and 30% of reviews written by junior reviewers received the highest rating by meta reviewers, in contrast to 14% for the main pool. Training reviewers at the beginning of their careers is a good start but may not be enough. There is some evidence8 that quality of an individual's review falls over time, at a slow but steady rate, possibly because of increasing time constraints or in reaction to poor-quality reviews they themselves receive. While researchers are trained to do research, there is little training for peer review … Training reviewers at the beginning of their careers is a good start but may not be enough.


Will Artificial Intelligence and robotics usher in an era of sustainable precision agriculture?

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Across midwestern farms, if Girish Chowdhary has his way, farmers will someday release beagle-sized robots into their fields like a pack of hounds flushing pheasant. The robots, he says, will scurry in the cool shade beneath a wide diversity of plants, pulling weeds, planting cover crops, diagnosing plant infections, and gathering data to help farmers optimize their farms. Chowdhary, a researcher at the University of Illinois, works surrounded by corn, one of the most productive monocultures in the world. In the United States, the corn industry was valued at $82.6 billion in 2021, but it -- like almost every other segment of the agricultural economy -- faces daunting problems, including changing weather patterns, environmental degradation, severe labor shortages, and the rising cost of key supplies, or inputs: herbicides, pesticides, and seed. Agribusiness as a whole is betting that the world has reached the tipping point where desperate need caused by a growing population, the economic realities of conventional farming, and advancing technology converge to require something called precision agriculture, which aims to minimize inputs and the costs and environmental problems that go with them. No segment of agriculture is without its passionate advocates of robotics and artificial intelligence as solutions to, basically, all the problems facing farmers today.


Identification of long COVID patients through machine learning

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In a recent study posted to Preprints with The Lancet*, researchers developed a machine learning approach to identify patients with long coronavirus disease (COVID). The post-acute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are called long COVID. In the present study, researchers aimed to generate a robust clinical definition for long COVID using data related to long COVID patients. The team utilized data obtained from electronic health records that were integrated and harmonized in the secure N3C Data Enclave. This allowed the team to identify unique patterns and clinical characteristics among COVID-19-infected patients.


Artificial intelligence makes a splash in small-molecule drug discovery

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In the past five years, interest in applying artificial intelligence (AI) approaches in drug research and development (R&D) has surged. Driven by the expectation of accelerated timelines, reduced costs and the potential to reveal hidden insights from vast datasets, more than 150 companies with a focus on AI have raised funding in this period, based on an analysis of the field by Back Bay Life Science Advisors (Figure 1a). And the number of financings and average amount raised soared in 2021. At the forefront of this field are companies harnessing AI approaches such as machine learning (ML) in small-molecule drug discovery, which account for the majority of financings backed by venture capital (VC) in recent years (Figure 1b), as well as some initial public offerings (IPOs) for pioneers in the area (Table 1). Such companies have also attracted large pharma companies to establish multiple high-value partnerships (Table 2), and the first AI-based small-molecule drug candidates are now in clinical trials (Nat.


New imaging method makes tiny robots visible in the body

Robohub

How can a blood clot be removed from the brain without any major surgical intervention? How can a drug be delivered precisely into a diseased organ that is difficult to reach? Those are just two examples of the countless innovations envisioned by the researchers in the field of medical microrobotics. Tiny robots promise to fundamentally change future medical treatments: one day, they could move through patient's vasculature to eliminate malignancies, fight infections or provide precise diagnostic information entirely noninvasively. In principle, so the researchers argue, the circulatory system might serve as an ideal delivery route for the microrobots, since it reaches all organs and tissues in the body.


AI-based speech pattern analysis may allow Alzheimer's diagnosis by phone

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Patterns of speech in a phone conservation can be used to correctly identify adults in the early stages of Alzheimer's disease, a study published Wednesday by the journal PLOS found. Using more than 1,600 voice recordings of phone conversations made from 24 people with confirmed Alzheimer's and 99 healthy controls, researchers correctly identified those with the common form of dementia with roughly 90% accuracy, the data showed. The approach relies on the tendency of people with Alzheimer's "to speak more slowly and with longer pauses and to spend more time finding the correct word," the researchers said. These "vocal features" result in "broken messages and lack of speech fluency," which can be analyzed using an artificial intelligence-based program. The computer program was able to identify those with early Alzheimer's with essentially the same level of accuracy as a telephone-based test for cognitive function, according to the researchers.


AI and Oncology: IRT Saint Exupéry and IUCT-Oncopole collaborate to advance cancer research - Actu IA

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On May 17, two Toulouse-based institutes, the IRT Saint Exupéry and the IUCT-Oncopole, a European center of expertise in oncology, signed a partnership focused on artificial intelligence. The aim of this partnership is to pool cutting-edge skills around AI-based research projects designed to improve prevention, diagnosis and care in oncology, particularly by predicting therapeutic effectiveness. Two of these projects are already at an advanced stage. The Saint Exupéry Institute of Technological Research aims to accelerate scientific and technological research and transfer to the aeronautics and space industries for the development of reliable, robust, certifiable and sustainable innovative solutions. A private research foundation supported by the French government, the IRT's mission is to promote French technological research for the benefit of industry and to develop the ecosystem of the aeronautics, space and critical systems sectors by providing access to its research projects, technological platforms and expertise.