19 research outputs found
SINDROME DA ANTICORPI ANTIFOSFOLIPIDI E LESIONI PUNTIFORMI DELLA SOSTANZA BIANCA ALLA RISONANZA MAGNETICA. QUAL E' IL LORO SIGNIFICATO? VALUTAZIONE DI 169 PAZIENTI CON MANIFESTAZIONI NEUROLOGICHE
No full textCorrection: Six minute walk distance and reference values in healthy Italian children: A cross-sectional study (PLoS ONE (2018) 13, 10 (e0205792) DOI:10.1371/journal.pone.0205792)
No full textThe affiliation for the fifth author is incorrect. Roberto Codella is not affiliated with #4–8 but with #4 and #8: School of Exercise Sciences, Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy and Metabolism Research Center, IRCCS Policlinico San Donato, San Donato Milanese, Italy
Investigating the generalizability of Economic Evaluations conducted in Italy: a critical review
No full textbstract: Aim. To assess the methodological quality of Italian HEEs and their generalizability or
transferability to different settings.
Methods. A literature search was performed on Pubmed search engine to identify trial-based, non-
experimental prospective studies or model-based full economic evaluations, carried out in Italy from
1995 to 2013. The studies were randomly assigned to four reviewers who applied a detailed checklist
to assess the generalizability and the quality of reporting. The review process followed a three-step
blinded procedure. The reviewers who carried out the data extraction were blind as to the name of the
author(s) of each study. Second, after the first review, articles were re-assigned through a second blind
randomization to a second reviewer. Finally, any disagreement between the first two reviews was
solved by a senior researcher.
Results. One-hundred fifty-one economic evaluations eventually met the inclusion criteria. Over time,
we observed an increasing transparency of methods and a greater generalizability of results, along
with a wider and more representative sample in trials and a larger adoption of transition-Markov
models. On the other hand, often context-specific economic evaluations are carried out and not enough
effort is done to assure the transferability of their results to other contexts. In recent studies, Cost-
Effectiveness Analyses and the use of the Incremental Cost-Effectiveness Ratio were preferred.
Conclusion. Despite a quite positive temporal trend, generalizability of results still appears as an
unsolved question, even if some indication of improvement within Italian studies has been observe
Investigating the Generalizability of Economic Evaluations Conducted in Italy: A Critical Review
No full textObjectives To assess the methodological quality of Italian health economic evaluations and their generalizability or transferability to different settings. Methods A literature search was performed on the PubMed search engine to identify trial-based, nonexperimental prospective studies or model-based full economic evaluations carried out in Italy from 1995 to 2013. The studies were randomly assigned to four reviewers who applied a detailed checklist to assess the generalizability and quality of reporting. The review process followed a three-step blinded procedure. The reviewers who carried out the data extraction were blind as to the name of the author(s) of each study. Second, after the first review, articles were reassigned through a second blind randomization to a second reviewer. Finally, any disagreement between the first two reviewers was solved by a senior researcher. Results One hundred fifty-one economic evaluations eventually met the inclusion criteria. Over time, we observed an increasing transparency in methods and a greater generalizability of results, along with a wider and more representative sample in trials and a larger adoption of transition-Markov models. However, often context-specific economic evaluations are carried out and not enough effort is made to ensure the transferability of their results to other contexts. In recent studies, cost-effectiveness analyses and the use of incremental cost-effectiveness ratio were preferred. Conclusions Despite a quite positive temporal trend, generalizability of results still appears as an unsolved question, even if some indication of improvement within Italian studies has been observed
Validation of the New MINI-CUBE for Clinic Determination of Erythrocyte Sedimentation Rate
No full textbackground: erythrocyte sedimentation rate (ESR) indirectly measures blood fibrinogen, and it is altered by all those pathological conditions that modify the aggregation of red blood cells. the international guidelines by the international council for standardization in hematology (ICSH) define the westergren method as the gold standard for ESR, although it is completely operator-dependent, time-consuming, and requires a patient's blood consumption. therefore, the validation of new ESR analyzers is needed. the aim of this study is the validation of a new automated ESR analyzer, MINI-CUBE (DIESSE, Diagnostica Senese, Italy). methods: the samples (n = 270) were collected at the university hospital of the university of rome tor vergata. a comparison between the automated instrument and the gold standard was performed. statistical analyses were processed by medCalc software. results: the comparison analysis performed on the overall samples reported a good agreement, showing a spearman rank correlation coefficient of 0.94 (P < 0.001), compared to the westergren test. the bland-altman analysis showed a mean bias of 1.5 (maximum (max.):19.6; minimum (min.):-16.6). Inter-run (level 1 coefficient of variation (CV): 4.9%; level 2 CV: 0.8%), intra-run (level 1 CV: 21.1%; level 2 CV: 3.2%), and inter-instrument (level 1 CV: 27.1%; level 2 CV: 5.6%) precision were also assessed. the hematocrit value did not interfere with the analysis: spearman rank correlation coefficient of 0.929 (P < 0.001); mean bias of 1.3 (max.:18.3; min.:-15.6). conclusions: overall results from MINI-CUBE asserted a good correlation rate with the gold standard, and it could be considered an accurate, and objective alternative for the westergren test
