Reliable prediction of difficult airway for tracheal intubation from patient preoperative photographs by machine learning methods

Background: Estimating the risk of a difficult tracheal intubation should help clinicians in better anaesthesia planning, to maximize patient safety. Routine bedside screenings suffer from low sensitivity. Objective: To develop and evaluate machine learning (ML) and deep learning (DL) algorithms for the reliable prediction of intubation risk, using information about airway morphology. Methods: Observational, prospective cohort study enrolling $n$=623 patients who underwent tracheal intubation: 53/623 difficult cases (prevalence 8.51%). First, we used our previously validated deep convolutional neural network (DCNN) to extract 2D image coordinates for 27 + 13 relevant anatomical landmarks in two preoperative photos (frontal and lateral views). Here we propose a method to determine the 3D pose of the camera with respect to the patient and to obtain the 3D world coordinates of these landmarks. Then we compute a novel set of $d_M$=59 morphological features (distances, areas, angles and ratios), engineered with our anaesthesiologists to characterize each individual's airway anatomy towards prediction. Subsequently, here we propose four ad hoc ML pipelines for difficult intubation prognosis, each with four stages: feature scaling, imputation, resampling for imbalanced learning, and binary classification (Logistic Regression, Support Vector Machines, Random Forests and eXtreme Gradient Boosting). These compound ML pipelines were fed with the $d_M$=59 morphological features, alongside $d_D$=7 demographic variables. Here we trained them with automatic hyperparameter tuning (Bayesian search) and probability calibration (Platt scaling). In addition, we developed an ad hoc multi-input DCNN to estimate the intubation risk directly from each pair of photographs, i.e. without any intermediate morphological description. Performance was evaluated using optimal Bayesian decision theory. It was compared against experts' judgement and against state-of-the-art methods (three clinical formulae, four ML, four DL models). Results: Our four ad hoc ML pipelines with engineered morphological features achieved similar discrimination capabilities: median AUCs between 0.746 and 0.766. They significantly outperformed both expert judgement and all state-of-the-art methods (highest AUC at 0.716). Conversely, our multi-input DCNN yielded low performance due to overfitting. This same behaviour occurred for the state-of-the-art DL algorithms. Overall, the best method was our XGB pipeline, with the fewest false negatives at the optimal Bayesian decision threshold. Conclusions: We proposed and validated ML models to assist clinicians in anaesthesia planning, providing a reliable calibrated estimate of airway intubation risk, which outperformed expert assessments and state-of-the-art methods. Our novel set of engineered features succeeded in providing informative descriptions for prognosis

Development of machine learning system for airway prediction from facial image with mobile device

Goals: A reliable prognostic tool for a difficult airway (DA) may enhance patients’ safety during orotracheal intubation by decreasing unanticipated DAs. We aim to examine the applicability of an Artificial Intelligence-Deep Learning (AI-DL) algorithm to measure airway’s anatomy, and to predict DA based on published models. Materials and methods: Observational prospective cohort study with n=503 patients recruited at Galdakao-Usansolo and Basurto University Hospitals (Biscay, Spain) between 2018 and 2020. Two pre-operative photos for each patient were collected: a frontal view, in which patients were instructed to open their mouth completely; and a lateral view, with head in vertical ex tension. Smartphones with general-purpose cameras were used, and a cue card was added to the scene as reference. Patients’ medical records were logged. After intubation, HAN score and IDS-ASA criteria for intubation difficulty [1] were collected. Our anaesthesiology team defined a set of relevant orofacial landmarks, whereas our data-science team developed an AI-DL algorithm, trained to identify locate them automatically within the images. In a previous evaluation, the system achieved an accuracy comparable to the consensus of two human annotators [2]. Landmark positions output by the AI-DL method were subsequently used by the system to ex tract two anatomical measurements: thyromental distance and interincisor gap. Finally, these two were integrated into a published model for DA prognosis: Naguib et al. 2006 [3], which also employed patients’ height and Mallampati score. Results and discussion: The estimated incidence of DA was 6.36% (32 out of 503 patients) according to the IDS-ASA criteria. Naguib’s model, when used in combination with our automatic AI-DL based measurements, achieved 53.12% sensitivity and 79.83% specificity; compared to clinicians’ subjective assessment, who obtained 25.00% sensitivity and 93.63% specificity. Conclusion(s): In this work, we evaluated an AI-DL method to predict DA for intubation, with two pre-operative photos and Naguib’s model. Our results complemented expert judgements’ predictive ability in terms of sensitivity, substantially lowering false negatives; at the expense of a restrained loss in specificity (false positives). Thus, our proposal may provide anaesthesiologists with an automatic, objective and accessible decision support tool for the prognosis of DAs

