An Early Feasibility Study to Explore a Novel Objective Pain Measurement Using a Wearable Biosensor and a Novel Mobile Platform in Patients With Endometriosis (OPINE)

This study aims to explore a novel objective measurement for endometriosis-related pain. A variety of pain symptoms are associated with endometriosis, including dysmenorrhea, dyspareunia, dysuria, dyschezia and chronic pelvic pain. However, a clear characterization of pain typology and topology in populations with endometriosis, other gynecologic pathology, or a normal pelvis is lacking. Understanding the precise nature of the relationship between pain and endometriosis is important for the clinical management of affected women, given the body of evidence indicating that medical and surgical management for pain associated with endometriosis has been shown to be effective. Evaluating the relationship between pain and endometriosis, however, is challenging given that pain is difficult to measure and the mechanism by which endometriosis causes pain is not well understood. While previous studies have provided important data on the incidence of pelvic pain and endometriosis, little research has been done to assess both the typology and topology of pelvic pain, pain beyond the pelvis, endometriosis diagnosis, or severity of pain using operative findings and a standardized classification system.

Start Date06 Nov 2020

Sponsor / Collaborator

Biofourmis Singapore Pte Ltd.

[+1]

NCT03072693

/ Unknown statusNot Applicable

Daily Ambulatory Remote Monitoring System Vs Conventional Therapy For The Post-Dischage Management Of Acute Decompensated Heart Failure

Background: Despite the advances in pharmacological management of heart failure (HF), the associated mortality and re-hospitalization for HF remain poor. This is at least partly due to suboptimal early discharge care, delayed detection of HF complications, and underutilization and under-dosing of evidence-based HF medications. Mobile technology has revolutionized inter-personal communication allowing instantaneous, multi-directional, and massive data transfer. Nonetheless the potential of these enhanced communications have not been fully explored in the management of patients with HF.
Objective: To explore the potential of state-of-the-art mobile technology for home-based remote HF management in order to reduce HF mortality and HF re-hospitalization.
Study Design: This will be a multicenter, randomized controlled clinical trial in patients with HF and reduced left ventricular ejection fraction (LVEF) who are discharged from hospital following an episode of acutely decompensated HF. The clinical effectiveness of a physician-directed patient self-management strategy based on remotely collected physiological data obtained from home-based and wearable devices will be compared with two control groups who will receive the home-based remote HF management system without activation or routine therapy. In the interventional arm, there will be three modes of home-based HF management: (1) Early discharge mode to optimize volume status; (2) Drug escalation mode to ensure the utilization of evidence-based medications at the maximum tolerated dose; and (3) Maintenance mode to ensure medication compliance and early detection of complications such as acutely decompensated HF and atrial fibrillation. The trial will enroll up to 876 patients with LVEF <40% who are discharged from hospital after an episode of acutely decompensated HF. Randomization to the intervention group or control groups will be in a 1:1:1 ratio with follow-up for 1 year. The primary outcome will be a composite of cardiovascular death and HF hospitalization within 1 year.
Summary: DAVID-HF will provide essential information about the role of home-based, remote heart failure monitoring that will incorporate instantaneous physician-directed patient-self management in the long-term management of HF patients.

Start Date02 Nov 2020

Sponsor / Collaborator

The University of Hong Kong

[+3]

NCT04191356

/ TerminatedNot Applicable

An Observational, Multicentre Study to Evaluate the Feasibility of a Novel Mobile Health Monitoring Platform to Capture Patient-centered Outcomes Measures Among Patients With Heart Failure (HF)

Heart Failure (HF) is a highly prevalent disease that also carries high morbidity and mortality. The Food and Drug Administration (FDA) recognizes the importance of developing patient-centric endpoints that are relevant to patients beyond mortality and hospitalizations.
Although functional status can be objectively measured by peak exercise oxygen consumption, it is difficult and impractical to implement in large clinical studies, especially in an ambulatory real-world setting. Recently, new mobile health technologies have emerged as clinical tools and offer an opportunity to overcome these challenges in measuring functional capacity and recording symptoms.
The goal of this observational study is to evaluate the feasibility of monitoring functional capacity among patients with heart failure (HF) using mobile health monitoring platforms.
Everion® and Apple Watch Series 4 and above will be used for this study. The BiovitalsHF® mobile app can capture patients' key cardiopulmonary physiological parameters, functional capacity (using activity patterns, including guided mobile-based 6MWT, audio recording) and assessment of QoL through validated PROs.

