Statistical and artificial intelligence techniques to identify risk factors for suicide in children and adolescents.

BACKGROUND: Suicidal Behaviors and Thoughts are a relevant public health issue that includes suicidal ideation, non-suicidal self-harm, attempted suicide, and failed suicides. Since there is a progression of suicidal behaviors, whereby suicide is more likely to be completed if there have already been previous behaviors or attempts to harm oneself, WHO has highlighted the need to detect early predictors of such suicidal behaviors, which can help identify individuals at risk, plan prevention strategies and implement specific therapeutic interventions, particularly in young people, thus reducing the number of deaths. This retrospective observational study aimed to identify early predictors of suicidal risk in 237 inpatients admitted for Suicidal Behaviors and Thoughts at Child and Adolescent Psychiatry Emergency Unit of the Meyer Children's Hospital, Florence, Italy. METHODS: The study was subdivided into three phases: data collection, statistical analysis, and neural network. For each patient, we collected epidemiological and psychopathological data. We stratified the inpatients into two groups: "suicidal volition patients" and "suicidal motivation patients." RESULTS: The hospitalization rate for suicidal behaviors and thoughts showed a growing trend from 2016 to 2020 (27.69 to 45.28%). Under 12 years of age, diagnosis of disruptive, impulse-control and conduct disorder, previous specialist care, history of attempted suicide, and intoxication as methods of suicide were statistically correlated to an increased risk of suicidal behaviors. Artificial intelligence, with an accuracy of 86.7%, confirmed these risk factors. LIMITATIONS: The most important limitation of the study is its retrospective nature. CONCLUSIONS: Our study identifies new early predictors of suicidal risk: age less than 12, diagnosis of disruptive, impulse-control and conduct disorder. In addition, suicidal volition behavior emerges as an important and underestimated risk factor. The use of artificial intelligence methods could be supporting the clinician in assessing suicidal risk.

Engineering Auxetic Cylinders and Intestine to Improve Longitudinal Intestinal Lengthening and Tailoring Procedure.

Auxetic materials can be exploited for coupling different types of tissues. Herein, we designed a material where the microorganism metabolic activity yields the formation of buckled/collapsed bubbles within gelling silicone cylinders thus providing auxetic properties. The finite element model of such hollow auxetic cylinders demonstrated the tubular structure to promote worm-like peristalsis. In this scenario, the described hybrid auxetic structures may be applied to the longitudinal intestinal lengthening and tailoring procedure to promote enteral autonomy in short bowel syndrome. The presented material and analytical design synergistic approach offer a pioneering step for the clinical translation of hybrid auxetic materials.

Modelling and fabrication procedure for a 3D printed cardiac model - surgical planning of Left Ventricular Aneurysm.

The present paper describes a procedure for the development and production of a physical model for surgical planning of a Left Ventricular Aneurysm. The method is based on the general approach provided in Otton et al. (2017) and was customized to seek a reliable and fast procedure for the production of a specific type of cardiac model - i.e. chambers of the left side of the heart. The paper covers all the steps: processing of the data, segmentation, modelling and 3D printing; details are provided for all the phases, in order to allow the reproduction of the achieved results. The procedure relies on Computed Tomography - CT imaging as data source for the identification and modelling of the anatomy. Materialise Mimics was used as segmentation software to process the CT data. While its usefulness for the surgical needs was verified on a single clinical case (provided by the Careggi Hospital of Florence, Italy), the modelling procedure was tested twice, to produce a physical replica both ex-ante and ex-post surgical intervention.•The tools used for segmentation and generation of the printable model were customized to reduce modelling time for the specific type of desired model.•Detailed information on the use of modeling tools, not available in the literature, will be provided.•The procedure allows fabrication of a physical model representing the heart chambers in a short time.

Machine Learning for Renal Pathologies: An Updated Survey.

