Single Photon Cameras to Peer into your Brain?

At Last, Single-Photon Cameras Could Peer Into Your Brain The tech has long been stymied on how to scale it out of the lab    By Dina Genkina  in Spectrum IEEE

Superconductor-based cameras that can detect a single photon—the smallest smidgeon of light—have existed for 20 years, but they’ve remained confined to laboratories due to the inability to scale them past a few pixels. Now, a team at the National Institute of Standards and Technology (NIST) in Boulder, Colo., has created a 0.4-megapixel single-photon camera—400 times as large as the previous biggest camera of its type. They reported their results in a preprint they submitted to arXiv on 15 June.

Single-photon cameras, made of superconducting nanowires, measure light with unrivaled sensitivity and speed, and across an unmatched frequency range. With the leap in size, the single-photon camera is poised to transition from a lab curiosity to an industrial technology. Such cameras could find a home imaging the cosmos on the next James Webb–type telescope, measuring light in photonic quantum computers and communications, and peering into the brain with noninvasive light-based techniques.

“From a scientific perspective, this is definitely opening a new avenue in optical brain imaging,” says Stefan Carp, an associate professor of radiology at the Harvard Medical School who was not involved in the work. “Other approaches for optically mapping cortical brain flow may have lower costs, but they all have shortcomings impacting signal quality that often require complex signal processing. There is no compromise with nanowires from a performance perspective.”.... 

Dr. ChatGPT Will Interface With You Now

Inevitable applications, How broadly?  Accurately?

https://spectrum.ieee.org/chatgpt-medical-exam

Dr. ChatGPT Will Interface With You Now Questioning the answers at the intersection of Big Data and Big Doctor, Eliza Strickland 

If you’re a typical person who has plenty of medical questions and not enough time with a doctor to ask them, you may have already turned to ChatGPT for help. Have you asked ChatGPT to interpret the results of that lab test your doctor ordered? The one that came back with inscrutable numbers? Or maybe you described some symptoms you’ve been having and asked for a diagnosis. In which case the chatbot probably responsed with something that began like, “I’m an AI and not a doctor,” followed by some at least reasonable-seeming advice. ChatGPT, the remarkably proficient chatbot from OpenAI, always has time for you, and always has answers. Whether or not they’re the right answers... well, that’s another question.

One question was foremost in his mind: “How do we test this so we can start using it as safely as possible?”

Meanwhile, doctors are reportedly using it to deal with paperwork like letters to insurance companies, and also to find the right words to say to patients in hard situations. To understand how this new mode of AI will affect medicine, IEEE Spectrum spoke with Isaac Kohane, Chair of the Department of Biomedical Informatics at Harvard Medical School. Kohane, a practicing physician with a computer science PhD, got early access to GPT-4, the latest version of the large language model that powers ChatGPT. He and ended up writing a book about it with Peter Lee, Microsoft’s corporate vice president of research and incubations, and Carey Goldberg, a science and medicine journalist. ... 

Paper-Like, Battery-Free, AI-Enabled Sensor for Holistic Wound Monitoring

 Paper-Like, Battery-Free, AI-Enabled Sensor for Holistic Wound Monitoring

ACM TECHNEWS

By National University of Singapore, June 29, 2023

The sensor patch, resembling a five-petaled Pinwheel Flower (Tabernaemontana divaricata), and the sensing materials/principles for each colorimetric sensor.

Credit: Xin Ting Zheng et al

A battery-free holistic wound monitoring sensor patch developed by researchers at the National University of Singapore (NUS) and the Agency for Science, Technology and Research's Institute of Materials Research and Engineering uses artificial intelligence (AI) to identify the wearer's wounding healing status within 15 minutes.

The PETAL (Paper-like Battery-free In situ AI-enabled Multiplexed) sensor patch features five colorimetric sensors that measure the wound's temperature, pH, trimethylamine, uric acid, and moisture.

A mobile phone is used to capture the sensor images, which are then analyzed by an AI algorithm.

NUS's Benjamin Tee said, "Our AI algorithm is capable of rapidly processing data from a digital image of the sensor patch for very accurate classification of healing status. This can be done without removing the sensor from the wound. In this way, doctors and patients can monitor wounds more regularly with little interruption to wound healing."

