Tiya Tiyasirichokchai

Google Research
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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
    A mobile-optimized artificial intelligence system for gestational age and fetal malpresentation assessment
    Ryan Gomes
    Bellington Vwalika
    Chace Lee
    Angelica Willis
    Joan T. Price
    Christina Chen
    Margaret P. Kasaro
    James A. Taylor
    Elizabeth M. Stringer
    Scott Mayer McKinney
    Ntazana Sindano
    William Goodnight, III
    Justin Gilmer
    Benjamin H. Chi
    Charles Lau
    Terry Spitz
    Kris Liu
    Jonny Wong
    Rory Pilgrim
    Akib Uddin
    Lily Hao Yi Peng
    Kat Chou
    Jeffrey S. A. Stringer
    Shravya Ramesh Shetty
    Communications Medicine(2022)
    Preview abstract Background Fetal ultrasound is an important component of antenatal care, but shortage of adequately trained healthcare workers has limited its adoption in low-to-middle-income countries. This study investigated the use of artificial intelligence for fetal ultrasound in under-resourced settings. Methods Blind sweep ultrasounds, consisting of six freehand ultrasound sweeps, were collected by sonographers in the USA and Zambia, and novice operators in Zambia. We developed artificial intelligence (AI) models that used blind sweeps to predict gestational age (GA) and fetal malpresentation. AI GA estimates and standard fetal biometry estimates were compared to a previously established ground truth, and evaluated for difference in absolute error. Fetal malpresentation (non-cephalic vs cephalic) was compared to sonographer assessment. On-device AI model run-times were benchmarked on Android mobile phones. Results Here we show that GA estimation accuracy of the AI model is non-inferior to standard fetal biometry estimates (error difference -1.4 ± 4.5 days, 95% CI -1.8, -0.9, n=406). Non-inferiority is maintained when blind sweeps are acquired by novice operators performing only two of six sweep motion types. Fetal malpresentation AUC-ROC is 0.977 (95% CI, 0.949, 1.00, n=613), sonographers and novices have similar AUC-ROC. Software run-times on mobile phones for both diagnostic models are less than 3 seconds after completion of a sweep. Conclusions The gestational age model is non-inferior to the clinical standard and the fetal malpresentation model has high AUC-ROCs across operators and devices. Our AI models are able to run on-device, without internet connectivity, and provide feedback scores to assist in upleveling the capabilities of lightly trained ultrasound operators in low resource settings. View details