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iMD Research Inc Internship

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Number of Interns: 7
Proposed Start Date: Mon, 05/10/2021
Proposed End Date: Wed, 08/31/2022
Number of Work Hours per Week: 5-40

Contact Name/ Supervisor: Nathaniel Lasry

Full Address of organization/unit: 377 rue de la Commune O.

How to Apply:

Please complete the following application form. Only shortlisted candidates will be contacted for an interview.

 

Montreal-based company iMD Research designs, develop and builds AI-driven medical devices. One of its wearable devices is a small Bluetooth connected vital sign (i.e. Temperature, O2 saturation, Heart Rate, Respiratory Rate and Blood Pressure) monitoring bracelet that also detects falls. The simple low bulk bracelet is currently undergoing certification and homologation by Health Canada and the FDA. It is being solicited by institutional and telehealth networks because it provides patients in remote areas (from Northern Canada to Continental Africa) with remote access to health assessments. Most interestingly, the wearable device is designed with swappable battery-latches that enable continuous monitoring of vital signs for seniors and patients with chronic medical conditions. Founded in 2013 by Nathaniel Lasry, a physicist by training who completed a PhD in cognitive science (McGill, 2006) and postdoc from the Harvard School of Engineering & Applied Sciences. iMD Research has a long track record of working in academic partnerships (McGill, Ecole Polytechnique de Montreal, Université Laval, University of Alberta…) as well as the Montreal medical technology ecosystem (member of MEDTEQ, SCALE AI) and its academic hospital network. iMD Research is also an industrial partner and co-applicant for the Quebec SmartCare Consortium.

 

Placement Description & Logistics

The goal of this project is to build an AI infrastructure that can be used for research and provide tangible benefits for patients in remote areas from Northern Canada to Sub-Saharan Africa. For instance, patients who undergo changes in vital signs or gait (with or without associated changes in blood pressure) could be monitored more carefully for an increased risk of falling. The concomitant measure of different vital signs could be used to train networks to recognize a wide range of medical conditions as simple (but life-threatening) as atrial fibrillation to rarer conditions that may only be identified as more cases of a class are acquired and data accrued. The role of interns will be to build data infrastructure and begin building models that will advance our understanding of medical disorders and help monitor patients to avoid preventable medical conditions.

Internship Tasks

Expected Outputs


Task 1: Understand the architecture of iMD's hardware - sensing and computing outputs.
Task 2: Examine and enhance current processes of data transmission, reception, storage and retrieval.

  • Task 2a: interns will examine constraints on transmission (e.g., encryption, power optimization, connectivity alternatives: BLE vs CAT-M1).
  • Task 2b: Interns will evaluate and optimize data security for transmission and storage of individual health data records.
  • Task 2c: Interns will evaluate middleware solutions to house enable interoperability with different institutional HL-7 compliant systems.

Task 3: Interns will help conduct validation studies, including the collection of clinical validation data of ground truth measures.
Task 4: Interns will develop the online infrastructure to enable the building of predictive models that can alert patients and avoid preventable medical conditions.
 

Deliverable 1: Provide block diagrams and a proposal for optimizing the flow of data from the device.
Deliverable 2: Build software that will ensure the security of data at each level of the pipeline (in the wearable, on a receiving mobile device, in middleware that can inter-operably relay the data to institutions, in cloud-storage).
Deliverable 3: Refining of the protocol for a clinical validation study and in-person collection of clinical validation data.
Deliverable 4: Creation of online infrastructure that enables the analysis of data securely stored in the cloud to build predictive models that can alert patients and avoid preventable medical conditions.
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