Making large art historical photo archives searchable

In recent years, museums, archives and other cultural institutions have initiated important programs to digitize their collections. Millions of artefacts (paintings, engravings, drawings, ancient photographs) are now represented in digital photographic format. Furthermore, through progress in standardization, a growing portion of these images are now available online, in an easily accessible manner. This thesis studies how such large-scale art history collection can be made searchable using new deep learning approaches for processing and comparing images. It takes as a case study the processing of the photo archive of the Foundation Giorgio Cini, where more than 300'000 images have been digitized. We demonstrate how a generic processing pipeline can reliably extract the visual and textual content of scanned images, opening up ways to efficiently digitize large photo-collections. Then, by leveraging an annotated graph of visual connections, a metric is learnt that allows clustering and searching through artwork reproductions independently of their medium, effectively solving a difficult problem of cross-domain image search. Finally, the thesis studies how a complex Web Interface allows users to perform different searches based on this metric. We also evaluate the process by which users can annotate elements of interest during their navigation to be added to the database, allowing the system to be trained further and give better results. By documenting a complete approach on how to go from a physical photo-archive to a state-of-the-art navigation system, this thesis paves the way for a global search engine across the world's photo archives

Visual Patterns Discovery in Large Databases of Paintings

The digitization of large databases of works of arts photographs opens new avenue for research in art history. For instance, collecting and analyzing painting representations beyond the relatively small number of commonly accessible works was previously extremely challenging. In the coming years,researchers are likely to have an easier access not only to representations of paintings from museums archives but also from private collections, fine arts auction houses, art historian However, the access to large online database is in itself not sufficient. There is a need for efficient search engines, capable of searching painting representations not only on the basis of textual metadata but also directly through visual queries. In this paper we explore how convolutional neural network descriptors can be used in combination with algebraic queries to express powerful search queries in the context of art history research

Extracorporeal Membrane Oxygenation for Severe Acute Respiratory Distress Syndrome associated with COVID-19: An Emulated Target Trial Analysis.

RATIONALE: Whether COVID patients may benefit from extracorporeal membrane oxygenation (ECMO) compared with conventional invasive mechanical ventilation (IMV) remains unknown. OBJECTIVES: To estimate the effect of ECMO on 90-Day mortality vs IMV only Methods: Among 4,244 critically ill adult patients with COVID-19 included in a multicenter cohort study, we emulated a target trial comparing the treatment strategies of initiating ECMO vs. no ECMO within 7 days of IMV in patients with severe acute respiratory distress syndrome (PaO2/FiO2 <80 or PaCO2 ≥60 mmHg). We controlled for confounding using a multivariable Cox model based on predefined variables. MAIN RESULTS: 1,235 patients met the full eligibility criteria for the emulated trial, among whom 164 patients initiated ECMO. The ECMO strategy had a higher survival probability at Day-7 from the onset of eligibility criteria (87% vs 83%, risk difference: 4%, 95% CI 0;9%) which decreased during follow-up (survival at Day-90: 63% vs 65%, risk difference: -2%, 95% CI -10;5%). However, ECMO was associated with higher survival when performed in high-volume ECMO centers or in regions where a specific ECMO network organization was set up to handle high demand, and when initiated within the first 4 days of MV and in profoundly hypoxemic patients. CONCLUSIONS: In an emulated trial based on a nationwide COVID-19 cohort, we found differential survival over time of an ECMO compared with a no-ECMO strategy. However, ECMO was consistently associated with better outcomes when performed in high-volume centers and in regions with ECMO capacities specifically organized to handle high demand. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/)

New Techniques for the Digitization of Art Historical Photographic Archives - the Case of the Cini Foundation in Venice