Start Date17 Oct 2020

Sponsor / Collaborator

Biofourmis Singapore Pte Ltd.

[+2]

100 Clinical Results associated with Biofourmis Singapore Pte Ltd.

0 Patents (Medical) associated with Biofourmis Singapore Pte Ltd.

Literatures (Medical) associated with Biofourmis Singapore Pte Ltd.

24 Jul 2023·2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Automatic assessment of patient eligibility by utilizing NLP and rule-based analysis

Article

Author: Tun, Pyae Phyo ; Xie, Jiecheng ; Wibowo, Sandi ; Luo, Jiawen ; Hao, Chen

Assessment of patient eligibility is an essential process in the clinical trial but there are a lot of manual processes involved. Natural Language Processing (NLP) is a promising technique to automate analysing of the massive volume of Electronic Medical Records (EMRs) hence it can assist in the assessment of patient eligibility, especially in clinical trials that require complex inclusion/exclusion criteria. In this paper, we proposed a hybrid model which utilized both rule-based and NLP technologies to automate the assessment of patient eligibility. The result showed that the hybrid model had a better trade-off between sensitivity and precision compared to the rule-based model and NLP similarity model. Moreover, the accuracy of the hybrid model was validated on the larger dataset and it reached an accuracy of 87.3%. Therefore, this technique potentially can improve the efficiency of patient recruitment by eliminating the manual processes that involve in the assessment of patient eligibility.

24 Jul 2023·2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Use of Wearable Sensor Device and Mobile Application for Objective Assessment of Pain in Post-surgical Patients: A Preliminary Study

Article

Author: Wibowo, Sandi ; Hao, Chen ; Chaw, Wei Liang ; Antuvan, Chris Wilson

Effective post-operative pain management requires an accurate and frequent assessment of the pain experienced by the patients. The current gold-standard of pain assessment is through patient self-evaluation (e.g., numeric rating scale, NRS) which is subjective, prone to recall-bias, and does not provide comprehensive information of the pain intensity and its trends. We conducted a study to explore the potential of wearable biosensors and machine learning-based analysis of physiological parameters to estimate the pain intensity. The results from our study of post-operative knee surgery patients monitored over a period of 30 days demonstrate the feasibility of the system in ambulatory setting, with a substantial agreement (Cohen's Kappa = 0.70, 95% CI 0.68-0.72) between the pain intensity estimation and the patient reported numerical rating scale. Therefore, the wearable biosensors coupled with the machine learning-derived pain estimation are capable of remotely assessing the pain intensity.

08 Nov 2022·Circulation

Abstract 12390: Accelerometer-Based Digital Measurement of Functional Capacity in Heart Failure Patients

Author: Lee, Shao Guang Sheldon ; Wong, Raymond C ; Majmudar, Maulik ; Vaishnava, Prashant ; Sim, David K ; Chen, Hao ; Wibowo, Sandi ; Dwiyasa, Felis ; McLeod, Christopher J ; Velarde, Jackie ; Loh, Seet Yoong ; Soon, Dinna Kar Nee