From National University of Singapore

View Full Article   

Doctors Train in Virtual Reality

Doctors Train in Virtual Reality

By Jake Widman

Commissioned by CACM Staff, June 22, 2023

Surgeons collaborate during virtual surgery.

In addition to relevant tools and a patient, the simulations can provide highlights and tool tips or floating diagrams to guide a trainee through performing the surgery.

Healthcare is just one of the many fields finding new uses for extended reality (XR), comprising both augmented and virtual reality.

Virtual reality (VR) in particular is being used for everything from hosting remote support groups for teen cancer patients to giving medical students a taste of the grisly reality of trauma medicine.

Those projects typically are one-off purpose-built implementations of VR, though. The technology has continued to advance and its availability to increase, enabling the creation of more general-purpose applications.

The potential is not lost on VR developers and investors. According to the June 2022 Global AR-VR in Healthcare Market report from Research and Markets, the sector was valued at $2,748 million in 2021, and is expected to reach $9,796 million by 2027.

Two applications identified by Research and Markets as drivers of that growth are medical training and surgery planning.

The U.K.'s Royal College of Surgeons has set up an commission to examine where surgery is headed, and one of its commissioners, plastic surgeon and medical entrepreneur Dr. Nadine Hachach-Haram, told MobiHealthNews, "We can see the effective use of virtual reality in simulation, in terms of preparation and rehearsal. The challenge here is the cost of scaling, as there are tens of thousands of different procedures with nuances in each of them."

Several companies are tackling the scaling problem.

How surgeons now train

A main value of VR simulation, as exemplified by its use to expose medical students to trauma situations, is to give surgeons an idea of what they can expect to encounter in actual surgery.

Surgery is traditionally taught as a mentorship program, explains Dr. Michael Ast, an orthopedic surgeon and Chief Medical Innovation Officer at New York's Hospital for Special Surgery. "We do didactic teaching in a modified classroom setting, and then we guide our trainees through the operations. Eventually, they start to participate in the procedure."

The problem with that approach is the limited number of those "tens of thousands of different procedures with nuances in each of them" in which a trainee might actually get to participate. Also, those 'nuances' are not about just the mechanics of the surgery, says Dr. Ast; "The farther along into training, the more our trainees come to realize that our job is not just to fix the problem, but also to preserve the rest of the anatomy. The questions tend to change from 'how does this drill work' to 'how do I make sure that when I'm using this drill, I'm not causing damage to other things?'"

Training on generic patient models   ... ' 

AI Helps Show How the Brain's Fluids Flow

Further analysis of how the brain works.

AI Helps Show How the Brain's Fluids Flow

By University of Rochester News Center

June 20, 2023

A team of scientists led by the University of Rochester's Douglas Kelley created new artificial intelligence-based velocimetry measurements to quantify the flow of fluids around cerebral blood vessels in the brain.

The researchers produced high-resolution visualizations of fluid flow in perivascular spaces by combining data from two-dimensional studies with physics-informed neural networks.

Explained Kelley, "This is a way to reveal pressures, forces, and the three-dimensional flow rate with much more accuracy than we can otherwise do. The pressure is important because nobody knows for sure quite what pumping mechanism drives all these flows around the brain yet. This is a new field."

The researchers think insights stemming from the AI technique could have implications for designing treatments for diseases like Alzheimer's.

From University of Rochester News Center

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Algorithm Uses Phone Camera, AI to Detect Blood Oxygen Levels

 Ai driven algorithm captures blood oxygen levels on Smartphone.  Note 'Hyperspectral Learning'.

Algorithm Uses Phone Camera to Detect Blood Oxygen Levels

By Purdue University, June 9, 2023

A smartphone camera paired with an AI-driven algorithm can capture blood oxygen data faster and more efficiently than highly specialized equipment.

The researchers used a computational approach that they described as “hyperspectral learning.”

Purdue University researchers have developed an algorithm to improve the speed and accuracy of medical diagnoses using smartphone sensors.

Although smartphone cameras capture only red, green, and blue wavelengths of light in each pixel, the researchers were able to reconstruct the full spectrum of visible light in each pixel of an image taken by a smartphone camera using deep learning, statistical techniques, and an understanding of light-tissue interactions.

The researchers found their technology produced information about blood oxygen levels in study participants' eyelids faster than commercially available hyperspectral imaging equipment, while being less expensive and just as accurate.

