Development of an artificial intelligence-derived histologic signature associated with adjuvant gemcitabine treatment outcomes in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) has been left behind in the evolution of personalized medicine. Predictive markers of response to therapy are lacking in PDAC despite various histological and transcriptional classification schemes. We report an artificial intelligence (AI) approach to histologic feature examination that extracts a signature predictive of disease-specific survival (DSS) in patients with PDAC receiving adjuvant gemcitabine. We demonstrate that this AI-generated histologic signature is associated with outcomes following adjuvant gemcitabine, while three previously developed transcriptomic classification systems are not (n = 47). We externally validate this signature in an independent cohort of patients treated with adjuvant gemcitabine (n = 46). Finally, we demonstrate that the signature does not stratify survival outcomes in a third cohort of untreated patients (n = 161), suggesting that the signature is specifically predictive of treatment-related outcomes but is not generally prognostic. This imaging analysis pipeline has promise in the development of actionable markers in other clinical settings where few biomarkers currently exist.

Non-Contact Type Pulse Oximeter.

Heart rate and through-body blood perfusion are vital measurements in all stages of patient care, be it predictive, in the clinical setting, or outpatient monitoring. Irregular, underachieving, or overperforming heart rate is the main precursor of most cardiovascular diseases that have severe long-term complications. In addition to heart rate, the shape of the pulse waveforms can indicate the heart's valve health and electrophysiology health. The goal of the study was to design a noninvasive device for continuously measuring a patient's heart rate with clinical-grade accuracy along with the ability to indicate pulse waveforms for the patient and physician. An accurate, easy-to-use heart-rate measuring device prototype was developed that did not require the sensor to have direct skin contact to obtain measurements. The statistical analysis of the data gathered by the prototype compared to the data collected from the industry standard device indicated significant correlation. The two-sample T-test for the data recorded from the prototype and the data collected from the industry commercially available pulse oximeter showed a P-value of 0.521, which indicates that there was no significant difference between the prototype and the commercially available pulse oximeter when measuring heart rate.

SeismoWatch: Wearable Cuffless Blood Pressure Monitoring Using Pulse Transit Time.

The current norm for measuring blood pressure (BP) at home is using an automated BP cuff based on oscillometry. Despite providing a viable and familiar method of tracking BP at home, oscillometric devices can be both cumbersome and inaccurate with the inconvenience of the hardware typically limiting measurements to once or twice per day. To address these limitations, a wrist-watch BP monitor was developed to measure BP through a simple maneuver: holding the watch against the sternum to detect micro-vibrations of the chest wall associated with the heartbeat. As a pulse wave propagates from the heart to the wrist, an accelerometer and optical sensor on the watch measure the travel time - pulse transit time (PTT) - to estimate BP. In this paper, we conducted a study to test the accuracy and repeatability of our device. After calibration, the diastolic pressure estimations reached a root-mean-square error of 2.9 mmHg. The watch-based system significantly outperformed (p<0.05) conventional pulse arrival time (PAT) based wearable blood pressure estimations - the most commonly used method for wearable BP sensing in the existing literature and commercial devices. Our device can be a convenient means for wearable BP monitoring outside of clinical settings in both health-conscious and hypertensive populations.1.