Browse Topic: Diagnosis
Using an inexpensive electrode coated with DNA, MIT researchers have designed disposable diagnostics that could be adapted to detect a variety of diseases, including cancer or infectious diseases such as influenza and HIV.
Electric Vehicles (EVs) are rapidly transforming the automotive landscape, offering a cleaner and more sustainable alternative to internal combustion engine vehicles. As EV adoption grows, optimizing energy consumption becomes critical to enhancing vehicle efficiency and extending driving range. One of the most significant auxiliary loads in EVs is the climate control system, commonly referred to as HVAC (Heating, Ventilation, and Air Conditioning). HVAC systems can consume a substantial portion of the battery's energy—especially under extreme weather conditions—leading to a noticeable reduction in vehicle range. This energy demand poses a challenge for EV manufacturers and users alike, as range anxiety remains a key barrier to widespread EV acceptance. Consequently, developing intelligent climate control strategies is essential to minimize HVAC power consumption without compromising passenger comfort. These strategies may include predictive thermal management, cabin pre-conditioning, zonal climate control, and integration with renewable energy sources. By implementing such energy-efficient solutions, EVs can achieve better range performance, improved user satisfaction, and greater environmental benefits. Modern EV climate control systems increasingly rely on intelligent features such as Auto mode, which dynamically adjusts fan speed, airflow direction, and temperature settings based on real-time cabin and ambient conditions. By leveraging sensor data and adaptive control algorithms, Auto mode optimizes thermal comfort while minimizing unnecessary energy expenditure. This automated regulation plays a crucial role in reducing HVAC-related power consumption, thereby contributing to overall range improvement and enhancing system efficiency without compromising passenger comfort. This study focuses on the development of three distinct Auto mode calibration levels for each set condition, designed to achieve the same cabin temperature with varying dynamic responses and energy consumption profiles. In Auto mode, the cabin temperature is regulated through intelligent control of compressor speed, blower speed, and evaporator temperature. While all Auto levels can maintain the desired setpoint, the time required to reach this temperature and the system’s responsiveness to sudden thermal loads can vary significantly. This study introduces three distinct calibration profiles, each engineered to achieve the same cabin temperature under different dynamic conditions and energy consumption levels. These profiles allow users to choose between faster thermal response or reduced power usage, effectively enabling a trade-off between immediate comfort and extended driving range
Scientists used a “smart” shirt equipped with an electrocardiogram to track participants’ heart-rate recovery after exercise and developed a tool for analyzing the data to predict those at higher or lower risk of heart-related ailments.
This study introduces a novel in-cabin health monitoring system leveraging Ultra-Wideband (UWB) radar technology for real-time, contactless detection of occupants' vital signs within automotive environments. By capturing micro-movements associated with cardiac and respiratory activities, the system enables continuous monitoring without physical contact, addressing the need for unobtrusive vehicle health assessment. The system architecture integrates edge computing capabilities within the vehicle's head unit, facilitating immediate data processing and reducing latency. Processed data is securely transmitted via HTTPS to a cloud-based backend through an API Gateway, which orchestrates data validation and routing to a machine learning pipeline. This pipeline employs supervised classifiers, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF) to analyze features such as temporal heartbeat variability, respiration rate stability, and heart rate. Empirical evaluations demonstrate the system's proficiency in classifying occupant states, including normal, distressed, and unconscious conditions, achieving high prediction accuracy with low false positive rates. Notably, the system attains sub-10-second detection latency and facilitates end-to-end response actions within a 5-minute window. Experimental deployment in a Mercedes vehicle demonstrated high accuracy in occupancy detection (97%), vital sign monitoring (94%), and full ERS (Emergency Response System) activation within five minutes, meeting Euro NCAP 2025+ Child Presence Detection (CPD) requirements. Furthermore, the cloud infrastructure supports the accumulation of health data, contributing to personalized driver profiles and informed decision-making for future interventions. This research underscores the potential of UWB radar technology in augmenting automotive safety through real-time health monitoring, paving the way for smarter and more secure vehicular environments.
A noninvasive imaging system combines two advanced techniques to examine both the structure and chemical composition of skin cancers. This approach could improve how doctors diagnose and classify skin cancer and how they monitor treatment responses.