From Purdue University

View Full Article   

Generative AI at Mayo

AI in Healthcare at Mayo Clinic.  A good example for an example of data workflow.  

Mayo Clinic Partners With Google Cloud to Bring Generative AI to Healthcare

ERIC HAL SCHWARTZ on June 8, 2023 at 8:00 am

The Mayo Clinic is working on enhancing its work using generative AI as part of a new collaboration with Google. The healthcare center’s first project aims to speed up its research by integrating Google Cloud’s Enterprise Search in Generative AI App Builder within its clinical workflows. In tandem with the Mayo Clinic deal, Google Cloud announced that the Gen App Builder can now create systems capable of complying with HIPAA.

GENERATIVE MAYO

Google’s Gen App Builder offers an enterprise search feature useful for collating and finding data from a wide range of sources. Since doctors and medical professionals must look through patient records, research papers, hospital protocols, and other databases to plan any treatment or diagnosis plan, an AI assistant that can streamline the process has an obvious appeal. The HIPAA compliance Google Cloud’s platform now supports is crucial for the Mayo Clinic or any other healthcare provider, however, as they would want to ensure data security and privacy before they start linking generative AI products to patient records.

“Our prioritization of patient safety, privacy, and ethical considerations, means that generative AI can have a significant and positive impact on how we work and deliver healthcare,” Mayo Clinic chief information officer Cris Ross explained. “Google Cloud’s tools have the potential to unlock sources of information that typically aren’t searchable in a conventional manner, or are difficult to access or interpret, from a patient’s complex medical history to their imaging, genomics, and labs. Accessing insights more quickly and easily could drive more cures, create more connections with patients, and transform healthcare.”  .. ' 

Super Low-Cost Smartphone Attachment Brings Blood Pressure Monitoring to Your Fingertips

 Remember being told this was effectively impossible.

Super Low-Cost Smartphone Attachment Brings Blood Pressure Monitoring to Your Fingertips

By UC San Diego Today, May 31, 2023    A prototype of the blood pressure monitoring clip.

Researchers say the new blood pressure clip could help make regular blood pressure monitoring easy, affordable, and accessible to people in resource-poor communities.

An inexpensive plastic fingertip clip and custom smartphone app developed by University of California, San Diego researchers works with a smartphone's camera and flash to measure the user's blood pressure.

The goal is to make blood pressure monitoring more affordable and accessible.

The clip, which is placed over the smartphone's camera and flash, does not have to be calibrated to a cuff.

The smartphone app can measure the amount of pressure applied by the fingertip and the volume of blood moving in and out of the fingertip; it then converts this data into systolic and diastolic blood pressure readings using an algorithm.

From UC San Diego Today

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With Electronics in His Brain, Spine, Paralyzed Man Takes a Stride

 With Electronics in His Brain, Spine, Paralyzed Man Takes a Stride

The Washington Post

Daniel Gilbert, May 24, 2023

An international team of scientists and neurosurgeons implanted electronics into the brain and spinal cord of a paralyzed man that enable him to walk, and to climb stairs. Explained Grégoire Courtine at the Swiss Federal Institute of Technology, Lausanne, "We have created a wireless interface between the brain and the spinal cord using brain-computer interface technology that transforms thought into action." The system incorporates a device implanted in the skull above the brain's surface, which decodes patterns involved in walking and sends a signal to a second device implanted along the spinal cord. Electrodes activate the spinal cord sequentially to trigger leg muscles for walking. ... '

AI Powers Second-Skin-Like Wearable Tech

Biometric signals,

 AI Powers Second-Skin-Like Wearable Tech

Monash University (Australia),  May 19, 2023

Scientists at Australia's Monash University and the Melbourne Center for Nanofabrication integrated nanotechnology and artificial intelligence (AI) into an ultra-thin skinpatch that monitors 11 biometric signals. The researchers engineered the Deep Hybrid-Spectro frequency/amplitude-based neural network to track multiple biometrics transmitted by the skinpatch in a single signal. Monash's Wenlong Cheng said the layered, neck-worn patch can measure speech, neck movement, touch, respiration, and heart rate. Cheng explained, "Emerging soft electronics have the potential to serve as second-skin-like wearable patches for monitoring human health vitals, designing perception robotics, and bridging interactions between natural and artificial intelligence."

