Can thickness of subcutaneous fat tissue limit emergency percutaneous cricothyrotomy?

Background Needle cricothyrotomy is a method of maintaining airways in emergency situations. One of identified factors that can make this procedure difficult to perform is ‘difficult neck anatomy’ or short obese neck. Due to the growing problem of obesity, we decided to estimate feasibility of needle cricothyrotomy by measuring the thickness of neck fat tissue in the population. Evaluation of this method is important because it is the only method that can be legally performed by paramedics in Poland. The aim of the study was to estimate feasibility of needle cricoidectomy by evaluating the percentage of population in which the thickness of subcutaneous fat tissue could potentially limit or complicate such a procedure. Methods In this retrospective study we reviewed CT scans of the neck from a database at the Department of Radiology, University Hospital in Cracow. 550 CT scans met inclusion criteria: age of patient over 18 years old, lack of any lesions altering anatomy of measured region of neck, the first CT scan of patient. 50.36% of patients were women. Median age was 61 years (range 18 – 93). The distance from the skin surface at the level of the lower edge of the thyroid cartilage to the cricothyroid ligament (surface - ligament distance, SLD) was measured. Statistical analysis of the data was performed using R software (R version 4.0.3). Results Median SLD was 1.41 (1.01 - 2.04). Subcutaneous fat tissue was thicker than maximal depth of application of cricothyrotomy (3cm) device in 31 patients (5.64%). Conclusions Performing needle cricothyrotomy may be limited in a considerable percentage of population (5.64%).

Oxford Handbook of Head and Neck Anatomy

A sound knowledge of anatomy forms the scientific backbone of medicine and surgery – equipping the owner with the tools necessary to understand, remember and ultimately manage the clinical scenarios encountered throughout ones career. The Oxford Handbook of clinical head and neck anatomy is a novel venture for the Oxford Handbook series into the field of anatomy. It tackles the notoriously difficult three-dimensional anatomy of the head and neck and provides clinically relevant correlates, etymology and a brief insight into the people behind the commonly encountered eponymous structures and syndromes. Traditional anatomy texts and atlases can be overwhelming in terms of volume, descriptions and accompanying images. The Oxford Handbook of clinical Head and neck anatomy offers a succinct yet comprehensive, portable and quick reference guide with over 400 colour and grey-scale illustrations. The schematic nature of the illustrations makes them readily reproducible on the operating theatre whiteboard, outpatient clinic or ward, making it a true bedside companion. It is written and illustrated by a trainee in oral and maxillofacial surgery, with an awareness of the needs of those in a similar position, combined with firsthand insight into the difficulties faced by those attempting to learn and link the anatomy taught in pre-clinical years with cases seen in day to day clinical practice thereafter. This book is aimed at medical and dental students, trainees at all levels in Oral and Maxillofacial surgery, ENT surgery and plastic and reconstructive surgery, and radiologists and anaesthetists with an interest in head and neck. Furthermore, it provides an authoritative source for those preparing for the Member of the Royal College of Surgeons and Member of the Faculty of Dental Surgeons exams.

Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study

Background Over half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay treatment while also introducing interoperator variability, resulting in downstream radiation dose differences. Although auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying, and achieving expert performance remain. Objective Adopting a deep learning approach, we aim to demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck organs at risk commonly segmented in clinical practice. Methods The model was trained on a data set of 663 deidentified computed tomography scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations created by experienced radiographers as part of this research, all in accordance with consensus organ at risk definitions. Results We demonstrated the model’s clinical applicability by assessing its performance on a test set of 21 computed tomography scans from clinical practice, each with 21 organs at risk segmented by 2 independent experts. We also introduced surface Dice similarity coefficient, a new metric for the comparison of organ delineation, to quantify the deviation between organ at risk surface contours rather than volumes, better reflecting the clinical task of correcting errors in automated organ segmentations. The model’s generalizability was then demonstrated on 2 distinct open-source data sets, reflecting different centers and countries to model training. Conclusions Deep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.

Carotid artery stenosis in correlation with neck and carotid artery anatomy

Objectives The purpose of this study was to examine the relationship between neck anatomy, especially its largest muscle – sternocleidomastoid and carotid space, with carotid artery anatomy and stenosis. Methods We analysed 102 computed tomography carotid angiograms. The study included the measurement of the neck and sternocleidomastoid length, diameter and volume and the size of the carotid space. Analysis of carotid artery geometry, the length, angle and height of carotid artery bifurcation and the direction of the internal carotid artery origin was also included. Results We found a positive correlation only between the neck and carotid length. There was no correlation between other neck characteristics and a carotid anatomy or internal carotid artery stenosis. Direction of internal carotid artery origin was significantly different (p < 0.01) between the left and right sides. Conclusions We have not found a correlation between the size of sternocleidomastoid and carotid space and carotid stenosis as a hypothetical factor for atherosclerosis. Also, the degree of carotid artery stenosis did not correlate with other neck and carotid measurements. Neck and carotid anatomy correlated only in their lengths. The left internal carotid artery showed mostly posterolateral origin, and right internal carotid artery had no predominate direction.