Devices made with cheap strips of paper have outperformed two other testing methods in detecting malaria infection in asymptomatic people in Ghana — a diagnostic advance that could accelerate efforts to eliminate the disease, researchers say.
“Big iron” instruments, aka diagnostic radiology equipment such as x-ray, ultrasound, and CT scanners, are indispensable for diagnosing and guiding treatment for an array of conditions from tumors to arthritis to fractures. While a tremendous asset for hospitals, these instruments are traditionally large, heavy, power hungry, and expensive. They are also difficult to acquire, install, and use.
Chronic stress can lead to increased blood pressure and cardiovascular disease, decreased immune function, depression, and anxiety. Unfortunately, the tools we use to monitor stress are often imprecise or expensive, relying on self-reporting questionnaires and psychiatric evaluations.
Advancements in sensor technologies have led to increased interest in detecting and diagnosing “driver states”—collections of internal driver factors generally associated with negative driving performance, such as alcohol intoxication, cognitive load, stress, and fatigue. This is accomplished using imperfect behavioral and physiological indicators that are associated with those states. An example is the use of elevated heart rate variability, detected by a steering wheel sensor, as an indicator of frustration. Advances in sensor technologies, coupled with improvements in machine learning, have led to an increase in this research. However, a limitation is that it often excludes naturalistic driving environments, which may have conditions that affect detection. For example, reductions in visual scanning are often associated with cognitive load [1]; however, these reductions can also be related to novice driver inexperience [2] and alcohol intoxication [3]. Through our analysis of the research, we discover that the tendency to explore these singular driver states with only a comparison to “normal” driving is common. Additionally, research on interventions for these driver states is relatively scarce (fewer than 10% of cognitive load-related papers we examined assessed or discussed intervention solutions) and narrowly tailored to specific states [e.g., 4, vis-à-vis cognitive load]. States that share common behavioral and physiological markers tend to be explored independently when a more universal and integrated approach may be warranted. In this paper, we identify the need for a driver state and intervention framework that addresses these limitations by exploring state indicators and their overlap, interventions for one or multiple states, and major research gaps. Our framework offers practical approaches for handling one or many driver states, including interventions that may be deployed at different timings during a trip.
Every year, more than 5 million people in the United States are diagnosed with heart valve disease, but this condition has no effective long-term treatment. When a person’s heart valve is severely damaged by a birth defect, lifestyle, or aging, blood flow is disrupted. If left untreated, there can be fatal complications.
Researchers from the School of Engineering of the Hong Kong University of Science and Technology (HKUST) have successfully developed what they believe is the world’s smallest multifunctional biomedical robots. Capable of imaging, high-precision motion, and multifunctional operations like sampling, drug delivery, and laser ablation, the robot offers competitive imaging performance and a tenfold improvement in obstacle detection, paving the way for robotic applications in narrow and challenging channels of the human body, such as the lung’s end bronchi and the oviducts.
Biosensors are devices that can monitor physiological states, like heart rate or blood pressure, or detect biological parameters such as glucose levels or the presence of specific proteins in the blood. The information biosensors collect can be used to support a medical diagnosis (for instance, a specific infection) or to provide feedback to the user on parameters of interest (for instance, the number of calories burned in a workout).
Researchers have developed an optical biosensor that can rapidly detect monkeypox, the virus that causes mpox. The technology could allow clinicians to diagnose the disease at the point of care rather than wait for lab results.
In a world grappling with a multitude of health threats — ranging from fast-spreading viruses to chronic diseases and drug-resistant bacteria — the need for quick, reliable, and easy-to-use home diagnostic tests has never been greater. Imagine a future where these tests can be done anywhere, by anyone, using a device as small and portable as your smartwatch. To do that, you need microchips capable of detecting minuscule concentrations of viruses or bacteria in the air.
The emergence of data-driven healthcare promises predictive and preventive care through enhanced data integration and analytics. This trend means that medical device companies must navigate challenges related to data privacy and operational efficiency while transitioning to a data-centric approach. Artificial intelligence (AI) is spearheading this shift toward hyper-personalized medicine, enabling precision treatments based on genetic profiles and predictive analytics for early disease detection. Advancements in telemedicine, AI, wearable technology, and data analytics, are reshaping how care is delivered, making it more accessible, personalized, and efficient in 2025.