Article

Data Compression Scheme Facilitates Measurement of Blood Flow to the Brain

Healthcare

Data Compression Scheme Facilitates Measurement of Blood Flow to the Brain

SPIE, May 8, 2023

A data compression scheme developed by researchers at the U.K.'s University of Edinburgh allows most calculations involving single-photon avalanche diode (SPAD) data to be performed directly on a field-programmable gate array (FPGA). This paves the way for improvements in multispeckle diffuse correlation spectroscopy (DCS) techniques to better measure blood flow to the brain. The researchers connected a 192-by-128-pixel SPAD sensor array, packaged into the Quanticam camera module, to an FPGA and applied an autocorrelation algorithm. This allowed the computational burden of calculating autocorrelations to be shifted from the host computing system to the hardware linked directly to the SPAD sensors. University of Edinburgh's Robert K. Henderson said, "Our proposed system achieved a significant gain in the signal-to-noise ratio, which is 110 times higher than that possible on a single-speckle DSC implementation and three times higher than other state-of-the-art multispeckle DSC systems."

Google Enters Palm 2

The internals are getting weirder.

https://youtu.be/u_dSUtp4eM8    

Enter PaLM 2 (New Bard): Full Breakdown - 92 Pages Read and Gemini Before GPT 5? Google I/O

AI Explained

138K subscribers, Subscribed

62,701 views  May 11, 2023

Google puts it foot on the accelerator, casting aside safety concerns to not only release a GPT 4 -competitive model, PaLM 2, but also announce that they are already training Gemini, a GPT 5 competitor [likely on TPU v5 chips]. This is truly a major day in AI history, and I try to cover it all. 

I'll show the benchmarks in which PaLM (which now powers Bard) beats GPT 4, and detail how they use SmartGPT-like techniques to boost performance. Crazily enough, PaLM 2 beats even Google Translate, due in large part to the text it was trained on. We'll talk coding in Bard, translation, MMLU, Big Bench, and much more.

I'll end on the Universal Translator deepfakes and the underwhelming results from Sundar Pichai and Sam Altman's trip to the White House and what Hinton says about it all. On a more positive note, I cover Med PaLM 2, which could genuinely save thousands of lives.   .. 

AI-Powered Diagnostic Tool Predicts Pancreatic Cancer Up to 3 Years in Advance

Given how difficult pancreatic cancer can be to diagnose, the tool could be life-saving.

AI-Powered Diagnostic Tool Predicts Pancreatic Cancer Up to 3 Years in Advance

By Adrianna Nine  in Extremetech

Pancreatic cancer might be relatively rare, but it’s one of the world’s leading causes of cancer-related death. Thanks to the organ’s placement deep within the abdomen, tumors on the pancreas can be difficult to detect, forcing doctors to rely on expensive and invasive imaging or blood tests instead. If these strategies are implemented too late, a patient’s odds of successfully completing treatment are slim.

A new tool powered by artificial intelligence might make early detection easier and more accessible. Researchers from Harvard Medical School and the University of Copenhagen partnered with the VA Boston Healthcare System and the Dana-Farber Cancer Institute to build a screening program that scans people’s medical records to determine their likelihood of developing pancreatic cancer. According to a paper published Tuesday in Nature Medicine, the tool can predict future pancreatic cancer diagnosis up to three years ahead with 88% accuracy.

The researchers trained an AI model on roughly 9 million patients’ health records obtained from the Danish National Patient Registry (DNPR) and the US Veterans Affairs database (US-VA). Both of these databases were used to train the model separately. Of the 6 million patients in the DNPR, 23,985 had been diagnosed with pancreatic cancer; of the 3 million US-VA patients, 3,900 had received the same diagnosis. Using this data, the AI model was trained to pick up on disease codes, comorbidities, and timelines that might indicate a future pancreatic cancer diagnosis—even if the disease codes themselves had nothing to do with the pancreas. ... ' 

Can AI save lives? Cancer Detection Study Suggests Yes

Cancer Detection

Can AI save lives? Cancer detection study suggests yes

New research shows algorithm efficient at detecting recurrence in high-risk patients

May 1, 2023 - 5:00 pm

Much of the world may currently be fretting about how to limit the impact (lack of privacy, copyright issues, loss of jobs, world domination, etc.) of artificial intelligence. However, that does not mean that there isn’t enormous potential for AI to improve quality of life on earth. 