Introduction: Repeated 6MWT in clinic are inconvenient for patients and disruptive to usual clinic throughput. Digital measures may be a means to overcome these challenges and reliably measure functional capacity. Methods: As part of an observational clinical trial (NCT04191356), during an 8-week observation period, patients with heart failure (HF) utilize arm-band biosensors (Biofourmis Everion®) for at least 15 hours daily while carrying out daily activities and complete an in-clinic investigator-guided 6MWT on the first and last days of participation. Interim data (N=30, age 56.17 ± 11.26; 24 males; 3 NYHA I, 25 NYHA II, 2 NYHA III) are used for validation, where the distance of baseline 6MWT is used as reference standard. Results: Passive 6MWT, which is the maximum 6-minute step count derived from daily accelerometer data (Fig 1), is strongly correlated with the reference standard (R=0.7501, P<0.0001, N=30) (Fig 2). Further analysis (Fig 2) reveals that regardless of the duration of the step count, the digital measures consistently have strong correlations with the reference standard, with the highest correlation obtained by Passive 10MWT (R=0.7746, P<0.0001, N=30). Conclusions: Early data highlights that accelerometer data collected during daily activities appears to indicate functional capacity of HF patients. Continued data will validate digital measures against various patient outcomes. Fig 1 Aggregation of Passive 6MWT from step count recorded during daily activities Fig 2 Correlation between accelerometer-derived passive step count digital measures and distance manually measured during baseline investigator-guided 6MWT as reference standard (N=30)

News (Medical) associated with Biofourmis Singapore Pte Ltd.

18 Oct 2024

·www.mobihealthnews.com

Ex-Biofourmis CEO founds healthcare LMM startup and more AI briefs

Biofourmis founder launches health LMM startup Over a year after leaving biotechnology unicorn Biofourmis, ex-CEO and founder Kuldeep Singh Rajput recently announced that he set up a new health tech company focused on generative AI. In a LinkedIn post, he shared that his newly founded startup, OutcomesAI, has partnered with SingHealth to deploy its large multi-modal model (LMM) called Glia, which allows organisations to build clinician companion AI. National Heart Centre Singapore and National Cancer Centre Singapore were identified as the first sites of the LMM integration. JLK bags 510(k) for CT perfusion AI South Korean medical AI company JLK has obtained another regulatory approval in the United States for one of its stroke AI offerings. The company received its third 510(k) clearance from the US Food and Drug Administration for JLK-CTP, which uses AI to automatically identify the centre of cerebral infarction and the area of cerebral hypoperfusion in brain CT perfusion images. JLK received its first 510(k) in June for its MRI-based prostate analysis AI software, then a second one for another stroke AI solution that specifically detects large vessel occlusion in brain CT scans. Korea OKs Heuron's Parkinson's disease AI Fellow Korean medical imaging AI company, Heuron, also recently received regulatory approval from the South Korean Ministry of Food and Drug Safety. According to a press statement, its AI-powered software HeuronNM identifies and quantifies neuromelanin – a key biomarker of Parkinson's disease – in brain 3D MRI images. Meanwhile, Heuron is currently working to secure FDA approvals in Thailand for its range of AI solutions following its expansion there in August. Wadhwani AI backs AIIMS Indian non-profit Wadhwani Institute for Artificial Intelligence (Wadhwani AI) has been tapped to support the All India Institute of Medical Sciences in New Delhi in its healthcare AI integration projects. AIIMS was recently designated as one of India's "AI centres of excellence" under the government's Make AI in India, Make AI Work for India initiative, in which healthcare is one of the priority areas. As a partner, Wadhwani AI will be deploying its AI solutions that support the diagnosis of chest conditions and diabetic retinopathy. The Indian government has pledged to invest over $110 million in supporting the AI COEs over the next four years. NUS Medicine establishes digital health accelerator The Yong Loo Lin School of Medicine of the National University of Singapore (NUS Medicine) has recently launched an accelerator programme for digital health startups, including those developing AI solutions in mental health and hospital administration. A collaboration with 22Health Ventures, the Digital Accelerator Programme of the NUS Medicine Digital Advanced Technology Accelerator aims to support startups from conception through global expansion. The cohort includes 2Strands, which develops blood-based cancer recurrence monitoring tests; Health BETA, which offers genetic tests and digital therapeutics for personalised health monitoring; HealthBridge AI, which is currently testing its clinical product for streamlining administrative tasks; Marymount Lab, which provides patient engagement software for preventive health; and TenangAI, which offers an AI-driven solution to support youth mental health.

100 Deals associated with Biofourmis Singapore Pte Ltd.

100 Translational Medicine associated with Biofourmis Singapore Pte Ltd.

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