Convolutional Neural Networks for Breast Density Classification: Performance and Explanation Insights

We propose and evaluate a procedure for the explainability of a breast density deep learning based classifier. A total of 1662 mammography exams labeled according to the BI-RADS categories of breast density was used. We built a residual Convolutional Neural Network, trained it and studied the responses of the model to input changes, such as different distributions of class labels in training and test sets and suitable image pre-processing. The aim was to identify the steps of the analysis with a relevant impact on the classifier performance and on the model explainability. We used the grad-CAM algorithm for CNN to produce saliency maps and computed the Spearman's rank correlation between input images and saliency maps as a measure of explanation accuracy. We found that pre-processing is critical not only for accuracy, precision and recall of a model but also to have a reasonable explanation of the model itself. Our CNN reaches good performances compared to the state-of-art and it considers the dense pattern to make the classification. Saliency maps strongly correlate with the dense pattern. This work is a starting point towards the implementation of a standard framework to evaluate both CNN performances and the explainability of their predictions in medical image classification problems

Harnessing Innovative Data and Technology to Measure Development Effectiveness

In this study, the authors discuss and show how new kinds of digital data and analytics methods and tools falling under the umbrella term of Big Data, including Artificial Intelligence (AI) systems, can help measure development effectiveness. Selected case studies provide examples of assessments of the effectiveness of ODA-funded policies and programmes. They use different data and techniques. For example, analysis of mobile phone data and satellite images: to estimate poverty and inequality, traffic congestion, social cohesion or machine learning approaches to social media analysis to understand social interactions and networks, and natural language processing to study changes in public awareness. A toolkit contains resources and suggestions on key steps and considerations, including legal and ethical, when designing and implementing projects aimed at measuring development effectiveness through new digital data and tools. The chapter closes by describing the core principles and requirements of a vision of a ‘Human AI’, which would reflect and leverage the key features of current narrow AI systems that are able to identify and reinforce the neurons that help them reach their goals. A Human AI would be a data and machine-enabled human system (such as a society) that would seek to continuously learn and adjust to improve—rather than prove after the facts—the effectiveness of its collective actions, including development programming and public policies

Microglial Endocannabinoid Signalling in AD

Chronic inflammation in Alzheimer's disease (AD) has been recently identified as a major contributor to disease pathogenesis. Once activated, microglial cells, which are brain-resident immune cells, exert several key actions, including phagocytosis, chemotaxis, and the release of pro- or anti-inflammatory mediators, which could have opposite effects on brain homeostasis, depending on the stage of disease and the particular phenotype of microglial cells. The endocannabinoids (eCBs) are pleiotropic bioactive lipids increasingly recognized for their essential roles in regulating microglial activity both under normal and AD-driven pathological conditions. Here, we review the current literature regarding the involvement of this signalling system in modulating microglial phenotypes and activity in the context of homeostasis and AD-related neurodegeneration

Assessing Trustworthy AI in times of COVID-19. Deep Learning for predicting a multi-regional score conveying the degree of lung compromise in COVID-19 patients

Abstract—The paper's main contributions are twofold: to demonstrate how to apply the general European Union’s High-Level Expert Group’s (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare; and to investigate the research question of what does “trustworthy AI” mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multi-regional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient’s lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia (Italy) since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses socio-technical scenarios to identify ethical, technical and domain-specific issues in the use of the AI system in the context of the pandemic.</p

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Convolutional Neural Network (CNN) based classifier for breast density assessment