One such application is healthcare. With the ability to process big data sets, the deployment of AI could lead to significant advances in predictive diagnostics, including early detection of cancer. While more research is needed, one of the latest studies in the field shows promising results for AI-assisted diagnosis of lung cancer. 

Doctors and researchers at the Royal Marsden NHS foundation trust, the Institute of Cancer Research, and Imperial College London have built an AI algorithm they say can diagnose cancerous growths more efficiently than current methods. 

In the study named OCTAPUS-AI, researchers used imaging and clinical data from over 900 patients from the UK and Netherlands following curative radiotherapy to develop and test ML algorithms to see how accurately the models could predict recurrence. 

Specifically, the study looked at if AI could help identify the risk of cancer returning in non-small cell lung cancer (NSCLC) patients. Researchers used CT scans to develop an AI algorithm using radiomics. This is a quantitative approach which extracts novel data and predictive biomarkers from medical imaging. 

Research algorithm superior to current technology

NSCLC patients make up 85% of lung cancer cases. While the disease is often treatable when caught early, in over a third of patients, the cancer returns. The study found that using the algorithm, clinicians may eventually be able to identify recurrence earlier in high-risk patients. 

The scientists used a measure called area under the curve (AUC) to see how efficient the model was at detecting cancer. A perfect 100% accuracy score would be a 1, whereas a model that was purely guessing 50-50 would get 0.5. In the study, the AI algorithm built by the researchers scored 0.87. This can be compared to the 0.67 score of the technology currently in use. 

“Next, we want to explore more advanced machine learning techniques, such as deep learning, to see if we can get even better results,” Dr Sumeet Hindocha, Clinical Oncology Specialist Registrar at The Royal Marsden NHS Foundation Trust, and Clinical Research Fellow at Imperial College London, said. “We then want to test this model on newly diagnosed NSCLC patients and follow them to see if the model can accurately predict their risk of recurrence.”...' 

An ML-based Approach to better Characterize Lung Diseases

Machine Learning at work.

An ML-based approach to better characterize lung diseases

THURSDAY, APRIL 27, 2023

Posted by Babak Behsaz, Software Engineer, and Andrew Carroll, Product Lead, Genomics

The combination of the environment an individual experiences and their genetic predispositions determines the majority of their risk for various diseases. Large national efforts, such as the UK Biobank, have created large, public resources to better understand the links between environment, genetics, and disease. This has the potential to help individuals better understand how to stay healthy, clinicians to treat illnesses, and scientists to develop new medicines.

One challenge in this process is how we make sense of the vast amount of clinical measurements — the UK Biobank has many petabytes of imaging, metabolic tests, and medical records spanning 500,000 individuals. To best use this data, we need to be able to represent the information present as succinct, informative labels about meaningful diseases and traits, a process called phenotyping. That is where we can use the ability of ML models to pick up on subtle intricate patterns in large amounts of data.

We’ve previously demonstrated the ability to use ML models to quickly phenotype at scale for retinal diseases. Nonetheless, these models were trained using labels from clinician judgment, and access to clinical-grade labels is a limiting factor due to the time and expense needed to create them.

In “Inference of chronic obstructive pulmonary disease with deep learning on raw spirograms identifies new genetic loci and improves risk models”, published in Nature Genetics, we’re excited to highlight a method for training accurate ML models for genetic discovery of diseases, even when using noisy and unreliable labels. We demonstrate the ability to train ML models that can phenotype directly from raw clinical measurement and unreliable medical record information. This reduced reliance on medical domain experts for labeling greatly expands the range of applications for our technique to a panoply of diseases and has the potential to improve their prevention, diagnosis, and treatment. We showcase this method with ML models that can better characterize lung function and chronic obstructive pulmonary disease (COPD). Additionally, we show the usefulness of these models by demonstrating a better ability to identify genetic variants associated with COPD, improved understanding of the biology behind the disease, and successful prediction of outcomes associated with COPD.

ML for deeper understanding of exhalation

For this demonstration, we focused on COPD, the third leading cause of worldwide death in 2019, in which airway inflammation and impeded airflow can progressively reduce lung function. Lung function for COPD and other diseases is measured by recording an individual’s exhalation volume over time (the record is called a spirogram; see an example below). Although there are guidelines (called GOLD) for determining COPD status from exhalation, these use only a few, specific data points in the curve and apply fixed thresholds to those values. Much of the rich data from these spirograms is discarded in this analysis of lung function.

