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Record W4214841764 · doi:10.2196/36906

Confocal Microscopy and its Role in Teledermatology: Diagnosis of Basal Cell Carcinoma in a Clinical Setting

2022· article· en· W4214841764 on OpenAlexvenueno aff
Jason Garfinkel, Craig Chandler, Gennady Rubinstein, Manu Jain

Bibliographic record

VenueIproceedings · 2022
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsnot available
FundersNational Cancer Institute
KeywordsTeledermatologyMedicineMedical diagnosisBasal cell carcinomaDermatoscopyMedical physicsTelemedicineDermatologyPathologyRadiologyBasal cellHealth careMelanoma

Abstract

fetched live from OpenAlex

Background Reflectance confocal microscopy (RCM) is a noninvasive tool that is used to diagnose skin cancers. However, RCM requires an expert consultation, which is often performed via store-and-forward (SAF) teledermatology. Unfortunately, SAF does not mimic bedside diagnosis, nor permits interaction between the remote expert reader, physician, and patient. Recently, a live interactive method (LIM)–tele-RCM approach was shown to diagnose basal cell carcinoma (BCC) from a remote location, demonstrating advantages over SAF by providing a bedside diagnosis during consultation. Objective The aim of this study is to validate the LIM-tele-RCM approach to diagnose BCC in a real-world setting. Methods In this pilot study, 4 patients with 6 clinically suspicious BCC lesions were enrolled and imaged with RCM at a Los Angeles dermatology clinic. A Health Insurance Portability and Accountability Act–compliant teleconferencing application was used to livestream RCM images to an expert RCM reader in New York. The expert reader had remote control of the software, direct audio communication with the clinic, and the patient’s clinical history with dermoscopy. During imaging, RCM features were noted, and a diagnosis was made at the bedside. After imaging, patients completed a short questionnaire (on a 5-point scale, with 5 being the highest score) about satisfaction, comfort, and communication during the session. Results RCM diagnosed 4/6 (67%) lesions correctly as BCC and 2/6 (33%) were false-positive diagnoses. The true-positive lesions had “tumor islands with palisading and clefting” and were directly managed with Mohs surgery. The false-positive lesions had “dark silhouettes” (a common false-positive feature for BCC) and underwent a shave biopsy for confirmation. The entire session ranged from 15 to 20 minutes (an average of 17.7 minutes), comparable to the reported RCM procedure time. On the questionnaire, all patients responded with the highest rating (5/5) for each question. Conclusions LIM-tele-RCM demonstrates potential advantages over the SAF method, enabling bedside diagnosis with similar diagnostic accuracy as reported in the literature and proper management. Additionally, the remote reader had access to patients’ clinical backgrounds and could engage with patients. It may also be useful for training novice RCM users and beneficial in settings where remote diagnostics are desired, such as during the COVID-19 pandemic. However, technical challenges such as image quality degradation during video streaming, poor internet bandwidth, and end user latency may impact diagnosis. Larger, multicenter studies are needed to assess the accuracy of LIM-tele-RCM for the diagnosis of BCC and other neoplastic and inflammatory lesions, and to quantify technical limitations. Acknowledgments This work was funded by the National Cancer Institute Cancer Center (Grant P30 CA008748). Conflicts of Interest None declared.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.309
Teacher spread0.292 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations1
Published2022
Admission routes1
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