Numerous libraries and museums hold large art historical photographic collections, numbering millions of images. Because of their non-standard format, these collections pose special challenges for digitization. This paper address these difficulties by proposing new techniques developed for the digitization of the photographic archive of the Cini Foundation. This included the creation of a custom-built circular, rotating scanner. The resulting digital images were then automatically indexed, while artificial intelligence techniques were employed in information extraction. Combined, these tools vastly sped processes which were traditionally undertaken manually, paving the way for new ways of exploring the collections

Characteristics, management, and prognosis of elderly patients with COVID-19 admitted in the ICU during the first wave: insights from the COVID-ICU study

International audienceBackground: The COVID-19 pandemic is a heavy burden in terms of health care resources. Future decision-making policies require consistent data on the management and prognosis of the older patients (&gt; 70 years old) with COVID-19 admitted in the intensive care unit (ICU). Methods: Characteristics, management, and prognosis of critically ill old patients (&gt; 70 years) were extracted from the international prospective COVID-ICU database. A propensity score weighted-comparison evaluated the impact of intubation upon admission on Day-90 mortality. Results: The analysis included 1199 (28% of the COVID-ICU cohort) patients (median [interquartile] age 74 [72–78] years). Fifty-three percent, 31%, and 16% were 70–74, 75–79, and over 80 years old, respectively. The most frequent comorbidities were chronic hypertension (62%), diabetes (30%), and chronic respiratory disease (25%). Median Clinical Frailty Scale was 3 (2–3). Upon admission, the PaO2/FiO2 ratio was 154 (105–222). 740 (62%) patients were intubated on Day-1 and eventually 938 (78%) during their ICU stay. Overall Day-90 mortality was 46% and reached 67% among the 193 patients over 80 years old. Mortality was higher in older patients, diabetics, and those with a lower PaO2/FiO2 ratio upon admission, cardiovascular dysfunction, and a shorter time between first symptoms and ICU admission. In propensity analysis, early intubation at ICU admission was associated with a significantly higher Day-90 mortality (42% vs 28%; hazard ratio 1.68; 95% CI 1.24–2.27; p &lt; 0·001). Conclusion: Patients over 70 years old represented more than a quarter of the COVID-19 population admitted in the participating ICUs during the first wave. Day-90 mortality was 46%, with dismal outcomes reported for patients older than 80 years or those intubated upon ICU admission

Extracorporeal Membrane Oxygenation for Severe Acute Respiratory Distress Syndrome Associated with COVID-19: An Emulated Target Trial Analysis

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Predicting 90-day survival of patients with COVID-19: Survival of Severely Ill COVID (SOSIC) scores

International audienceBackground Predicting outcomes of critically ill intensive care unit (ICU) patients with coronavirus-19 disease (COVID-19) is a major challenge to avoid futile, and prolonged ICU stays. Methods The objective was to develop predictive survival models for patients with COVID-19 after 1-to-2 weeks in ICU. Based on the COVID–ICU cohort, which prospectively collected characteristics, management, and outcomes of critically ill patients with COVID-19. Machine learning was used to develop dynamic, clinically useful models able to predict 90-day mortality using ICU data collected on day (D) 1, D7 or D14. Results Survival of Severely Ill COVID (SOSIC)-1, SOSIC-7, and SOSIC-14 scores were constructed with 4244, 2877, and 1349 patients, respectively, randomly assigned to development or test datasets. The three models selected 15 ICU-entry variables recorded on D1, D7, or D14. Cardiovascular, renal, and pulmonary functions on prediction D7 or D14 were among the most heavily weighted inputs for both models. For the test dataset, SOSIC-7’s area under the ROC curve was slightly higher (0.80 [0.74–0.86]) than those for SOSIC-1 (0.76 [0.71–0.81]) and SOSIC-14 (0.76 [0.68–0.83]). Similarly, SOSIC-1 and SOSIC-7 had excellent calibration curves, with similar Brier scores for the three models. Conclusion The SOSIC scores showed that entering 15 to 27 baseline and dynamic clinical parameters into an automatable XGBoost algorithm can potentially accurately predict the likely 90-day mortality post-ICU admission (sosic.shinyapps.io/shiny). Although external SOSIC-score validation is still needed, it is an additional tool to strengthen decisions about life-sustaining treatments and informing family members of likely prognosis