Breast cancer is one of the most diagnosed cancer all over the world. It has been studied that one woman in eight is going to develop a breast cancer in her life. It is also widely accepted that early diagnosis is one of the most powerful instrument we have in fighting this sort of cancer. For these reasons, in Tuscany, mammographic screening programs are performed on asymptomatic women at risk every two years in a range between 45 and 74 years. Full Field Digital Mammography (FFDM) is a non-invasive high sensitive method for early stage breast cancer detection and diagnosis, and represents the reference imaging technique to explore the breast in a complete way. Since mammography is a 2D x-ray imaging technique, it suffers of some intrinsic problems: a) breast structures overlapping, b) malignant masses absorb x-rays similarly to the benignant ones and c) the sensitivity is lower for masses or microcalcifications clusters in denser breasts. In fact, a mammogram with a very high percentage of fibro-glandular tissue is less readable because dense tissue presents an x-ray absorption coefficient similar to cancer’s one. Furthermore, to have a sufficient sensitivity in dense breast, a higher dose is given to the patient. Since a lot of healthy women are called to partecipate to the screening programs, dose delivering should be carefully controlled. Furthermore, the European Directive 59/2013/EURATOM states that patients must be well informed about the amount of received radiation dose. For these reasons, the RADIOMA project (RADiazioni IOnizzanti in MAmmografia) was born with the aim of developing a personalized and reliable dosimetric quantitative index for mammographic examinations. The purpose of this master thesis was to build a breast density classifier in order to personalize the new dosimetric index according to breast density. Since the most used density standard has been established by the American College of Radiology (ACR) in 2013, we decided to use those classes to train the classifier. This standard is written on the Breast Imaging Reporting and Data System (BIRADS) Atlas and it is made of four qualitative classes: almost entirely fatty (“A”), scattered areas of fibroglandular density (“B”), heterogeneously dense (“C”) and extremely dense (“D”). In this master thesis, a deep learning based technique has been explored to build the classifier. In fact, in the last few years, deep learning-based methods have been developed with success in a wide range of medical image analysis problems. Since deep learning needs a huge amount of data, the “Azienda Ospedaliero-Universitaria Pisana” (AOUP) collected about 2000 exams from the Senology Department. The exams has been selected by a mammography specialized physician and a radiology technician. This dataset has been anonymized and extracted from the AOUP database. Once obtained the dataset, first, I tried to solve a preliminary classification problem using only two of the four BIRADS classes: the A class, made of less dense breasts, and D class, made of densest breasts. The chosen architecture to solve this problem is a VGG architecture, in which several convolutional layers are stacked together in order to have a high number of input image representations, keeping low the number of parameters. After obtaining good results from this classifier, I proceed to build a more complex classifier. Since one of the main problem in mammograms classification is to build a classifier able to discriminate between dense and non-dense breast, I trained a CNN to solve this problem. In fact, some clinical decisions depend on the possible masking effect that dense tissue could produce on a mammogram. In BIRADS density standard, this means that we should classify two classes: the first one is made of mammograms belonging to A and B classes and the second one is made of mammograms belonging to C and D classes. To solve this problem, I chose a residual architecture, in which the network is asked to learn the residual mapping of convolutional layers. The variability of test accuracy, over input size and over the number of projections used, has been studied. The highest test accuracy is equal to 89.4% and this means that the classifier is able to predict the rightlabel of new mammograms with a precision equal to 89.4%. Finally, I trained a Convolutional Neural Network with a very deep residual architecture to build a BIRADS classifier. As first step, the best optimization of the hyperparameters have been performed. Afterwards, a sistematic study of accuracy variation over input size and the projection used has been performed. The highest test accuracy is equal to 78.0%. Both dense/non-dense and BIRADS classifier have been built with 4 CNNs, one for each projection. The last layer of each CNNs, which represents the score classification, has been averaged over the four mammographic projection in order to assess density as a overall evaluation on an entire mammographic exam, as radiologists do. For the BIRADS classifier, a further rule to produce the final label of the test set has been established: since a radiologist assigns the highest density class when a breast density asymmetry occurs between right and left breasts, in this work, the final label has been assessed separatly for right and left breasts and then the highest density class is assigned to the patient. Regarding the first problem, the convolutional neural network trained on 650x650 pixels images predicts the right label with an accuracy equal to 89.4%. This classifier works well at least as much as other classifier described in other previous works. Regarding the BIRADS classification, I obtained a test accuracy on 650x650 pixels images equal to 78.0%. This result is very good compared to test ac- curacy of other classifier of breast density. Some achievable improvements can be performed in order to have a higher accuracy and a better generalization. First, the ground truth of this work has been decided by only one radiologist. Since the intra-observer and inter-observer variabilities are quite high in BIRADS classification, we could produce a ground truth using the maximum agreement between more than one radiologist. Second, we can increase the number of exams in training, validation and test set in several ways. In fact, other clinical mammograms are going to be collected and, thanks to a recently born collaboration between RADIOMA project and “Azienda Toscana Nord-Ovest” (ATNO), screening exams are going to be collected too. Furthermore, if we find a well-working image standardization method, we may be able to analyze together mammographic exams obtained with different mammographic imaging systems. Finally, using more powerful GPUs, we may be able to train the CNN with high resolution images and with the four mammographic projection at the same time. Beyond this work, we can exploit such technique to find new relationships between mammograms and known breast cancer risk factors. Since breast density is a well known risk factor, which is not considered in the most used breast cancer risk models, CNNs can be used to assess quantitavely and automatically not only breast density but also its role in cancer developing

A story of identity construction : the example of social work and the North Carolina eugenics program

The paper will focus on the philosophy of eugenics and the enactment of policies based on eugenic principles by social workers post-World War II specific to the state of North Carolina, where social workers were given unprecedented power to determine who was a viable candidate for compulsory sterilization between 1945 and 1977. A genealogy, as set forth by Foucault, will be conducted wherein the following historical archival data will provide an example of the development of social ideas and sentiments, discourses and constructs, regarding welfare recipients: the Human Betterment League archives, social work field notes from Mecklenburg County, North Carolina, and the policy briefings of the Board of Public Health of North Carolina. A social constructionist informed thematic analysis was conducted on the archival data of the Eugenics Board of North Carolina as an example of how identity develops through language, such as the pejorative identity of the welfare recipient as it was created by the power accorded to social workers. Findings revealed social workers used language that was distilled into twelve major overarching themes derived from in vivo codes indicating pejorative descriptors of welfare recipients culled from social work authored sterilization petitions

Deep Learning and Medical Image Analysis: Epistemology and Ethical Issues

Machine and deep learning methods applied to medicine seem to be a promising way to improve the perfor-mance in solving many issues from the diagnosis of a disease to the prediction of personalized therapies byanalyzing many and diverse types of data. However, developing an algorithm with the aim of applying it inclinical practice is a complex task which should take into account the context in which the software is devel-oped and should be used. In the first report of the World Health Organization (WHO) about the ethics andgovernance of Artificial Intelligence (AI) for health published in 2021, it has been stated that AI may improvehealthcare and medicine all over the world only if ethics and human rights are a main part of its development.Involving ethics in technology development means to take into account several issues that should be discussedalso inside the scientific community: the epistemological changes, population stratification issues, the opacityof deep learning algorithms, data complexity and accessibility, health processes and so on. In this work, someof the mentioned issues will be discussed in order to open a discussion on whether and how it is possible to address them