CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison

Large, labeled datasets have driven deep learning methods to achieve expert-level performance on a variety of medical imaging tasks. We present CheXpert, a large dataset that contains 224,316 chest radiographs of 65,240 patients. We design a labeler to automatically detect the presence of 14 observations in radiology reports, capturing uncertainties inherent in radiograph interpretation. We investigate different approaches to using the uncertainty labels for training convolutional neural networks that output the probability of these observations given the available frontal and lateral radiographs. On a validation set of 200 chest radiographic studies which were manually annotated by 3 board-certified radiologists, we find that different uncertainty approaches are useful for different pathologies. We then evaluate our best model on a test set composed of 500 chest radiographic studies annotated by a consensus of 5 board-certified radiologists, and compare the performance of our model to that of 3 additional radiologists in the detection of 5 selected pathologies. On Cardiomegaly, Edema, and Pleural Effusion, the model ROC and PR curves lie above all 3 radiologist operating points. We release the dataset to the public as a standard benchmark to evaluate performance of chest radiograph interpretation models. The dataset is freely available at https://stanfordmlgroup.github.io/competitions/chexpert .Comment: Published in AAAI 201

Equilibrium and dynamical properties of two dimensional self-gravitating systems

A system of N classical particles in a 2D periodic cell interacting via long-range attractive potential is studied. For low energy density $U$ a collapsed phase is identified, while in the high energy limit the particles are homogeneously distributed. A phase transition from the collapsed to the homogeneous state occurs at critical energy U_c. A theoretical analysis within the canonical ensemble identifies such a transition as first order. But microcanonical simulations reveal a negative specific heat regime near $U_c$. The dynamical behaviour of the system is affected by this transition : below U_c anomalous diffusion is observed, while for U > U_c the motion of the particles is almost ballistic. In the collapsed phase, finite $N$-effects act like a noise source of variance O(1/N), that restores normal diffusion on a time scale diverging with N. As a consequence, the asymptotic diffusion coefficient will also diverge algebraically with N and superdiffusion will be observable at any time in the limit N \to \infty. A Lyapunov analysis reveals that for U > U_c the maximal exponent \lambda decreases proportionally to N^{-1/3} and vanishes in the mean-field limit. For sufficiently small energy, in spite of a clear non ergodicity of the system, a common scaling law \lambda \propto U^{1/2} is observed for any initial conditions.Comment: 17 pages, Revtex - 15 PS Figs - Subimitted to Physical Review E - Two column version with included figures : less paper waste

Digital health technology-specific risks for medical malpractice liability

The global digital health market is worth approximately 300 billion USD1 and is predicted to grow by up to 25% this year. Consequently, medical professionals are increasingly required to use digital technologies such as telehealth platforms, AI-driven clinical decision-making tools, digitally enabled surgical tools, mHealth technologies, or electronic health care records (EHR), as part of care delivery. These technologies hold clear benefits for enabling more efficient, modern care delivery however there are significant challenges to implementation, including when and how to use them, how to enable an accurate medical diagnosis in a virtual environment, interpretation and relevance of novel data points from digital devices, the potential for automation bias, appropriate utilisation of and engagement with digital disease management platforms and continuity of care in a digital world. Several of these issues have become apparent through the pandemic due to the hasty deployment of novel technologies as ‘bolt-on’ solutions to address standalone challenges in healthcare delivery, without consideration of the broader healthcare architecture. The majority of practicing clinicians are not sufficiently trained in how to safely integrate digital health technologies into the clinical workflow before encountering such technologies in practice. The introduction of digital health technologies may therefore represent a risk for medical error and subsequent malpractice liability. Medical malpractice is frequently defined as a physician’s failure to comply with customary medical practice,2 however the application of this standard in the context of digital health is challenging. What are the accepted norms for history and examination during a telehealth consult? How should these be documented on electronic systems? When is it safe to offer digital first solutions for disease management? What is the custom for clinicians to ensure continuity of care? If there is a medical error, should the error be attributed to the clinician or the artificial intelligence-based clinical decision-making system? In this article we identify and discuss technology specific risks for malpractice liability arising from the rapidly growing use of digital health technologies

Self-supervised learning for medical image classification: a systematic review and implementation guidelines

Abstract Advancements in deep learning and computer vision provide promising solutions for medical image analysis, potentially improving healthcare and patient outcomes. However, the prevailing paradigm of training deep learning models requires large quantities of labeled training data, which is both time-consuming and cost-prohibitive to curate for medical images. Self-supervised learning has the potential to make significant contributions to the development of robust medical imaging models through its ability to learn useful insights from copious medical datasets without labels. In this review, we provide consistent descriptions of different self-supervised learning strategies and compose a systematic review of papers published between 2012 and 2022 on PubMed, Scopus, and ArXiv that applied self-supervised learning to medical imaging classification. We screened a total of 412 relevant studies and included 79 papers for data extraction and analysis. With this comprehensive effort, we synthesize the collective knowledge of prior work and provide implementation guidelines for future researchers interested in applying self-supervised learning to their development of medical imaging classification models

Variation in the erg11 Gene from Pneumocystis jirovecii.

The erg11 gene codes for sterol 14-demethylase (14DM), a key enzyme in sterol biosynthesis and the target for triazole antimycotic drugs. Two sterol composition phenotypes have been described in patients with pneumonia caused by P. jirovecii; one phenotype has a much higher amount of lanosterol derivatives (C31 and C32 sterols) dominated by pneumocysterol. Most of the Pneumocystis-distinct sterols in this phenotype were those with a methyl group present at the C-14 position of the sterol nucleus suggesting that the P. jirovecii present might lack 14DM activity. To gain insight into whether the apparent lack of 14DM activity might be due to mutation of the erg11 gene, we have analyzed a 1,000-bp segment of this 1,800-bp gene in nine specimens of P. jirovecii (from the USA, Italy and Denmark). Thus far, two alleles of the erg11 gene have been observed: allele 1 with G at position 238 and allele 2 with A at this nucleotide position. All three specimens from Italy had allele 2, two of which were shown to contain high proportions of pneumocysterol. The other specimens analyzed had allele 1. The two alleles differ at a single site that is located in an apparent intron. Sequencing of the entire erg11 gene from these and additional samples is in progress

Treatment of low-flow vascular malformations of the extremities using MR-guided high intensity focused ultrasound: preliminary experience

Five patients with painful vascular malformations of the extremities that were refractory to standard treatment and were confirmed as low-flow malformations on dynamic contrast-enhanced magnetic resonance (MR) imaging were treated with MR imaging-guided high intensity focused ultrasound. Daily maximum numeric rating scale scores for pain improved from 8.4 ± 1.5 to 1.6 ± 2.2 (P = .004) at a median follow-up of 9 months (range, 4-36 mo). The size of the vascular malformations decreased on follow-up MR imaging (median enhancing volume, 8.2 mL [0.7-10.1 mL] before treatment; 0 mL [0-2.3 mL] after treatment; P = .018) at a median follow-up of 5 months (range, 3-36 mo). No complications occurred