Hemodynamic effects of creatine phosphate in patients with congestive heart failure: A double‐blind comparison trial versus placebo
No full textPROSPECTIVE WORK FOR ALMA: THE MILLIMETERWAVE AND SUBMILLIMETERWAVE SPECTRUM OF C-GLYCOLALDEHYDE
No full textThis work is supported by the Programme National de Physico-Chimie du Milieu Interstellaire (PCMI-CNRS) and by the contract ANR-08-BLAN-0054.Author Institution: Laboratoire PhLAM, UMR8523 CNRS-Universite; Lille 1, F-59655 Villeneuve d'Ascq Cedex, France; UMR6226 CNRS-Ecole Nationale; Superieure de Chimie de Rennes,F-35700 Rennes, FranceGlycolaldehyde has been identified in interstellar sources ~{\bf 554}(2001) L81 ; R.~A.~H.~Butler, F.~C.~De~Lucia, D.~T~Petkie, et al., {\em Astrophys.~J.~Supp.}~{\bf 134} (2001) 319 ; M.~T.~Beltran, C.~Codella, S.~Viti, R.~Niri, R.~Cesaroni, {\em Astrophys.~J.}~{\bf 690} (2009) L93.}. The relative abundance ratios of the three isomers (acetic acid) : (glycolaldehyde) : (methylformate) were estimated . The detection of C and C isotopomers of methylformate has been recently reported in Orion, as a result of the detailled labororatory spectroscopic study~{\bf 500} (2009) 1109.}. Therefore the spectroscopy of the C isotopomers of glycolaldehyde is investigated in laboratory in order to provide data for an astronomical search. The instrument ALMA will certainly be a good instrument to detect them. Up to now, only the microwave spectra of CHOH-CHO and of CHOH-CHO have been observed several years ago in the 12-40 GHz range~{\bf 16} (1973) 259.}. Spectra of both species are presently recorded in Lille in the 150-950 GHz range with the new submillimetre-wave spectrometer based on harmonic generation of a microwave synthesizer source, using only solid-state devices, and coupled to a cell of 2.2 m length~{\bf 264} (2010) 94.}. The absolute accuracy of the line positions is better than 30 KHz. The rotational structure of the ground state and of the three first excited vibrational states has been observed. Two C enriched samples were used. The analysis is in progress
MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis
No full textThis data repository for MedMNIST v1 is out of date! Please check the latest version of MedMNIST v2.
Abstract
We present MedMNIST, a collection of 10 pre-processed medical open datasets. MedMNIST is standardized to perform classification tasks on lightweight 28x28 images, which requires no background knowledge. Covering the primary data modalities in medical image analysis, it is diverse on data scale (from 100 to 100,000) and tasks (binary/multi-class, ordinal regression and multi-label). MedMNIST could be used for educational purpose, rapid prototyping, multi-modal machine learning or AutoML in medical image analysis. Moreover, MedMNIST Classification Decathlon is designed to benchmark AutoML algorithms on all 10 datasets; We have compared several baseline methods, including open-source or commercial AutoML tools. The datasets, evaluation code and baseline methods for MedMNIST are publicly available at https://medmnist.github.io/.
Please note that this dataset is NOT intended for clinical use.
We recommend our official code to download, parse and use the MedMNIST dataset:
pip install medmnist
Citation and Licenses
If you find this project useful, please cite our ISBI'21 paper as:
Jiancheng Yang, Rui Shi, Bingbing Ni. "MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis," arXiv preprint arXiv:2010.14925, 2020.
or using bibtex:
@article{medmnist,
title={MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis},
author={Yang, Jiancheng and Shi, Rui and Ni, Bingbing},
journal={arXiv preprint arXiv:2010.14925},
year={2020}
}
Besides, please cite the corresponding paper if you use any subset of MedMNIST. Each subset uses the same license as that of the source dataset.
PathMNIST
Jakob Nikolas Kather, Johannes Krisam, et al., "Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study," PLOS Medicine, vol. 16, no. 1, pp. 1–22, 01 2019.
License: CC BY 4.0
ChestMNIST
Xiaosong Wang, Yifan Peng, et al., "Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases," in CVPR, 2017, pp. 3462–3471.
License: CC0 1.0
DermaMNIST
Philipp Tschandl, Cliff Rosendahl, and Harald Kittler, "The ham10000 dataset, a large collection of multisource dermatoscopic images of common pigmented skin lesions," Scientific data, vol. 5, pp. 180161, 2018.
Noel Codella, Veronica Rotemberg, Philipp Tschandl, M. Emre Celebi, Stephen Dusza, David Gutman, Brian Helba, Aadi Kalloo, Konstantinos Liopyris, Michael Marchetti, Harald Kittler, and Allan Halpern: “Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)”, 2018; arXiv:1902.03368.
License: CC BY-NC 4.0
OCTMNIST/PneumoniaMNIST
Daniel S. Kermany, Michael Goldbaum, et al., "Identifying medical diagnoses and treatable diseases by image-based deep learning," Cell, vol. 172, no. 5, pp. 1122 – 1131.e9, 2018.