We reasoned that ML models trained to classify spirograms would be able to use the rich data present more completely and result in more accurate and comprehensive measures of lung function and disease, similar to what we have seen in other classification tasks like mammography or histology. We trained ML models to predict whether an individual has COPD using the full spirograms as inputs.    .... '

AI and Healthcare

Proactive direction.

9 ways AI is already shaping the future of healthtech    in Fastcompany

Amid an increasing shift from reactive care to proactive care, here are some of the latest AI developments in healthtech.

9 ways AI is already shaping the future of healthtech

[Source images: ALFRED PASIEKA/SCIENCE PHOTO LIBRARY/Getty Images; Towfiqu barbhuiya/Unsplash; National Cancer Institute/Unsplash; National Cancer Institute/Unsplash]

BY LAYA NEELAKANDAN2 MINUTE READ

Over the past few months, developments in AI have opened the doors for improvements in healthtech to more closely and efficiently track and treat illnesses. Such innovations could lead to huge health savings for consumers—according to a 2020 study, the integration of AI in healthtech can cut annual U.S. healthcare costs by $150 billion in 2026. 

And it’s clear in the innovations that have evolved since: In 2022 alone, the Food and Drug Administration approved nearly 100 AI-enabled medical devices, according to data from October 5, ranging in complexity from basic algorithms to entirely machine learning-based tools. 

Hwalthcare and AI

While a recent poll conducted by the Pew Research Center found a majority of Americans would be uncomfortable if their healthcare provider relied on AI, researchers are nonetheless continuing to pivot from reactive health care to proactive care.

Here are nine ways the technology has already made an impact on the healthtech sphere.

AI TOOL TO IDENTIFY CANCER

A tool developed last year by researchers at the Royal Marsden NHS Foundation Trust, the Institute of Cancer Research in London, and Imperial College London has the ability to accurately identify cancerous growths on CT scans.

The researchers say the tool can help doctors make quick decisions and fast-track patients to the best treatment for their illness. While still at an early stage, they emphasized the benefits of the tool are clear. ... ' 

Fraunhofer and Digital Medicine

 Fraunhofer at the DMEA

Fraunhofer to showcase digital healthcare of tomorrow

Press Release / April 05, 2023

Improved patient care, speedier diagnoses and savings in care costs – the digital transformation of the healthcare industry promises solutions to the urgent problems of our time. Increasingly, systems based on artificial intelligence (AI) are being employed. But how can these be used effectively while maintaining compliance with data protection laws? Experts from the Fraunhofer-Gesellschaft will be providing insights into their current work at DMEA 2023 in Berlin and answering questions about tomorrow’s health IT at booth D107 in Hall 2.2.

Fraunhofer to showcase digital healthcare of tomorrow

Health research occupies a prominent position at the Fraunhofer-Gesellschaft. Together with partners from the world of medicine, a number of institutes are working to develop digital solutions for the prevention of disease as well as the diagnosis, therapy and rehabilitation of patients. The aim is to streamline processes and also to make affordable care available for an increasingly aging society. The resulting technologies support players in the healthcare sector, such as clinics and medical staff, as well as patients by providing them with applications for use at home. 

Apps and applications for optimized and individualized treatment

The Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS will be demonstrating AI-based software for the automated processing of medical documents. Advances in AI research are enabling the reliable and comprehensible use of large-scale language models (foundation models) for applications such as the generation of physicians’ letters, information extraction and billing. This saves time in everyday clinical practice and guarantees high-quality treatment.

The Fraunhofer Institute for Computer Graphics Research IGD is making an active contribution to personalized medicine with digital solutions. Its software applications support specialist staff and patients in prevention, diagnostics and therapy. These include a new method for allergy test evaluation on a smartphone and the option of performing visual-interactive data analysis based on cohorts, for example in the area of chronic inflammatory bowel disease. The Guardio® AI-based software package converts heart movements into an ECG while the patient’s own smartphone is placed in proximity to the chest.

The main concern of the Fraunhofer Institute for Cognitive Systems IKS is trust and efficiency when artificial intelligence is employed in medical care. The research team will be presenting new quantum computing approaches in the context of AI to improve the early detection of breast cancer. They will also be showing how routine clinical data and AI can be reliably combined to support doctors in their decision making.

