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Record W2607484545 · doi:10.1117/12.2251001

Handheld SFDI/polarimetric imaging device for objective evaluation of hypertrophic scars (Conference Presentation)

2017· article· en· W2607484545 on OpenAlexaboutno aff
Jessica C. Ramella‐Roman, Karla A. Montejo, Nicole Sevilla, Susan Stoff, Mariacarla Gonzalez, Joseph Chue-Sang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsnot available
Fundersnot available
KeywordsPresentation (obstetrics)Mobile deviceComputer sciencePolarimetryComputer visionMedicineOpticsRadiologyPhysicsWorld Wide Web

Abstract

fetched live from OpenAlex

Scars can be debilitating and cause serious functional limitations, significantly reduced physical function and loss of ability to perform normal daily activities. Scar formation is not fully understood and the treatment options have been hampered by the lack of an objective diagnostic tool to assess scars. Presently, assessment of hypertrophic scars has been based on subjective clinician rankings using a four-parameter scale called the Vancouver Scar Scale (VSS) or the Patient Observer Scar Assessment Scale (POSAS) but no objective, standardized tool for quantifying scar severity is available, despite known inadequacies of the subjective scales. We have developed a hand-held multi modal system consisting of a combined Spatial Frequency Domain Imager (SFDI) used for the assessment of tissue molecular components and a polarimeter for structural measurements. The SFDI capability is provided by an Arduino board controlled spectrally and polarimetric diverse Light Emitting Diodes (LED) ring illuminator. For SFDI imagery, the LEDs are combined with sinusoidal patterns. A single pattern snapshot SFDI approach is used to observe and quantify the biological components in the scar tissue including: oxygenated and de oxygenated hemoglobin, water, and melanin. The SFDI system is integrated with a reduced Mueller Matrix polarimetric system, whose illumination is also included in the LED’s ring, and providing for the assessment of collagen orientation through Mueller Matrix decomposition. The design of the system and experimental work on phantoms will be presented.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.222
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.133
GPT teacher head0.387
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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