Lung cancer is a leading cause of cancer-related deaths worldwide, and early detection is crucial for improving patient outcomes. Nevertheless, early diagnosis of cancer is a major challenge, particularly in low-resource settings where access to medical resources and trained radiologists is limited. The objective of this study is to propose an automated end-to-end deep learning-based framework for the early detection and classification of lung nodules, specifically for low-resource settings. The proposed framework consists of three stages: lung segmentation using a modified 3D U-Net named 3D Res-U-Net, nodule detection using YOLO-v5, and classification with a Vision Transformer-based architecture. We evaluated the proposed framework on a publicly available dataset, LUNA16. The proposed framework's performance was measured using the respective domain's evaluation matrices. The proposed framework achieved a 98.82% lung segmentation dice score while detecting the lung nodule with 0.76 mAP@50 from the segmented lung, at a low false-positive rate. The performance of both networks of the proposed framework was compared with other studies and found to outperform them regarding segmentation and detection accuracy. Additionally, our proposed Vision transformer network obtained an accuracy of 93.57%, which is 1.21 higher than the state-of-the-art networks. Our proposed end-to-end deep learning-based framework can effectively segment lungs, and detect and classify lung nodules, specifically in low-resource settings with limited access to radiologists. The proposed framework outperforms existing studies regarding all the respective evaluation metrics. The proposed framework can potentially improve the accuracy and efficiency of lung cancer screening in low-resource settings, ultimately leading to better patient outcomes.

Objectives: The present study evaluated the impact of a commercially available explainable AI algorithm in augmenting the ability of clinicians to identify lung cancer on chest X-rays (CXR). Design: This retrospective study evaluated the performance of 11 clinicians for detecting lung cancer from chest radiographs, with and without assistance from a commercially available AI algorithm (red dot, Behold.ai) that predicts suspected lung cancer from CXRs. Clinician performance was evaluated against clinically confirmed diagnoses. Setting: The study analysed anonymised patient data from an NHS hospital; the dataset consisted of 400 chest radiographs from adult patients (18 years and above) who had a CXR performed in 2020, with corresponding clinical text reports. Participants: A panel of readers consisting of 11 clinicians (consultant radiologists, radiologist trainees and reporting radiographers) participated in this study. Main outcome measures: Overall accuracy, sensitivity, specificity and precision for detecting lung cancer on CXRs by clinicians, with and without AI input. Agreement rates between clinicians and performance standard deviation were also evaluated, with and without AI input. Results: The use of the AI algorithm by clinicians led to an improved overall performance for lung tumour detection, achieving an overall increase of 17.4% of lung cancers being identified on CXRs which would have otherwise been missed, an overall increase in detection of smaller tumours, a 24% and 13% increased detection of stage 1 and stage 2 lung cancers respectively, and standardisation of clinician performance. Conclusions: This study showed great promise in the clinical utility of AI algorithms in improving early lung cancer diagnosis and promoting health equity through overall improvement in reader performances, without impacting downstream imaging resources.

GE Healthcare and Optellum today announced that they have signed a letter of intent to collaborate to advance precision diagnosis and treatment of lung cancer. GE Healthcare is a global leader in medical imaging solutions. Optellum is the leader in AI decision support for the early diagnosis and optimal treatment of lung cancer. This press release features multimedia. Together, the companies are seeking to address one of the largest challenges in the diagnosis of lung cancer, helping providers to determine the malignancy of a lung nodule: a suspicious lesion that may be benign or cancerous.

A research project between Bayer and digital health company Huma will use artificial intelligence to detect lung cancer in CT scans – and determine which type a patient has, in order to direct treatment. AI is already being applied by a number of groups, with some studies indicating it can even be more effective than trained radiologists in detecting subtle patterns that indicate the presence of tumours. About 75% of patients with lung cancer die within five years of diagnosis, but prospects rise significantly if it is detected while still confined to the lung. At the moment, that happens in only around a third of cases. Bayer and Huma's project wants to go a step further than detecting the presence or absence of a tumour.

Deep neural networks are often called black-boxes due to their difficult-to-interpret decisions. This is characteristic of a deeper trend in machine learning, where predictive performance typically comes at the cost of interpretability. In some domains, such as image-based diagnostic tasks, understanding the reasons behind machine generated predictions is vital in assessing trust. In this study, we introduce novel designs of capsule networks to provide explainable diagnoses. Our proposed deep explainable capsule architecture, called DX-Caps, can encode high-level visual attributes within the vectors of capsules in order to simultaneously produce malignancy predictions for lung cancer as well as approximations of six visually-interpretable attributes, used by radiologists to explain their predictions. To reduce parameter and memory burden of this deeper network, we introduce a new capsule-average pooling function. With this simple, but fundamental addition, capsule networks can be designed in a deeper fashion than was possible before. Our overall approach can be characterized as multi-task learning; we learn to approximate the six high-level visual attributes of a lung nodule within the vectors of our uniquely constructed deep capsule network, while simultaneously segmenting the nodule and predicting its malignancy potential (diagnosis). Tested on over 1000 CT scans, our experimental results show that our proposed algorithm can approximate the visual attributes of lung nodules far better than a deep multi-path dense 3D CNN. The proposed network also achieves higher diagnostic accuracy than a baseline explainable capsule network X-Caps and CapsNet when applied to this task for the first time as well. To the best of our knowledge, this is the first study to investigate capsule networks for visual attribute prediction in general, and explainable medical image diagnosis in particular.

One of lung cancer's most lethal attributes is its ability to trick radiologists. Some nodules appear threatening but turn out to be false positives. Others escape notice entirely, and then spiral without symptoms into metastatic disease. On Monday, however, Google unveiled an artificial intelligence system that -- in early testing -- demonstrated a remarkable talent for seeing through lung cancer's disguises. A study published in Nature Medicine reported that the algorithm, trained on 42,000 patient CT scans taken during a National Institutes of Health clinical trial, outperformed six radiologists in determining whether patients had cancer.