The application of Digital Twin (DT) technology and Federated Learning (FL) has great potential to change the field of biomedical image analysis, particularly for Computed Tomography (CT) scans. This paper presents Federated Transfer Learning (FTL) as a new Digital Twin-based CT scan analysis paradigm. FTL uses pre-trained models and knowledge transfer between peer nodes to solve problems such as data privacy, limited computing resources, and data heterogeneity. The proposed framework allows real-time collaboration between cloud servers and Digital Twin-enabled CT scanners while protecting patient identity. We apply the FTL method to a heterogeneous CT scan dataset and assess model performance using convergence time, model accuracy, precision, recall, F1 score, and confusion matrix. It has been shown to perform better than conventional FL and Clustered Federated Learning (CFL) methods with better precision, accuracy, recall, and F1-score. The technique is beneficial in settings where the data is not independently and identically distributed (non-IID), and it offers reliable, efficient, and secure solutions for medical diagnosis. These findings highlight the possibility of using FTL to improve decision-making in digital twin-based CT scan analysis, secure and efficient medical image analysis, promote privacy, and open new possibilities for applying precision medicine and smart healthcare systems.

Chicago's Field Museum is home to over a dozen ancient Egyptian mummies but one in particular has perplexed researchers for years. Now, the mystery of Lady Chenet-aa's burial procedure appears to be solved with the use of a CT scanner. Lady Chenet-aa lived roughly 3,000 years ago amid the 22nd Dynasty during Egypt's Third Intermediate Period. Soon after her death, one of the ways funerary experts prepared her for the afterlife was by constructing a cartonnage--a paper mache-like box housing a deceased person's body. In Chenet-aa's case, however, there isn't any hint of a visible seam, leaving Egyptologists to wonder for years exactly how embalmers placed her inside the casing. According to an October 24 announcement from the Field Museum, a mobile CT scanner helped to finally explain the strategy behind Chenet-aa's "locked-mummy" cartonnage, as well as new physical information about her at her time of death.

If there's one thing a hospital patient doesn't want to think about as they prepare for a medical scan it's the possibility a cyberattacker might have found a way to remotely tamper with the diagnostic images, or even quietly upped the radiation levels used to generate them. The good news is that nobody has ever been confirmed to have done such a thing to a computed tomography (CT) X-ray scanner, which along with MRI (magnetic resonance imaging) and ultrasound systems form the backbone of modern hospital diagnosis. There is a caveat of course – the moment when somebody tries must be growing closer, leaving researchers searching for a reliable way to head off the troubling possibilities. Now a team at Israel's famous Ben-Gurion University of the Negev thinks it has come up with a solution to the problem of defending medical imaging devices (MIDs) using an AI system trained with families of open source algorithms to monitor commands sent to CT scanners for something that doesn't look right. In a proof of concept study due to be published this month, this splits the AI defense into a context-free (CF) layer that filters for obviously suspect commands (an excessive radiation level, say), and a more sophisticated context-sensitive (CS) layer that compares an apparently legitimate command to the medical context in which it is being used (giving a child an adult radiation dose).

Will the critical shortages of therapy radiographers mean that we are about to be replaced by AI, robots and machine learning systems and that will essentially solve the training, retention and employment problems in our profession by stealth? The Society of Radiographers have just announced that the new apprentice programs are now "GO" and where a more vocational training environment in combination with a prospective employer and a degree course will allow employers to "attract and select individuals they believe have the potential to become radiographers". It has also been announced this month that the University of Portsmouth is to close its degree course in radiotherapy and oncology in 2020 for which the timing is particularly ironic and may well impact on recruitment in the South further exacerbating the current problem. I looked at these issues in my January blog and reported on some items in the media relating to this. The College of Radiographers published some of their latest feedback and information on Radiographer Apprenticeships in my February blog and now having read some of the latest books on the impact of Artificial Intelligence on us and especially the workplace, I thought it would be interesting this month to see how this might impact on our profession.

The algorithm, which is integrated with three new Canon CT scanners installed at Frimley Health NHS Foundation Trust, has been trained to differentiate'noise' from true signal, reducing distortions and maintaining details in image outputs.

A mummified Ancient Egyptian priest is talking from beyond the grave thanks to modern technology. Nesyamun, a priest at the time of pharaoh Ramses II the Pharaohs was mummified around 3,000 years ago. His remains are so well preserved that scientists were able to map his throat, mouth and voice box using a CT scanner at Leeds General Infirmary, and recreate it using 3D printing. The priest, who is normally on display at Leeds museum, was first unwrapped in 1824 and has'true of voice' inscribed on his coffin. Academics believe his voice would have produced a vowel-like sound -- somewhere between an'a' and'e' noise.

Predictive analytics can be used to reclaim millions of dollars in operational costs for healthcare organizations. As pressure mounts to lower healthcare costs, healthcare delivery organizations are taking a closer look at costs in all aspects of their business, particularly operations. More organizations are realizing there is a huge opportunity to lower operational costs by leveraging machine data and machine learning. By leveraging machine learning, the solution can predict and avoid unnecessary downtime, reduce maintenance costs, and maximize profitability from capital-intensive medical equipment. We've created a Glassbeam Healthcare ROI Calculator so you can easily visualize potential saving.

The Transportation Security Agency plans to incorporate machine learning into the computer tomography scanners that are starting to be used at airport security checkpoints. To advance the Accessible Property Screening Systems program, TSA is looking for researchers and industry partners to develop algorithms that could improve the automated detection of explosives and prohibited items among carry-on baggage and speed passengers through the checkpoints. Although it has been used to screen checked luggage for explosives since 2001, CT scanning is a relatively new tool for examining carry-on baggage where it would have to identify prohibited items like knives and disassembled weapons. The 3-D imaging and detection software in the CT scanners would increase the speed and accuracy of the scans, flagging the threats operators should manually check. It may eliminate the need for passengers to put their electronics and liquids in separate screening bins, TSA said prior to a June 2017 test of the technology.