Bayer and U.K.-based artificial intelligence (AI) software developer Huma are joining forces to develop AI technology to distinguish different forms of non-small-cell lung cancer (NSCLC) on CT exams.
Making use of Huma's machine-learning experience and Bayer's oncology and medical imaging capabilities, the companies plan to utilize machine-learning technology to spot correlations in molecular and imaging assessments -- such as ground-glass opacities -- that can differentiate types of lung cancers. They will then train and test models to provide accurate diagnoses.
The goal is to quickly identify the patients with certain types of NSCLC who can benefit the most from tailored treatments, according to the firms. Their collaboration will begin immediately.
Bayer's Leaps by Bayer investment arm has been both a series B and series C investor in Huma.
![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&dpr=2&fit=crop&h=167&q=70&w=250)
