In 2017, the Kaggle Data Science Bowl took aim at using machine learning and artificial intelligence to fight the leading cause of cancer death in the US among both men and women. Entrants were challenged to use a dataset of thousands of high-resolution pulmonary CT images to create new lung cancer detection algorithms. These algorithms were made to improve diagnosis and reduce false positive rates.

Of the 394 competing teams, which team received the top prize? A team combining members from both the Medicine and Computer Science Departments of Tsinghua University in China.

Competitions such as this are a great way to combine international talent with global problems. This style of teamwork is just scratching the surface of the infinite potential for advancement within our field through interactions between medical professionals and computer science.

During radiology training, we learn that a 3-cm, spiculated, soft-tissue attenuating lung mass has a very high probability of being cancer. Likewise, a 5-mm, smooth, calcified nodule has a very low probability of being cancer.

However, we also know that many pulmonary nodules lay somewhere in between our ability to accurately predict malignancy. The Fleischner Society worked very hard to offer a solution with its updated follow-up criteria in 2017, which included both size and density changes. However, we still can’t look at an 8-mm nodule with a slightly irregular border and say how likely it will be cancer.

To take the Kaggle Competition one step further, there is a very real possibility that Fleischner criteria (or its replacement) will be very customizable and lung nodule tracking will improve. We will dive more into this in Article 2.