Benefits and risks of noninvasive oxygenation strategy in COVID-19: a multicenter, prospective cohort study (COVID-ICU) in 137 hospitals

International audienceAbstract Rational To evaluate the respective impact of standard oxygen, high-flow nasal cannula (HFNC) and noninvasive ventilation (NIV) on oxygenation failure rate and mortality in COVID-19 patients admitted to intensive care units (ICUs). Methods Multicenter, prospective cohort study (COVID-ICU) in 137 hospitals in France, Belgium, and Switzerland. Demographic, clinical, respiratory support, oxygenation failure, and survival data were collected. Oxygenation failure was defined as either intubation or death in the ICU without intubation. Variables independently associated with oxygenation failure and Day-90 mortality were assessed using multivariate logistic regression. Results From February 25 to May 4, 2020, 4754 patients were admitted in ICU. Of these, 1491 patients were not intubated on the day of ICU admission and received standard oxygen therapy (51%), HFNC (38%), or NIV (11%) ( P < 0.001). Oxygenation failure occurred in 739 (50%) patients (678 intubation and 61 death). For standard oxygen, HFNC, and NIV, oxygenation failure rate was 49%, 48%, and 60% ( P < 0.001). By multivariate analysis, HFNC (odds ratio [OR] 0.60, 95% confidence interval [CI] 0.36–0.99, P = 0.013) but not NIV (OR 1.57, 95% CI 0.78–3.21) was associated with a reduction in oxygenation failure). Overall 90-day mortality was 21%. By multivariable analysis, HFNC was not associated with a change in mortality (OR 0.90, 95% CI 0.61–1.33), while NIV was associated with increased mortality (OR 2.75, 95% CI 1.79–4.21, P < 0.001). Conclusion In patients with COVID-19, HFNC was associated with a reduction in oxygenation failure without improvement in 90-day mortality, whereas NIV was associated with a higher mortality in these patients. Randomized controlled trials are needed

Characteristics and prognosis of bloodstream infection in patients with COVID-19 admitted in the ICU: an ancillary study of the COVID-ICU study

International audienceBackground Patients infected with the severe acute respiratory syndrome coronavirus 2 (SARS-COV 2) and requiring intensive care unit (ICU) have a high incidence of hospital-acquired infections; however, data regarding hospital acquired bloodstream infections (BSI) are scarce. We aimed to investigate risk factors and outcome of BSI in critically ill coronavirus infectious disease-19 (COVID-19) patients. Patients and methods We performed an ancillary analysis of a multicenter prospective international cohort study (COVID-ICU study) that included 4010 COVID-19 ICU patients. For the present analysis, only those with data regarding primary outcome (death within 90 days from admission) or BSI status were included. Risk factors for BSI were analyzed using Fine and Gray competing risk model. Then, for outcome comparison, 537 BSI-patients were matched with 537 controls using propensity score matching. Results Among 4010 included patients, 780 (19.5%) acquired a total of 1066 BSI (10.3 BSI per 1000 patients days at risk) of whom 92% were acquired in the ICU. Higher SAPS II, male gender, longer time from hospital to ICU admission and antiviral drug before admission were independently associated with an increased risk of BSI, and interestingly, this risk decreased over time. BSI was independently associated with a shorter time to death in the overall population (adjusted hazard ratio (aHR) 1.28, 95% CI 1.05–1.56) and, in the propensity score matched data set, patients with BSI had a higher mortality rate (39% vs 33% p = 0.036). BSI accounted for 3.6% of the death of the overall population. Conclusion COVID-19 ICU patients have a high risk of BSI, especially early after ICU admission, risk that increases with severity but not with corticosteroids use. BSI is associated with an increased mortality rate