Going beyond the means: Exploring the role of bias from digital determinants of health in technologies.

BACKGROUND: In light of recent retrospective studies revealing evidence of disparities in access to medical technology and of bias in measurements, this narrative review assesses digital determinants of health (DDoH) in both technologies and medical formulae that demonstrate either evidence of bias or suboptimal performance, identifies potential mechanisms behind such bias, and proposes potential methods or avenues that can guide future efforts to address these disparities. APPROACH: Mechanisms are broadly grouped into physical and biological biases (e.g., pulse oximetry, non-contact infrared thermometry [NCIT]), interaction of human factors and cultural practices (e.g., electroencephalography [EEG]), and interpretation bias (e.g, pulmonary function tests [PFT], optical coherence tomography [OCT], and Humphrey visual field [HVF] testing). This review scope specifically excludes technologies incorporating artificial intelligence and machine learning. For each technology, we identify both clinical and research recommendations. CONCLUSIONS: Many of the DDoH mechanisms encountered in medical technologies and formulae result in lower accuracy or lower validity when applied to patients outside the initial scope of development or validation. Our clinical recommendations caution clinical users in completely trusting result validity and suggest correlating with other measurement modalities robust to the DDoH mechanism (e.g., arterial blood gas for pulse oximetry, core temperatures for NCIT). Our research recommendations suggest not only increasing diversity in development and validation, but also awareness in the modalities of diversity required (e.g., skin pigmentation for pulse oximetry but skin pigmentation and sex/hormonal variation for NCIT). By increasing diversity that better reflects patients in all scenarios of use, we can mitigate DDoH mechanisms and increase trust and validity in clinical practice and research.

Optimization of Oligomer Stamping Technique for Normally Closed Elastomeric Valves on Glass Substrate.

Microscale elastomeric valves are an integral part of many lab-on-chip applications. Normally closed valves require lower actuation pressures to form tight seals, making them ideal for portable devices. However, fabrication of normally closed valves is typically more difficult because the valve structure must be selectively bonded to its substrate. In this work, an oligomer stamping technique for selective bonding of normally closed valves is optimized for bonding of PDMS devices on glass substrates. Contact angle and blister bursting testing measurements are used to quantitatively characterize the oligomer stamping process for the first time, and recommendations are made for plasma treatment conditions, microstamping technique, and valve construction. Glass-PDMS devices are ideal for lab-on-chip systems that integrate electrodes on the rigid glass substrate. Here, integrated electrodes are used to assess valve performance, demonstrating electrical isolation in excess of 8 MΩ over the biologically relevant frequency range in the closed state. Further, electrical measurement is used to demonstrate that the valve design can operate under a pulsed actuation scheme, sealing to withstand fluid pressures in excess of 200 mbar.

Development of a Mobile Application Platform for Self-Management of Obesity Using Artificial Intelligence Techniques.

Obesity is a major global health challenge and a risk factor for the leading causes of death, including heart disease, stroke, diabetes, and several types of cancer. Attempts to manage and regulate obesity have led to the implementation of various dietary regulatory initiatives to provide information on the calorie contents of meals. Although knowledge of the calorie content is useful for meal planning, it is not sufficient as other factors, including health status (diabetes, hypertension, etc.) and level of physical activity, are essential in the decision process for obesity management. In this work, we present an artificial intelligence- (AI-) based application that is driven by a genetic algorithm (GA) as a potential tool for tracking a user's energy balance and predicting possible calorie intake required to meet daily calorie needs for obesity management. The algorithm takes the users' input information on desired foods which are selected from a database and extracted records of users on cholesterol level, diabetes status, and level of physical activity, to predict possible meals required to meet the users need. The micro- and macronutrients of food content are used for the computation and prediction of the potential foods required to meet the daily calorie needs. The functionality and performance of the model were tested using a sample of 30 volunteers from the University of Ghana. Results revealed that the model was able to predict both glycemic and non-glycemic foods based on the condition of the user as well as the macro- and micronutrients requirements. Moreover, the system is able to adequately track the progress of the user's weight loss over time, daily nutritional needs, daily calorie intake, and predictions of meals that must be taken to avoid compromising their health. The proposed system can serve as a useful resource for individuals, dieticians, and other health management personnel for managing obesity, patients, and for training students in fields of dietetics and consumer science.

A CMOS-Based Bidirectional Brain Machine Interface System With Integrated fdNIRS and tDCS for Closed-Loop Brain Stimulation.

A CMOS-based bidirectional brain machine interface system with on-chip frequency-domain near infrared spectroscopy (fdNIRS) and transcranial direct-current stimulation (tDCS) is designed to enable noninvasive closed-loop brain stimulation for neural disorders treatment and cognitive performance enhancement. The dual channel fdNIRS can continuously monitor absolute cerebral oxygenation during the entire tDCS process by measuring NIR light's attenuation and phase shift across brain tissue. Each fdNIRS channel provides 120 dBΩ transimpedance gain at 80 MHz with a power consumption of 30 mW while tolerating up to 8 pF input capacitance. A photocurrent between 10 and 450 nA can be detected with a phase resolution down to 0.2°. A lensless system with subnanowatt sensitivity is realized by using an avalanche photodiode. The on-chip programmable voltage-controlled resistor stimulator can support a stimulation current from 0.6 to 2.2 mA with less than 1% variation, which covers the required current range of tDCS. The chip is fabricated in a standard 130-nm CMOS process and occupies an area of 2.25 mm2.

Optical Characterization of Tissue Phantoms Using a Silicon Integrated fdNIRS System on Chip.

An interface circuit with signal processing and digitizing circuits for a high frequency, large area avalanche photodiode (APD) has been integrated in a 130 nm BiCMOS chip. The system enables the absolute oximetry of tissue using frequency domain Near Infrared Spectroscopy (fdNIRS). The system measures the light absorbed and scattered by the tissue by measuring the reduction in the amplitude of signal and phase shift introduced between the light source and detector which are placed a finite distance away from each other. The received 80 MHz RF signal is downconverted to a low frequency and amplified using a heterodyning scheme. The front-end transimpedance amplifier has a 3-level programmable gain that increases the dynamic range to 60 dB. The phase difference between an identical reference channel and the optical channel is measured with a 0.5° accuracy. The detectable current range is [Formula: see text] and with a 40 A/W reponsivity using the APD, power levels as low as 500 pW can be detected. Measurements of the absorption and reduced scattering coefficients of solid tissue phantoms using this system are compared with those using a commercial instrument with differences within 30%. Measurement of a milk based liquid tissue phantom show an increase in absorption coefficient with addition of black ink. The miniaturized circuit serves as an efficiently scalable system for multi-site detection for applications in neonatal cerebral oximetry and optical mammography.