English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Colon cancer is the second leading cause of cancer-related death in theUnited States of America. Its prognosis has significantly improved with theadvancement of targeted therapies based on underlying molecular changes. TheKRAS mutation is one of the most frequent molecular alterations seen in coloncancer and its presence can affect treatment selection. We attempted to useApple machine learning algorithms to diagnose colon cancer and predict the KRASmutation status from histopathological images. We captured 250 colon cancerimages and 250 benign colon tissue images. Half of colon cancer images werecaptured from KRAS mutation-positive tumors and another half from KRASmutation-negative tumors. Next, we created Image Classifier Model using AppleCreateML machine learning module. The trained and validated model was able tosuccessfully differentiate between colon cancer and benign colon tissue imageswith 98 % recall and 98 % precision. However, our model failed to reliablyidentify KRAS mutations, with the highest realized accuracy of 66 %. Althoughnot yet perfected, in the near future Apple CreateML modules can be used indiagnostic smartphone-based applications and potentially alleviate shortages ofmedical professionals in understaffed parts of the world.

Apple Machine Learning Algorithms Successfully Detect Colon Cancer but Fail to Predict KRAS Mutation Status