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.

Manejo anestésico de histerectomía más doble anexectomía por tumor borderline de ovario izquierdo en paciente con síndrome de sensibilidad química múltiple / Anesthetic management of hysterectomy and double adnexectomy for borderline left ovarian tumor in a patient with multiple chemical sensitivity syndrome

El síndrome de sensibilidad química múltiple (SQM), también conocido como intolerancia ambiental idiopática (IAI), entre otros, es un desorden complejo y mal definido que produce diversos síntomas en respuesta a diferentes estímulos. No hay estudios válidos que establezcan la patogénesis de este síndrome. El manejo anestésico de estos pacientes es un reto para los anestesiólogos, dado que no hay unas pautas de actuación establecidas. Se presenta un caso de cirugía exitosa en una paciente afecta de SQM realizándose una anestesia total intravenosa (TIVA) convencional a la que se añadió una premedicación exhaustiva y la aplicación del protocolo de alergia al látex

The multiple chemical sensitivity syndrome (MCS), also known as idiopathic environmental intolerance (IAI), among others, is a complex and poorly defi ned disorder that produces various symptoms in response to various stimuli. There is a lack of valid studies that establish the pathogenesis of this syndrome. The anesthetic management of these patients is a challenge for anesthesiologists, due to the fact that there are no established guidelines. We present a case of successful surgery in a patient that suffers from MCS by performing a conventional TIVA enhanced with a thorough premedication and the latex allergy protocol