A new handheld, sound-based diagnostic system can deliver precise results in an hour with a mere finger prick of blood. The researchers used tiny particles they call functional negative acoustic contrast particles (fNACPs) and a custom-built, handheld instrument or acoustic pipette that delivers sound waves to the blood samples inside.
Researchers have created a portable device that can detect colorectal and prostate cancer more cheaply and quickly than prevailing methods. The team believes the device may be especially helpful in developing countries, which experience higher cancer mortality rates due in part to barriers to medical diagnosis.
A team of researchers at the University of California – San Diego has developed a new and improved wearable ultrasound patch for continuous and noninvasive blood pressure monitoring. Their work marks a major milestone, as the device is the first wearable ultrasound blood pressure sensor to undergo rigorous and comprehensive clinical validation on over 100 patients.
Wearable devices like smartwatches and fitness trackers interact with parts of our bodies to measure and learn from internal processes, such as our heart rate or sleep stages. Now, MIT researchers have developed wearable devices that may be able to perform similar functions for individual cells inside the body.
The healthcare industry is evolving and facing two major challenges. First, the rise of chronic diseases. By 2050, chronic diseases such as cardiovascular diseases, cancer, diabetes, and respiratory illnesses could account for 86 percent of the 90 million deaths each year, according to the World Health Organization (WHO) in its 2023 World Health Statistics report. This increase is due to factors such as an aging population, lifestyle changes, and risk factors like high blood pressure, high blood sugar, and air pollution. Consequently, this creates a second challenge: added strain on healthcare resources. To address this, WHO recommends tackling the root causes of chronic diseases, promoting healthier behaviors, and ensuring universal access to healthcare resources.
Dopamine, a neurotransmitter in our brains, not only regulates our emotions but also serves as a biomarker for the screening of certain cancers and other neurological conditions.
Solving a decades-old problem, a multi-disciplinary team of Caltech researchers has figured out a method to noninvasively and continually measure blood pressure anywhere on the body with next to no disruption to the patient. A device based on the new technique holds the promise to enable better vital-sign monitoring at home, in hospitals, and possibly even in remote locations where resources are limited.
In the realm of ear health, accurate diagnosis is crucial for effective treatment, especially when dealing with conditions that can lead to hearing loss. Traditionally, otolaryngologists have relied on the otoscope, a device that provides a limited view of the eardrum’s surface. This conventional tool, while useful, has its limitations, particularly when the tympanic membrane (TM) is opaque due to disease.
Nanosensors are transforming the field of disease detection by offering unprecedented sensitivity, precision, and speed in identifying biomarkers associated with various health conditions. These tiny sensors, often built at the molecular or atomic scale, can detect minute changes in biological samples, enabling the early diagnosis of diseases such as cancer, infectious diseases, and neurological disorders.
Small wearable or implantable electronics could help monitor our health, diagnose diseases, and provide opportunities for improved, autonomous treatments. But to do this without aggravating or damaging the cells around them, these electronics will need to not only bend and stretch with our tissues as they move, but also be soft enough that they will not scratch and damage tissues.
A wearable health monitor can reliably measure levels of important biochemicals in sweat during physical exercise. The 3D-printed monitor could someday provide a simple and non-invasive way to track health conditions and diagnose common diseases, such as diabetes, gout, kidney disease or heart disease.
iMotions employs neuroscience and AI-powered analysis tools to enhance the tracking, assessment and design of human-machine interfaces inside vehicles. The advancement of vehicles with enhanced safety and infotainment features has made evaluating human-machine interfaces (HMI) in modern commercial and industrial vehicles crucial. Drivers face a steep learning curve due to the complexities of these new technologies. Additionally, the interaction with advanced driver-assistance systems (ADAS) increases concerns about cognitive impact and driver distraction in both passenger and commercial vehicles. As vehicles incorporate more automation, many clients are turning to biosensor technology to monitor drivers' attention and the effects of various systems and interfaces. Utilizing neuroscientific principles and AI, data from eye-tracking, facial expressions and heart rate are informing more effective system and interface design strategies. This approach ensures that automation advancements improve rather than hinder the driving experience.