Within the literature concerning modern machine learning techniques applied to the medical field, there is a growing interest in the application of these technologies to the nephrological area, especially regarding the study of renal pathologies, because they are very common and widespread in our society, afflicting a high percentage of the population and leading to various complications, up to death in some cases. For these reasons, the authors have considered it appropriate to collect, using one of the major bibliographic databases available, and analyze the studies carried out until February 2022 on the use of machine learning techniques in the nephrological field, grouping them according to the addressed pathologies: renal masses, acute kidney injury, chronic kidney disease, kidney stone, glomerular disease, kidney transplant, and others less widespread. Of a total of 224 studies, 59 were analyzed according to inclusion and exclusion criteria in this review, considering the method used and the type of data available. Based on the study conducted, it is possible to see a growing trend and interest in the use of machine learning applications in nephrology, becoming an additional tool for physicians, which can enable them to make more accurate and faster diagnoses, although there remains a major limitation given the difficulty in creating public databases that can be used by the scientific community to corroborate and eventually make a positive contribution in this area.

Minimizing Enterostomy Complication in Neonates, Lessons Learnt from Three European Tertiary Centres.

INTRODUCTION: Stoma formation in neonates is often a life-saving procedure across a variety of conditions but is still associated with significant morbidity. Tube stoma technique was originally described for short bowel patients, but in selected cases of neonates this approach could prevent the incidence of stoma-related complications. The aim of the study was to evaluate the safety and utility of tube stomas as an alternative to conventional enterostomy in the neonatal population. MATERIAL AND METHODS: A retrospective multicentre analysis of neonates undergoing emergency laparotomy and tube stoma formation between 2005 and 2017 was performed. Tube stoma complications were analysed. The investigation focused on stricture, skin lesion, enteric fistula and prolapse. RESULTS: Thirty-seven neonates underwent tube stoma fashioning during the study period. Tube-stoma complications were limited to three patients (8.1%), with two children (5.4%) requiring additional stoma surgery during the first 30 days because of an enterocutaneous fistula, and one child (2.7%) for bowel stenosis. CONCLUSIONS: In select neonates, such as those with proximal enteric stomas, the tube stoma avoids some of the commonly encountered complications (prolapse, skin excoriation). Further prospective studies are needed to validate these findings in order for us to recommend this technique as superior.

U-net for auricular elements segmentation: a proof-of-concept study.

Convolutional neural networks are increasingly used in the medical field for the automatic segmentation of several anatomical regions on diagnostic and non-diagnostic images. Such automatic algorithms allow to speed up time-consuming processes and to avoid the presence of expert personnel, reducing time and costs. The present work proposes the use of a convolutional neural network, the U-net architecture, for the segmentation of ear elements. The auricular elements segmentation process is a crucial step of a wider procedure, already automated by the authors, that has as final goal the realization of surgical guides designed to assist surgeons in the reconstruction of the external ear. The segmentation, performed on depth map images of 3D ear models, aims to define of the contour of the helix, antihelix, tragus-antitragus and concha. A dataset of 131 ear depth map was created;70% of the data are used as the training set, 15% composes the validation set, and the remaining 15% is used as testing set. The network showed excellent performance, achieving 97% accuracy on the validation test.

G-ear: a user-friendly tool for assisted autologous ear reconstruction.

The major breakthroughs in the fields of reverse engineering and additive manufacturing have dramatically changed medical practice in recent years, pushing for a modern clinical model in which each patient is considered unique. Among the wide spectrum of medical applications, reconstructive surgery is experiencing the most benefits from this new paradigm. In this scenario, the present paper focuses on the design and development of a tool able to support the surgeon in the reconstruction of the external ear in case of malformation or total absence of the anatomy. In particular, the paper describes an appositely devised software tool, named G-ear, which enables the semi-automatic modeling of intraoperative devices to guide the physician through ear reconstruction surgery. The devised system includes 3D image segmentation, semi-automated CAD modelling and 3D printing to manufacture a set of patient-specific surgical guides for ear reconstruction. Usability tests were carried out among the surgeons of the Meyer Children's Hospital to obtain an assessment of the software by the end user. The devised system proved to be fast and efficient in retrieving the optimal 3D geometry of the surgical guides and, at the same time, to be easy to use and intuitive, thus achieving high degrees of likability.