In modern clinical practice, intelligent data integration is becoming increasingly important for medical staff. With the help of new algorithms and innovative AI, more precise diagnoses and personalized therapy plans can be created automatically.

At the DMEA, the Fraunhofer Institute for Digital Medicine MEVIS will present software solutions for data structuring and guideline-based decision-making to interested companies. In addition, MEVIS experts will demonstrate optimized image-based follow-up of cancer treatments using AI.

The Fraunhofer Center for Digital Diagnostics addresses the improvement of medical care in rural areas through patient-oriented diagnostics and digitalization. Data discontinuities in patient care are analyzed and optimized. Healthcare in rural areas is facilitated by the development of a fully automated, mobile health center. Next-generation virus tests will allow for needs-based diagnostics and outbreak containment. Intelligent wound care will enable faster healing of festering wounds.

In Portugal, the Fraunhofer Center for Assistive Information and Communication Solutions AICOS develops technologies for digital healthcare, in which predictive, preventive, personalized and participatory medicine plays a key role. The team will be presenting the results of its work to the German health market at the DMEA. The technologies developed facilitate human intervention, connectivity and collaboration in healthcare. In the matter of decentralized healthcare, the Fraunhofer Center has set itself the goal of improving access to early treatment, supporting clinical decisions with the help of algorithms, generating transparent and explainable AI-based decisions, and minimizing bias and unfairness.  ... ' 

Boosting Photodiode Efficiency to 220%

Towards more efficient power for devices

Boosting Photodiode Efficiency to 220%,   By R. Colin Johnson

Commissioned by CACM Staff, March 16, 2023

Eindhoven University of Technology’s super-sensitive photodiode will be used in sensors for medical monitors, wearables, light communication, health surveillance systems, and machine vision.

Credit: Eindhoven University of Technology

The physics of thermodynamics dictates that a photodiode's "energy efficiency" must be less than 100%, defined as "the ratio between the useful output and input of an energy conversion process." However, in sensor applications, it is the quantum efficiency that is important—the ratio between the outputs (electrons) and inputs (photons), and as such is not limited to 100%.

Until now, the highest quantum efficiency of a photodiode used as a sensor, according to its designers at Eindhoven University of Technology (EUT, Netherlands), was 70%. However, the EUT researchers say their newest invention is a tandem-like photodiode (similar to tandem-layer solar cells) that have a hitherto impossibly high quantum efficiency of 220%—since they output 22 electrons for every 10 photons input.

"The tandem-like architecture developed by this group successfully solves several key challenges in the field of near-infrared detection," said photodiode expert professor Fei Huang at the South China University of Technology, who was not involved in the project. "EUT demonstrated devices with a simple but reliable configuration, achieving excellent narrowband detection, very promising operational stability, as well as very low signal-to-noise ratio."

All photodiodes convert photons into electrons—for uses ranging from the near-infrared (NIR) pulse oximeter sensor your doctor clips on your fingertip, to the visible-light solar panels on your roof. As sensors, photodiodes are optimized for superb sensitivity in reflected NIR detection of heart rate, its variability, and blood oxygen levels, whereas photodiodes for solar cells are optimized for energy conversion efficiency from sunlight to electricity. As sensors, photodiodes are usually tuned to different frequencies, too—typically NIR for medical sensors, instead of the visible light spectrum from the Sun for solar cells.

Tandem photovoltaic cells, on the one hand, harvest photonic energy from two different bands of light coming from the Sun, then combine the resulting two separate electron streams, boosting the overall performance of the tandem solar cells to achieve 20% to 40% energy efficiency.

EUT's tandem-like photodiode, on the other hand, achieves greater than 100% quantum efficiency by separately filling a "reservoir" of extra electrons with self-generated photons from an LED shining on the tandem layer. The electrons harvested there are then "gated" into the output electron stream by the incident NIR photons, thus enhancing quantum efficiency far beyond the highest energy efficiency of any solar cell.

"The efficiency that we are talking about is the quantum efficiency—it counts the number of charges (electrons) that pass a circuit per incident photon. This is not really related to the energy efficiency," said Rene Janssen, professor and leader of the interdepartmental research group called Molecular Materials and Nanosystems at EUT. "For our photodiodes, the quantum efficiency is what counts. For a photovoltaic solar cell, it is the energy efficiency that counts. These are related, but the working principle is entirely different—we cannot claim that the effect that we demonstrate here would boost the efficiency of photovoltaic solar cells."  ... ' 

Arterys: The future of precision medicine

Brought to my Attention , aimed at healthcare practices...

