Predicting total lung capacity from spirometry: a machine learning approach.

Background And Objective: Spirometry patterns can suggest that a patient has a restrictive ventilatory impairment; however, lung volume measurements such as total lung capacity (TLC) are required to confirm the diagnosis. The aim of the study was to train a supervised machine learning model that can accurately estimate TLC values from spirometry and subsequently identify which patients would most benefit from undergoing a complete pulmonary function test.

Methods: We trained three tree-based machine learning models on 51,761 spirometry data points with corresponding TLC measurements.

Artificial intelligence and machine learning for medical imaging: A technology review.

Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence of disruptive technical advances and impressive experimental results, notably in the field of image analysis and processing. In medicine, specialties where images are central, like radiology, pathology or oncology, have seized the opportunity and considerable efforts in research and development have been deployed to transfer the potential of AI to clinical applications. With AI becoming a more mainstream tool for typical medical imaging analysis tasks, such as diagnosis, segmentation, or classification, the key for a safe and efficient use of clinical AI applications relies, in part, on informed practitioners.

Reinforcement Learning for Radiotherapy Dose Fractioning Automation.

External beam radiotherapy cancer treatment aims to deliver dose fractions to slowly destroy a tumor while avoiding severe side effects in surrounding healthy tissues. To automate the dose fraction schedules, this paper investigates how deep reinforcement learning approaches (based on deep Q network and deep deterministic policy gradient) can learn from a model of a mixture of tumor and healthy cells. A 2D tumor growth simulation is used to simulate radiation effects on tissues and thus training an agent to automatically optimize dose fractionation.

Isoflucypram, the first representative of a new succinate dehydrogenase inhibitor fungicide subclass: Its chemical discovery and unusual binding mode.

Succinate dehydrogenase inhibitors (SDHIs) have played a crucial role in disease control to protect cereals as well as fruit and vegetables for more than a decade. Isoflucypram, the first representative of a newly installed subclass of SDHIs inside the Fungicide Resistance Action Committee (FRAC) family of complex II inhibitors, offers unparalleled long-lasting efficacy against major foliar diseases in cereals. Herein we report the chemical optimization from early discovery towards isoflucypram and the first hypothesis of its altered binding mode in the ubiquinone binding site of succinate dehydrogenase.