SOURCES SOUGHT
A -- Multivariate and/or machine learning algorithm(s) development services
- Notice Date
- 12/15/2021 4:09:23 AM
- Notice Type
- Sources Sought
- NAICS
- 541715
— Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)
- Contracting Office
- W4PZ USA MED RSCH ACQUIS ACT FORT DETRICK MD 21702-5014 USA
- ZIP Code
- 21702-5014
- Solicitation Number
- LSI_SFDI
- Response Due
- 12/22/2021 10:00:00 AM
- Archive Date
- 12/25/2021
- Point of Contact
- Tammy McRae, Jeremy McMurry
- E-Mail Address
-
tammy.n.mcrae.civ@mail.mil, jeremy.l.mcmurry.civ@mail.mil
(tammy.n.mcrae.civ@mail.mil, jeremy.l.mcmurry.civ@mail.mil)
- Description
- Request for Information/Sources Sought for Research Support Services INTRODUCTION The U.S. Army Medical Research Acquisition Activity (USAMRAA) located at 820 Chandler Street, Fort Detrick, MD on behalf of the U.S. Army Institute of Surgical Research (USAISR) is issuing this sources sought synopsis as a means of conducting market research to identify potential sources having an interest in providing a multivariate and/or machine learning algorithm(s) services. The result of market research will contribute to determining the method of procurement, if a requirement materializes. Based on the responses to this sources sought notice/market research, this requirement may be set-aside for small businesses (in full or part) or procured through full and open competition. Multiple awards may be made. All small business set-aside categories will be considered. Telephone inquiries will not be accepted or acknowledged, and no feedback or evaluation will be provided to companies regarding submissions. PROGRAM BACKGROUND The USAISR strives to be the nation�s premier joint research organization for planning and executing registry-based and translational research; which provides innovative solutions for burn, trauma, and combat casualty care from the point of injury through rehabilitation. There is a critical unmet need for far-forward triage, and monitoring tools that enhance battlefield diagnostics for burn wounds by non-burn experts. Whilst burns, particularly in their early stages are dynamic, and often evolving, they are influenced by secondary comorbidities.� There is a need for differentiating burn depth between superficial-partial, and deep partial-thickness burns at the point of injury. Soldiers� evacuation/triage is currently determined by burn depth and Total Body Surface Area (TBSA).� Deep partial thickness requires evacuation to a definitive care facility whereas soldiers with superficial partial thickness burns can be initially treated at far-forward facilities.� Burns that are greater than 20% TBSA require evacuation. Thus, there is an unmet need for portable, non-contact, compact, easy-to-use, devices/algorithms that can rapidly, and objectively classify burn severity in the hands of non-expert personnel at the point of injury. REQUIRED CAPABILITIES The goal of this procurement is the development of a new set of multivariate and/or machine learning algorithms to assess burn severity and burn surface area using data collected from Spatial Frequency Domain Imaging (SFDI) and Laser Speckle Imaging (LSI) systems.� Specifically, the USAISR has a requirement for a multivariate and/or machine learning algorithm(s) as outlined below: Year One Train USAISR personnel on best practices involving operation and data collection from the SFDI and the LSI. � Perform routine validation measurements on skin-simulating optical phantoms. Create phantoms having optical properties that mimic normal and burned skin. Perform detailed analysis of the Region of Interest (ROI) imaging data (SFDI) - optical properties (absorption coefficient, ?a and reduced scattering coefficient, ?s�) and LSI - Perfusion Units (PU) and correlate data to histopathology and burn depth. Perform detailed analysis of the ROI imaging data (SFDI) - optical properties (absorption coefficient, ?a and reduced scattering coefficient, ?s�) and LSI - Perfusion Units (PU)) and correlate data to Lund-Browder Chart and TBSA. Convert SFDI initial eight wavelengths between 471-850nm (potential ninth ~950nm) and at five spatial frequencies evenly spaced between 0-0.2mm-1 into outputs for skin structural information, edema, tissue chromophores, oxygen saturation, and total hemoglobin. Integrate profilometry to develop a 3-D profile from the imaged regions to map burn depth and TBSA. Develop algorithms for stitching individual imaged areas (SFDI and LSI) into a burn severity map. Generate a Receiver Operator Curve (ROC) for each of the following imaging parameters: absorption coefficient (?a), reduced scattering coefficient (?s�), deoxyhemoglobin concentration, oxygemoglobin concentration, total hemoglobin concentration, Perfusion Units (PU), Speckle Flow Index (SFI), corrected speckle flow index (CSFI), Tissue Metabolic Rate of Oxygen (TMRO2), and diffuse reflectance. Determine ability of each parameter to classify burns in a binary manner using the Youden index: superficial partial-thickness burn or deep partial-thickness burn. Minimize of the number of SFDI and LSI acquisition parameters (3.1.7) to yield a simplified algorithm based on data from 3.1.7 and 3.1.7.1. Submit abstract to MHSRS; if accepted, attend and present the work at the conference Year Two Develop multivariate and/or machine learning algorithms (support vector machine (SVM) learning algorithm) combining SFDI and LSI data from multiple wide-field images to produce two outputs: burn depth and TBSA. Develop and optimize SVM algorithm based on collected parameters from 3.1.7. Accurately determine TBSA within 10% for burns greater than 20%. Report accuracy of burn depth measurement TBSA measurement determined for SFDI and LSI, from preclinical porcine burn model compared to the clinical assessment standard. Sensitivity, specificity, positive predictive values, and negative predictive values will be analyzed. Integrated quantification of local burn metabolism using both LSI and SFDI data. Submit abstract to MHSRS; if accepted, attend and present the work at the conference. � ELIGIBILITY The applicable NAICS code for this requirement is (541715 - Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology). The Product Service Code is AN23 � Health R&D Services; Health Research and Training; Experimental Development. SUBMISSION DETAILS Interested businesses should submit a brief capabilities statement package (no more than ten 8.5 X 11 inch pages, font no smaller than 10 point) demonstrating ability to provide the services listed in the technical capabilities description. Documentation should be in bullet format. No phone or email solicitations with regards to the status of the RFP will be accepted prior to its release. Your response to this Sources Sought, including any capabilities statement, shall be electronically submitted to the Contract Specialist, Tammy McRae, in either Microsoft Word or Portable Document Format (PDF), via email to tammy.n.mcrae.civ@mail.mil no later than 1:00 p.m. Eastern Standard Time on 22 December 2021 and reference this Notice ID number in the subject line of your e-mail and on all enclosed documents. Information and materials submitted in response to this request WILL NOT be returned. DO NOT SUBMIT CLASSIFIED MATERIAL. If your organization has the potential capacity to provide the required services, please provide the following information: 1) Organization name, address, primary points of contact (POCs) and their email address, FAX, Web site address, telephone number, and type of ownership for the organization, DUNS number; and 2) a tailored capability statement addressing the requirements of this notice, with appropriate documentation supporting claims of organizational and staff capability. If significant subcontracting or teaming is anticipated in order to deliver technical capabilities, organizations should address the administrative and management structure of such arrangements. All data received in response to this Sources Sought that is marked or designated as corporate or proprietary will be fully protected from any release outside the Government.
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- Record
- SN06198460-F 20211217/211215230123 (samdaily.us)
- Source
-
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