Evaluation of the antimicrobial activity and cytotoxicity of different components of natural origin present in essential oils

Even though essential oils (EOs) have been used for therapeutic purposes, there is now a renewed interest in the antimicrobial properties of phytochemicals and EOs in particular. Their demonstrated low levels of induction of antimicrobial resistance make them interesting for bactericidal applications, though their complex composition makes it necessary to focus on the study of their main components to identify the most effective ones. Herein, the evaluation of the antimicrobial action of different molecules present in EOs against planktonic and biofilm-forming Gram-positive (Staphylococcus aureus) and Gram-negative (Escherichia coli) bacteria was assessed. The bactericidal mechanisms of the different molecules, as well as their cytocompatibility, were also studied. Carvacrol, cinnamaldehyde, and thymol exhibit the highest in vitro antimicrobial activities against E. coli and S. aureus, with membrane disruption the bactericidal mechanism identified. The addition of those compounds (=0.5 mg/mL) hampers S. aureus biofilm formation and partially eliminates preformed biofilms. The subcytotoxic values of the tested EO molecules (0.015–0.090 mg/mL) are lower than the minimum inhibitory and bactericidal concentrations obtained for bacteria (0.2–0.5 mg/mL) but are higher than that obtained for chlorhexidine (0.004 mg/mL), indicating the reduced cytotoxicity of EOs. Therefore, carvacrol, cinnamaldehyde, and thymol are molecules contained in EOs that could be used against E. coli– and S. aureus–mediated infections without a potential induction of bactericidal resistance and with lower cell toxicity than the conventional widely used chlorhexidine

Automated location of orofacial landmarks to characterize airway morphology in anaesthesia via deep convolutional neural networks

Background:A reliable anticipation of a difficult airway may notably enhance safety during anaesthesia. In current practice, clinicians use bedside screenings by manual measurements of patients’ morphology. Objective:To develop and evaluate algorithms for the automated extraction of orofacial landmarks, which characterize airway morphology. Methods:We defined 27 frontal + 13 lateral landmarks. We collected n=317 pairs of pre-surgery photos from patients undergoing general anaesthesia (140 females, 177 males). As ground truth reference for supervised learning, landmarks were independently annotated by two anaesthesiologists. We trained two ad-hoc deep convolutional neural network architectures based on InceptionResNetV2 (IRNet) and MobileNetV2 (MNet), to predict simultaneously: (a) whether each landmark is visible or not (occluded, out of frame), (b) its 2D-coordinates (x, y). We implemented successive stages of transfer learning, combined with data augmentation. We added custom top layers on top of these networks, whose weights were fully tuned for our application. Performance in landmark extraction was evaluated by 10-fold cross-validation (CV) and compared against 5 state-of-the-art deformable models. Results:With annotators’ consensus as the ‘gold standard’, our IRNet-based network performed comparably to humans in the frontal view: median CV loss L=1.277·10-3, inter-quartile range (IQR) [1.001, 1.660]; versus median 1.360, IQR [1.172, 1.651], and median 1.352, IQR [1.172, 1.619], for each annotator against consensus, respectively. MNet yielded slightly worse results: median 1.471, IQR [1.139, 1.982]. In the lateral view, both networks attained performances statistically poorer than humans: median CV loss L=2.141·10-3, IQR [1.676, 2.915], and median 2.611, IQR [1.898, 3.535], respectively; versus median 1.507, IQR [1.188, 1.988], and median 1.442, IQR [1.147, 2.010] for both annotators. However, standardized effect sizes in CV loss were small: 0.0322 and 0.0235 (non-significant) for IRNet, 0.1431 and 0.1518 (p<0.05) for MNet; therefore quantitatively similar to humans. The best performing state-of-the-art model (a deformable regularized Supervised Descent Method, SDM) behaved comparably to our DCNNs in the frontal scenario, but notoriously worse in the lateral view. Conclusions:We successfully trained two DCNN models for the recognition of 27 + 13 orofacial landmarks pertaining to the airway. Using transfer learning and data augmentation, they were able to generalize without overfitting, reaching expert-like performances in CV. Our IRNet-based methodology achieved a satisfactory identification and location of landmarks: particularly in the frontal view, at the level of anaesthesiologists. In the lateral view, its performance decayed, although with a non-significant effect size. Independent authors had also reported lower lateral performances; as certain landmarks may not be clear salient points, even for a trained human eye.BERC.2022-2025 BCAM Severo Ochoa accreditation CEX2021-001142-S / MICIN / AEI / 10.13039/50110001103

