When diagnosing lung cancer at an early stage, proper lung nodule analysis is essential. Diagnostic tools such as Infervision’s InferRead CT Lung play a key role in this.
Infervision and its AI for lung cancer diagnosis
Infervision’s AI for lung cancer diagnosis reduces the inter-observer difference in the assessment of potential lung cancer cases. In fact, it has been shown in clinical trials to reduce missed nodules by 35% for radiologists, in addition to saving healthcare professionals 30% of the time spent reading the exam.
Features of Infervision AI
Many hospitals and imaging centres are already using InferRead CT Lung. The goal? To detect lung cancer at an early stage by recognising and classifying lung nodules in chest CT scans through the application of artificial intelligence in medicine.
Among its advantages, we can list that:
- It helps to detect, quantify and classify nodules as solid or semi-solid, calcified, ground glass, etc.
- It helps to evaluate their growth and determine the degree of malignancy.
- It detects incidental findings.
- It allows reconstruction and visualisation of the 3D image to plan surgery.
In addition, InferRead CT Lung offers a preliminary report in which it is possible to consult the lists of predictions and suspicious nodules and access data relevant to diagnosis and intervention such as nodule location, density measurement and probability of malignancy.
It also provides relevant information about the nodule growth (Volume Doubling Time). It contains a quantitative analysis and data on the composition, characteristics and evolution over time of the nodule that will be taken into account in the operation.
Regarding the effectiveness of the tool and its impact on the accuracy of diagnosis and subsequent treatment, it is worth mentioning that hundreds of thousands of chest CT scans have been used in its training.