Effects of incorrect computer-aided detection (CAD) output on human decision-making in mammography

To investigate the effects of incorrect computer output on the reliability of the decisions of human users. This work followed an independent UK clinical trial that evaluated the impact of computer-aided detection(CAD) in breast screening. The aim was to use data from this trial to feed into probabilistic models (similar to those used in "reliability engineering") which would detect and assess possible ways of improving the human-CAD interaction. Some analyses required extra data; therefore, two supplementary studies were conducted. Study 1 was designed to elucidate the effects of computer failure on human performance. Study 2 was conducted to clarify unexpected findings from Study 1

Focal Spot, Spring 2004

https://digitalcommons.wustl.edu/focal_spot_archives/1096/thumbnail.jp

A comparative evaluation of two algorithms of detection of masses on mammograms

In this paper, we implement and carry out the comparison of two methods of computer-aided-detection of masses on mammograms. The two algorithms basically consist of 3 steps each: segmentation, binarization and noise suppression using different techniques for each step. A database of 60 images was used to compare the performance of the two algorithms in terms of general detection efficiency, conservation of size and shape of detected masses.Comment: 9 pages, 5 figures, 1 table, Vol.3, No.1, February 2012,pp19-27; Signal & Image Processing : An International Journal (SIPIJ),201

Towards automatic pulmonary nodule management in lung cancer screening with deep learning

The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines, the workup of screen-detected nodules strongly relies on nodule size and nodule type. In this paper, we present a deep learning system based on multi-stream multi-scale convolutional networks, which automatically classifies all nodule types relevant for nodule workup. The system processes raw CT data containing a nodule without the need for any additional information such as nodule segmentation or nodule size and learns a representation of 3D data by analyzing an arbitrary number of 2D views of a given nodule. The deep learning system was trained with data from the Italian MILD screening trial and validated on an independent set of data from the Danish DLCST screening trial. We analyze the advantage of processing nodules at multiple scales with a multi-stream convolutional network architecture, and we show that the proposed deep learning system achieves performance at classifying nodule type that surpasses the one of classical machine learning approaches and is within the inter-observer variability among four experienced human observers.Comment: Published on Scientific Report

Improving the cost effectiveness equation of cascade testing for Familial Hypercholesterolaemia (FH)

Purpose of Review : Many International recommendations for the management of Familial Hypercholesterolaemia (FH) propose the use of Cascade Testing (CT) using the family mutation to unambiguously identify affected relatives. In the current economic climate DNA information is often regarded as too expensive. Here we review the literature and suggest strategies to improve cost effectiveness of CT. Recent findings : Advances in next generation sequencing have both speeded up the time taken for a genetic diagnosis and reduced costs. Also, it is now clear that, in the majority of patients with a clinical diagnosis of FH where no mutation can be found, the most likely cause of their elevated LDL-cholesterol (LDL-C) is because they have inherited a greater number than average of common LDL-C raising variants in many different genes. The major cost driver for CT is not DNA testing but of treatment over the remaining lifetime of the identified relative. With potent statins now off-patent, the overall cost has reduced considerably, and combining these three factors, a FH service based around DNA-CT is now less than 25% of that estimated by NICE in 2009. Summary : While all patients with a clinical diagnosis of FH need to have their LDL-C lowered, CT should be focused on those with the monogenic form and not the polygenic form