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Bilson Jake Libres Campana

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    Preview abstract Background Health datasets from clinical sources do not reflect the breadth and diversity of disease in the real world, impacting research, medical education and artificial intelligence (AI) tool development. Dermatology is a suitable area to develop and test a new and scalable method to create representative health datasets. Methods We used Google Search advertisements to solicit contributions of images of dermatology conditions, demographic and symptom information from internet users in the United States (US) over 265 days starting March 2023. With informed contributor consent, we described and released this dataset containing 10,106 images from 5058 contributions, with dermatologist labels as well as Fitzpatrick Skin Type and Monk Skin Tone labels for the images. Results We received 22 ± 14 submissions/day over 265 days. Female contributors (66.04%) and younger individuals (52.3% < age 40) had a higher representation in the dataset compared to the US population, and 36.6% of contributors had a non-White racial or ethnic identity. Over 97.5% of contributions were genuine images of skin conditions. Image quality had no impact on dermatologist confidence in assigning a differential diagnosis. The dataset consists largely of short duration (54% with onset < 7 days ago) allergic, infectious, and inflammatory conditions. Fitzpatrick skin type distribution is well-balanced, considering the geographical origin of the dataset and the absence of enrichment for population groups or skin tones. Interpretation Search ads are effective at crowdsourcing images of health conditions. The SCIN dataset bridges important gaps in the availability of representative images of common skin conditions. View details
    Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program
    Dr. Paisan Raumviboonsuk
    Dr. Peranut Chotcomwongse
    Rajiv Raman
    Sonia Phene
    Kornwipa Hemarat
    Mongkol Tadarati
    Sukhum Silpa-Archa
    Jirawut Limwattanayingyong
    Chetan Rao
    Oscar Kuruvilla
    Jesse Jung
    Jeffrey Tan
    Surapong Orprayoon
    Chawawat Kangwanwongpaisan
    Ramase Sukumalpaiboon
    Chainarong Luengchaichawang
    Jitumporn Fuangkaew
    Pipat Kongsap
    Lamyong Chualinpha
    Sarawuth Saree
    Srirut Kawinpanitan
    Korntip Mitvongsa
    Siriporn Lawanasakol
    Chaiyasit Thepchatri
    Lalita Wongpichedchai
    Lily Peng
    Nature Partner Journal (npj) Digital Medicine (2019)
    Preview abstract Deep learning algorithms have been used to detect diabetic retinopathy (DR) with specialist-level accuracy. This study aims to validate one such algorithm on a large-scale clinical population, and compare the algorithm performance with that of human graders. A total of 25,326 gradable retinal images of patients with diabetes from the community-based, nationwide screening program of DR in Thailand were analyzed for DR severity and referable diabetic macular edema (DME). Grades adjudicated by a panel of international retinal specialists served as the reference standard. Relative to human graders, for detecting referable DR (moderate NPDR or worse), the deep learning algorithm had significantly higher sensitivity (0.97 vs. 0.74, p < 0.001), and a slightly lower specificity (0.96 vs. 0.98, p < 0.001). Higher sensitivity of the algorithm was also observed for each of the categories of severe or worse NPDR, PDR, and DME (p < 0.001 for all comparisons). The quadratic-weighted kappa for determination of DR severity levels by the algorithm and human graders was 0.85 and 0.78 respectively (p < 0.001 for the difference). Across different severity levels of DR for determining referable disease, deep learning significantly reduced the false negative rate (by 23%) at the cost of slightly higher false positive rates (2%). Deep learning algorithms may serve as a valuable tool for DR screening. View details
    Preview abstract Crowdsourcing has enabled the collection, aggregation and refinement of human knowledge and judgment, i.e. ground truth, for problem domains with data of increasing complexity and scale. This scale of ground truth data generation, especially towards the development of machine learning based medical applications that require large volumes of consistent diagnoses, poses significant and unique challenges to quality control. Poor quality control in crowdsourced labeling of medical data can result in undesired effects on patients' health. In this paper, we study medicine-specific quality control problems, including the diversity of grader expertise and diagnosis guidelines' ambiguity in novel datasets of three eye diseases. We present analytical findings on physicians' work patterns, evaluate existing quality control methods that rely on task completion time to circumvent the scarcity and cost problem of generating ground truth medical data, and share our experiences with a real-world system that collects medical labels at scale. View details
    Preview abstract Purpose: To present and evaluate a remote, tool-based system and structured grading rubric for adjudicating image-based diabetic retinopathy (DR) grades. Methods: We compared three different procedures for adjudicating DR severity assessments among retina specialist panels, including (1) in-person adjudication based on a previously described procedure (Baseline), (2) remote, tool-based adjudication for assessing DR severity alone (TA), and (3) remote, tool-based adjudication using a feature-based rubric (TA-F). We developed a system allowing graders to review images remotely and asynchronously. For both TA and TA-F approaches, images with disagreement were reviewed by all graders in a round-robin fashion until disagreements were resolved. Five panels of three retina specialists each adjudicated a set of 499 retinal fundus images (1 panel using Baseline, 2 using TA, and 2 using TA-F adjudication). Reliability was measured as grade agreement among the panels using Cohen's quadratically weighted kappa. Efficiency was measured as the number of rounds needed to reach a consensus for tool-based adjudication. Results: The grades from remote, tool-based adjudication showed high agreement with the Baseline procedure, with Cohen's kappa scores of 0.948 and 0.943 for the two TA panels, and 0.921 and 0.963 for the two TA-F panels. Cases adjudicated using TA-F were resolved in fewer rounds compared with TA (P < 0.001; standard permutation test). Conclusions: Remote, tool-based adjudication presents a flexible and reliable alternative to in-person adjudication for DR diagnosis. Feature-based rubrics can help accelerate consensus for tool-based adjudication of DR without compromising label quality. Translational Relevance: This approach can generate reference standards to validate automated methods, and resolve ambiguous diagnoses by integrating into existing telemedical workflows. View details
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