The chemical composition of the Orion star-forming region: II. Stars, gas, and dust: the abundance discrepancy conundrum

We re-examine the recombination/collisional emission line (RL/CEL) nebular abundance discrepancy problem in the light of recent high-quality abundance determinations in young stars in the Orion star-forming region. We re-evaluate the CEL and RL abundances of several elements in the Orion nebula and estimate the associated uncertainties, taking into account the uncertainties in the ionization correction factors for unseen ions. We estimate the amount of oxygen trapped in dust grains for several scenarios of dust formation. We compare the resulting gas+dust nebular abundances with the stellar abundances of a sample of 13 B-type stars from the Orion star-forming region (Ori\,OB1), analyzed in Papers I and III of this series. We find that the oxygen nebular abundance based on recombination lines agrees much better with the stellar abundances than the one derived from the collisionally excited lines. This result calls for further investigation. If the CEL/RL abundance discrepancy were caused by temperature fluctuations in the nebula, as argued by some authors, the same kind of discrepancy should be seen for the other elements, such as C, N and Ne, which is not what we find in the present study. Another problem is that with the RL abundances, the energy balance of the Orion nebula is not well understood. We make some suggestions concerning the next steps to undertake to solve this problem.Comment: 11 pages, 8 tables, 5 figures (To be published in A&A

Radiative and Auger decay data for modelling nickel K lines

Radiative and Auger decay data have been calculated for modelling the K lines in ions of the nickel isonuclear sequence, from Ni$^+$ up to Ni$^{27+}$. Level energies, transition wavelengths, radiative transition probabilities, and radiative and Auger widths have been determined using Cowan's Hartree--Fock with Relativistic corrections (HFR) method. Auger widths for the third-row ions (Ni$^+$--Ni$^{10+}$) have been computed using single-configuration average (SCA) compact formulae. Results are compared with data sets computed with the AUTOSTRUCTURE and MCDF atomic structure codes and with available experimental and theoretical values, mainly in highly ionized ions and in the solid state.Comment: submitted to ApJS. 42 pages. 12 figure

3D trapping of microbubbles by the Marangoni force.

In this Letter, we show 3D steady-state trapping and manipulation of vapor bubbles in liquids employing a low-power continuous-wave laser using the Marangoni effect. Light absorption from photodeposited silver nanoparticles on the distal end of a multi-mode optical fiber is used to produce bubbles of different diameters. The thermal effects produced by either the nanoparticles on the fiber tip or the light bulk absorption modulate the surface tension of the bubble wall and creates both longitudinal and transversal forces just like optical forces, effectively creating a 3D potential well. Using numerical simulations, we obtain expressions for the temperature profiles and present analytical expressions for the Marangoni force. In addition, using an array of three fibers with photodeposited nanoparticles is used to demonstrate the transfer of bubbles from one fiber to another by sequentially switching on and off the lasers

A Comprehensive X-ray Absorption Model for Atomic Oxygen

An analytical formula is developed to represent accurately the photoabsorption cross section of O I for all energies of interest in X-ray spectral modeling. In the vicinity of the Kedge, a Rydberg series expression is used to fit R-matrix results, including important orbital relaxation effects, that accurately predict the absorption oscillator strengths below threshold and merge consistently and continuously to the above-threshold cross section. Further minor adjustments are made to the threshold energies in order to reliably align the atomic Rydberg resonances after consideration of both experimental and observed line positions. At energies far below or above the K-edge region, the formulation is based on both outer- and inner-shell direct photoionization, including significant shake-up and shake-off processes that result in photoionization-excitation and double photoionization contributions to the total cross section. The ultimate purpose for developing a definitive model for oxygen absorption is to resolve standing discrepancies between the astronomically observed and laboratory measured line positions, and between the inferred atomic and molecular oxygen abundances in the interstellar medium from XSTAR and SPEX spectral models