Arterys:  The future of precision medicine   that only human + AI can achieve. 

The Arterys platform extracts actionable insights from medical images to add clinical value, improve diagnostic decision making, efficiency and productivity.

Arterys is the medical imaging AI platform allowing you to weave leading AI clinical applications directly into your existing PACS or EHR driven workflow to make it a natural extension of what you already do.

We are making AI real by improving physician experience, accuracy of diagnosis and treatment, financial performance and outcomes that matter to patients and providers.

Accessible anywhere from any validated device via the cloud for faster performance, ease of deployment with no PHI exchange and completely secure.

See how Arterys is transforming healthcare through deep learning and AI

History:  It started at Stanford

Before we founded Arterys, we were graduate students, searching for a way to make medicine better. Faster. More precise.

We pushed the boundaries of our disciplines until they finally overlapped, intersecting cloud computing with cutting edge medical image acquisition.

Soon after, Arterys was founded on our shared beliefs:

Faster, smarter diagnosis where it counts the most

We started by improving diagnoses for newborns and kids with heart defects. At the time, pediatric cardiovascular disease was diagnosed with ultrasound, which only offers a partial view of the heart with no blood flow quantifications. Or an MRI captured over grueling hours on the scanning table.

Without accurate quantification, cardiac medicine was an educated guess with a high error rate.

The solution was obvious: 4D Flow technology to visualize and quantify blood flow in mere minutes.

But image archiving servers in hospitals couldn’t read 4D Flow’s big data files. So we applied a system that could: cloud computer processing.

With cloud computing, we could put those life-saving 4D Flow images into radiologists hands. In mere moments, physicians could diagnose and make accurate treatment decisions.

Anyone with access to a web browser could access Arterys to quantify regurgitant flow and determine if a child with heart defects needed surgery.

We were helping to save kids’ lives with better diagnoses.

But we weren’t satisfied.

Even though we put 4D Flow at radiologists’ fingertips, we still saw physicians manually drawing contours to quantify the size of cardiac ventricles.

Artificial Intelligence gets real

We decided to power Deep Learning AI with cloud computing GPUs to automatically quantify and segment ventricles as accurately as manual measurements by experienced physicians. In 2017, our technology received the first ever US FDA clearance for leveraging cloud computing and deep learning in a clinical setting.

But we were still not satisfied.

Physicians around the world approached us, asking if we could apply the same AI cloud computing approach to image processing and analysis to cancer patients. Then to liver patients. Then lung, breast, brain, and everything in between.

The word was getting out that radiologists could receive automatic accurate measurements through deep learning powered in the cloud.

All of your data, all in one place. Simple.

Despite our breakthroughs, existing radiology workflows put AI image acquisition and analysis out of reach. Working outside of a unified platform, physicians switched interfaces multiple times an hour to access different AI imaging tools. This stole vital time that could be spent with a patient. It was time to transcend silos with one united AI web platform.

Soon after, we received FDA clearance for our Arterys web-based AI imaging platform.

Despite these successes, we are still not satisfied.

We want to push medicine to make it even faster and more precise. We’ve just gotten started  .... ..'

Minirobot Enters Blood Vessels, Complete Surgery

 Healthcare robotics advance.

Mini Robot Enters Blood Vessels, Completes Surgery

By IEEE Spectrum, March 10, 2023

Miniature robots traveling through blood vessels could offer a more precise and easy way to access an internal treatment site than estimating the position of a catheter from outside the patient’s body using X-rays,

Researchers at South Korea's Hanyang University recently demonstrated that a miniature robot could travel autonomously to a superficial femoral artery in a pig, deliver contrast dye, and return safely to the extraction point.

This could pave the way for the use of robots to treat occlusive vascular disease in humans and eliminate the need for X-ray imaging to guide surgical equipment.

The researchers developed the I-RAMAN (robotically assisted magnetic navigation system for endovascular intervention) robot, which can navigate a patient's blood vessels autonomously using a three-dimensional map generated from two-dimensional X-ray images.

The robot is injected into a blood vessel via catheter, then an external magnetic field untethers the robot from the catheter and guides it to the treatment area and back.

From IEEE Spectrum

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