Doctors at Israel's Maccabi national health fund have recently begun working with an Artificial Intelligence-based predictive algorithm that advises doctors in the process of deciding on personalized antibiotic treatment for patients.

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The new algorithm was developed by the Technion – Israel Institute of Technology together with KSM (Kahn-Sagol-Maccabi), the Maccabi Research and Innovation Center.

Maccabi chose to focus its first diagnoses on urinary tract infection – the most common bacterial infection among women. Around 30% of females suffer from the infection at least once during their lifetime, and up to 10% experience recurrent infections. Until now, in most cases, general treatment has been administered based on clinical guidelines and medical judgment. Sometimes, the bacteria prove to be antibiotic-resistant, resulting in the need to change the treatment plan.

Since the new algorithm was introduced, Maccabi doctors have treated tens of thousands of cases, and there has been a drop of around 35% in the need to switch antibiotics following the development of bacterial resistance to the drug prescribed.

This is significant because accuracy in the choice of antibiotics is far greater thanks to the new technology. In light of the success of this new development in the treatment of UTI, Maccabi has begun working on the development of additional detection systems that will help to contend with other infectious diseases that require personalized treatment with antibiotics.

The automated system works by recommending the most suitable antibiotic treatment for the patient to the doctor, based on clinical guidelines and other criteria such as age, gender, pregnancy status, residence in an assisted living facility, and personal history of UTI and antibiotics administered.

The unique algorithm was developed by Prof. Roy Kishony and Dr. Idan Yelin of the Technion Faculty of Biology, in cooperation with KSM, headed by Dr. Tal Patalon, and was introduced and implemented among Maccabi's doctors by the health fund's Medical Informatics team and Chief Physician's Department.

"The algorithm we developed together with Maccabi's experts is a major milestone in personalized medicine on the way to AI-based antibiotic treatments, which are personally tailored to the patient according to the prediction of treatment response and mitigate the development of resistant bacteria," said Kishony.

Dr. Shira Greenfield, Director of Medical Informatics at Maccabi, said: "The significance of administering personalized antibiotic treatment is that it lowers the risk of antibiotic resistance developing – a global problem which all healthcare entities are working to solve."

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Researchers at Ben-Gurion University of the Negev, Shamoon College of Engineering and Afeka Tel Aviv Academic College of Engineering have developed an innovative method for the rapid determination of the identity and antibiotic sensitivity of bacterial pathogens in urinary tract infections (UTI) in patients, BGN Technologies – the university' technology transfer company – announced Tuesday.

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The novel method enables detection of bacterial pathogens directly from urine samples in 30-40 minutes. The technology combines measurements of the infrared spectrum of the infecting bacteria with machine learning algorithms, to enable the simultaneous determination of both bacterial type at the species level and bacterial sensitivity to antibiotics.

The method has been tested on over 1,000 urine samples and was able to discriminate between bacterial species with approximately 97% accuracy and determine bacterial susceptibility to various antibiotics with approximately 85% accuracy.

If left untreated or treated with ineffective antibiotics, UTIs can lead to complications such as permanent kidney damage and blood contamination. UTIs affect over 150 million people annually around the globe and is the most common outpatient infection in the US. In hospitals, UTIs account for 40% of all hospital-acquired infections

The inventors of the new method include Professors Mahmoud Huleihel and Shraga Segal, both from the Dept. of Microbiology, Immunology and Genetics, Faculty of Health Science at BGU; Prof. Ahmad Salman from Shamoon College of Engineering and Dr Itshak Lapidot from Afeka Tel Aviv Academic College of Engineering. BGN Technologies, the technology transfer company of BGU, has filed for patent protection and is now seeking a strategic partner for the further development and commercialization of this promising technology.

Huleihel, said, "The new technology offers a novel clinical decision-support tool for early and precise antibiotic recommendations, that will result in effective treatment. More broadly, our invention is timely, given the global emerging threat of antimicrobial resistance."

"This method for the identification of bacterial pathogens in UTI patients is an important and long-awaited solution for the management of UTI," said BGN Technologies CEO Josh Peleg.

"Currently, identification of the bacterial pathogen and its antibiotics sensitivity is labor intensive and can take up to three days, leading to treatment delays and potential complications. This novel solution can supply medical staff with results within 1 hour after collecting a urine sample, with very high accuracy and minimal effort. We are confident that this method has the potential to become a mainstay in hospitals and outpatient clinics alike," Peleg added.

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Researchers from Ben-Gurion University of the Negev have developed a new method to combat the spread of antibiotic-resistant bacteria, the university announced Monday.

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Before the outbreak of the coronavirus pandemic, one of the most serious concerns in the medical community was antibiotic resistance, which occurs when bacteria develop the ability to defeat the drugs designed to kill them.

According to the World Health Organization, "a growing list of infections – such as pneumonia, tuberculosis, blood poisoning, gonorrhoea, and foodborne diseases – are becoming harder, and sometimes impossible, to treat as antibiotics become less effective."

BGU researchers, along with colleagues from the United States and Germany, have developed "molecular tweezers" that can damage the pathogenic bacteria's biofilm, a thin layer of fibers that protects it, and prevent the bacteria's spread and toxicity.

The process does not attack the bacteria directly and, therefore, they cannot develop a resistance to the technology.

"In the research that lasted more than three years, we succeeded in preventing biofilm formation by using molecular tweezers," BGU's Dr. Ravit Malishev said. "This breakthrough may pave the way for new methods in fighting antibiotic-resistant bacteria."

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