License: CC BY 4.0
RetinaMNIST
DeepDR Diabetic Retinopathy Image Dataset (DeepDRiD), "The 2nd diabetic retinopathy – grading and image quality estimation challenge," https://isbi.deepdr.org/data.html, 2020.
License: CC BY 4.0
BreastMNIST
Walid Al-Dhabyani, Mohammed Gomaa, Hussien Khaled, and Aly Fahmy, "Dataset of breast ultrasound images," Data in Brief, vol. 28, pp. 104863, 2020.
License: CC BY 4.0
OrganMNIST_{Axial,Coronal,Sagittal}
Patrick Bilic, Patrick Ferdinand Christ, et al., "The liver tumor segmentation benchmark (lits)," arXiv preprint arXiv:1901.04056, 2019.
Xuanang Xu, Fugen Zhou, et al., "Efficient multiple organ localization in ct image using 3d region proposal network," IEEE Transactions on Medical Imaging, vol. 38, no. 8, pp. 1885–1898, 2019.
License: CC BY 4.
The Bipolar X-Ray Jet of the Classical T Tauri Star DG Tau
No full textThis is the author accepted manuscript. The final version is available from the Astronomical Society of the Pacific via the link in this record16th Cambridge Workshop on Cool Stars, Stellar Systems and the Sun, 28 August-3 September 2010, Seattle, USAWe report on new X-ray observations of the classical T Tauri star DG
Tau. DG Tau drives a collimated bi-polar jet known to be a source of X-ray emission
perhaps driven by internal shocks. The rather modest extinction permits study of the
jet system to distances very close to the star itself. Our initial results presented here
show that the spatially resolved X-ray jet has been moving and fading during the past
six years. In contrast, a stationary, very soft source much closer (≈ 0.15 − 0.2
′′) to
the star but apparently also related to the jet has brightened during the same period. We
report accurate temperatures and absorption column densities toward this source, which
is probably associated with the jet base or the jet collimation region.Swiss National Science Foundatio
Validation of artificial intelligence prediction models for skin cancer diagnosis using dermoscopy images: the 2019 International Skin Imaging Collaboration Grand Challenge
No full textBackground: Previous studies of artificial intelligence (AI) applied to dermatology have shown AI to have higher diagnostic classification accuracy than expert dermatologists; however, these studies did not adequately assess clinically realistic scenarios, such as how AI systems behave when presented with images of disease categories that are not included in the training dataset or images drawn from statistical distributions with significant shifts from training distributions. We aimed to simulate these real-world scenarios and evaluate the effects of image source institution, diagnoses outside of the training set, and other image artifacts on classification accuracy, with the goal of informing clinicians and regulatory agencies about safety and real-world accuracy. Methods: We designed a large dermoscopic image classification challenge to quantify the performance of machine learning algorithms for the task of skin cancer classification from dermoscopic images, and how this performance is affected by shifts in statistical distributions of data, disease categories not represented in training datasets, and imaging or lesion artifacts. Factors that might be beneficial to performance, such as clinical metadata and external training data collected by challenge participants, were also evaluated. 25 331 training images collected from two datasets (in Vienna [HAM10000] and Barcelona [BCN20000]) between Jan 1, 2000, and Dec 31, 2018, across eight skin diseases, were provided to challenge participants to design appropriate algorithms. The trained algorithms were then tested for balanced accuracy against the HAM10000 and BCN20000 test datasets and data from countries not included in the training dataset (Turkey, New Zealand, Sweden, and Argentina). Test datasets contained images of all diagnostic categories available in training plus other diagnoses not included in training data (not trained category). We compared the performance of the algorithms against that of 18 dermatologists in a simulated setting that reflected intended clinical use. Findings: 64 teams submitted 129 state-of-the-art algorithm predictions on a test set of 8238 images. The best performing algorithm achieved 58·8% balanced accuracy on the BCN20000 data, which was designed to better reflect realistic clinical scenarios, compared with 82·0% balanced accuracy on HAM10000, which was used in a previously published benchmark. Shifted statistical distributions and disease categories not included in training data contributed to decreases in accuracy. Image artifacts, including hair, pen markings, ulceration, and imaging source institution, decreased accuracy in a complex manner that varied based on the underlying diagnosis. When comparing algorithms to expert dermatologists (2460 ratings on 1269 images), algorithms performed better than experts in most categories, except for actinic keratoses (similar accuracy on average) and images from categories not included in training data (26% correct for experts vs 6% correct for algorithms, p<0·0001). For the top 25 submitted algorithms, 47·1% of the images from categories not included in training data were misclassified as malignant diagnoses, which would lead to a substantial number of unnecessary biopsies if current state-of-the-art AI technologies were clinically deployed. Interpretation: We have identified specific deficiencies and safety issues in AI diagnostic systems for skin cancer that should be addressed in future diagnostic evaluation protocols to improve safety and reliability in clinical practice. Funding: Melanoma Research Alliance and La Marató de TV3. © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 licens