Recent advances in technology have opened many possibilities for using wearable and implantable sensors to monitor various indicators of patient health. Wearable pressure sensors are designed to respond to very small changes in bodily pressure, so that physical functions such as pulse rate, blood pressure, breathing rates, and even subtle changes in vocal cord vibrations can be monitored in real time with a high degree of sensitivity.
Remember that party where you were swinging glow sticks above your head or wearing them as necklaces? Fun times, right? Science times, too. Turns out those fun party favors are now being used by a University of Houston researcher to identify emerging biothreats for the United States Navy.
Avoiding lethal outcomes from sepsis — a severe, life-threatening reaction to infection within the body — requires a rapid, accurate diagnosis. Historically, it has been a challenge for healthcare providers to beat the clock and intervene with life-saving care. This has contributed to the disease’s lethality, making sepsis the leading cause of hospital-related deaths in the United States.
Pump systems are ubiquitous in medical and life science products, from blood pressure monitors and drug-delivery devices, to pipettors and diagnostic instruments. As the demand for smaller, less intrusive — sometimes even wearable — products grow, engineers must meet these expectations without compromising on pump system performance.
Processes and structures within the body that are normally hidden from the eye can be made visible through medical imaging. Scientists use imaging to investigate the complex functions of cells and organs and search for ways to better detect and treat diseases. In everyday medical practice, images from the body help physicians diagnose diseases and monitor whether therapies are working. To be able to depict specific processes in the body, researchers are developing new techniques for labelling cells or molecules so that they emit signals that can be detected outside the body and converted into meaningful images. A research team at the University of Münster has now adapted a cell labelling strategy currently used in microscopy — the so-called SNAP-tag technology — for use in whole-body imaging with positron emission tomography (PET).
Today’s necessity to reduce healthcare costs is generating a greater demand for medical electronics equipment that improves and expands patient diagnostics inside and outside healthcare facilities. For instance, portable medical instruments such as glucose meters, blood pressure monitors, oxygen meters, and automated external defibrillators (AED) have undergone many design considerations to be developed for doctors, paramedics, public use, and at home with at-risk patients. This article delves into the design considerations, challenges, and regulatory aspects of medical electronics and provides a case study involving automated external defibrillators (AEDs).
Engineers at MIT and Caltech have demonstrated an ingestible sensor whose location can be monitored as it moves through the digestive tract, an advance that could help doctors more easily diagnose gastrointestinal motility disorders such as constipation, gastroesophageal reflux disease, and gastroparesis.
Made with a laser-modified graphene nanocomposite material, a wearable device can detect specific glucose levels in sweat for three weeks while simultaneously monitoring body temperature and pH levels.
Patients with dizziness problems can now get better diagnosis in a simple and painless way. A new type of bone conduction speaker, easily attached behind the ear, can make the diagnosis more efficient and safer — especially for patients who also suffer from hearing problems. The technology has been developed by researchers at Chalmers University of Technology, Sweden, and is now ready for manufacturing.
Scientists have created a new way to detect the proteins that make up the pandemic coronavirus as well as antibodies against it. They designed protein-based biosensors that glow when mixed with components of the virus or specific COVID-19 antibodies. This could enable faster and more widespread testing in the near future.
Healthcare facilities and providers are the beneficiaries of impressive advances in therapeutic technologies that push the boundaries of what’s possible in patient care. Wearable devices have evolved from tracking personal fitness statistics to functioning as a direct conduit from patients to providers for diagnostic, monitoring, and treatment analysis. Artificial intelligence (AI) is currently a major driver of imaging, diagnosis, and treatment of oncology-, cardiac-, and diabetes-related conditions, among many others.
Inadequate real-time tissue assessment of biopsies from different cell types, like cancer cells, immune cells, granuloma, and others, forces proceduralists, such as bronchoscopists and radiologists, to choose between intraprocedural partial tissue adequacy assessment, rapid on-site evaluation (ROSE), or sending tissue samples for full pathology review. Neither truly answers the question, “Do we have enough cells to submit to pathology for the best chance of a conclusive diagnosis?” This can lead to prolonged delay for patient results, the need for a redo procedure, and potential delays for treatment options for the patient.
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