Feasibility Study of a Smartphone Application for Detecting Skin Cancers in People With Albinism
Bibliographic record
Abstract
PURPOSE: Albinism affects some facets of the eye's function and coloration, as well as hair and skin color. The prevalence of albinism is estimated to be one in 2,000-5,000 people in sub-Saharan Africa and one in 270 in Tanzania. People in Tanzania with albinism experience sociocultural and economic disparities. Because of stigma related to albinism, they present to hospitals with advanced disease, including skin cancers. Mobile health (mHealth) can help to bridge some of the gaps in detection and treatment of skin cancers affecting this population. METHODS: We assessed the feasibility of using a mobile application (app) for detection of skin cancers among people with albinism. The study was approved by the Ocean Road Cancer Institute institutional review board. Data, including pictures of the lesions, were collected using a mobile smartphone and submitted to expert reviewers. Expert reviewers' diagnosis options were benign, malignant, or unevaluable. RESULTS: A total of 77 lesions from different body locations of 69 participants were captured by the NgoziYangu mobile app. Sixty-two lesions (81%) were considered malignant via the app and referred for biopsy and histologic diagnosis. Of those referred, 55 lesions (89%) were biopsied, and 47 lesions (85%) were confirmed as skin malignancies, whereas eight (15%) were benign. CONCLUSION: With an increasing Internet coverage in Africa, there is potential for smartphone apps to improve health care delivery channels. It is important that mobile apps like NgoziYangu be explored to reduce diagnostic delay and improve the accuracy of detection of skin cancer, especially in stigmatized groups.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".