Metrological Characterization and Comparison of D415, D455, L515 RealSense Devices in the Close Range.

RGB-D cameras are employed in several research fields and application scenarios. Choosing the most appropriate sensor has been made more difficult by the increasing offer of available products. Due to the novelty of RGB-D technologies, there was a lack of tools to measure and compare performances of this type of sensor from a metrological perspective. The recent ISO 10360-13:2021 represents the most advanced international standard regulating metrological characterization of coordinate measuring systems. Part 13, specifically, considers 3D optical sensors. This paper applies the methodology of ISO 10360-13 for the characterization and comparison of three RGB-D cameras produced by Intel® RealSense™ (D415, D455, L515) in the close range (100-1500 mm). ISO 10360-13 procedures, which focus on metrological performances, are integrated with additional tests to evaluate systematic errors (acquisition of flat objects, 3D reconstruction of objects). The present paper proposes an off-the-shelf comparison which considers the performance of the sensors throughout their acquisition volume. Results have exposed the strengths and weaknesses of each device. The D415 device showed better reconstruction quality on tests strictly related to the short range. The L515 device performed better on systematic depth errors; finally, the D455 device achieved better results on tests related to the standard.

Preoperative Planning of Spiral Intestinal Lengthening and Tailoring: A Geometrical Approach.

Short bowel syndrome is a pathological condition resulting from extensive resection of the intestine, generally performed due to congenital abnormalities, Crohn's disease, mesenteric ischemia, or neoplasms. The main consequence of this syndrome is a reduction of intestinal absorption, which causes malnutrition and dehydration. In the most severe cases, specific and complex surgical procedures are requested to manage the syndrome. Such procedures consist of the intestinal lengthening, with lead to an increase of absorptive mucosal surface and intestinal transit time and an overall enhancement of intestinal absorption. One of the most promising surgical procedures is spiral intestinal lengthening and tailoring, which consists of a spiral incision of the intestinal wall and in the elongation longitudinally of the intestine by sliding one flap over the other. The final intestinal lengthening is strictly dependent on a series of parameters, some of which are defined by the surgeon. The present paper proposes a mathematical model, based on patient specific anatomical data, which aims to help the surgeon in defining the optimal parameters for the intervention and in foreseeing its outcomes from the preoperative planning phase. Such a tool can assist the physician in the surgery room by improving the procedure and reducing surgical times.

A novel ear elements segmentation algorithm on depth map images.

The growing interest in the auricular anatomy is due to two different strands of research: 1) in the medical field it is associated with autologous ear reconstruction, a surgery adopted following trauma or congenital malformations; 2) in surveillance and law enforcement the ear is used for human detection and recognition. Alternative systems of ear analysis can be differentiated for the type of input data (two-dimensional, three-dimensional or both), for the type of acquisition tools (3D scanner, photographs, video surveillance, etc.) and finally for the adopted algorithms. Although the segmentation and recognition of the ear from the face is a widely discussed topic in literature, the detection and recognition of individual anatomical elements has not yet been studied in depth. To this end, this work lays the foundation for the identification of the auricular elements through image processing algorithms. The proposed algorithm automatically identifies the contours of the main anatomical elements by processing depth map images. The algorithm was tested qualitatively and quantitatively on a dataset composed of 150 ears. The qualitative evaluation was performed with the collaboration of medical staff and the quantitative tests were performed using manually annotated ground truth data.

Modified Oesophago-Gastric Dissociation (M-OGD) - a technical modification.

Adhesions and fibrosis following failed primary surgery for severe gastro-oesophageal reflux (GOR) in neurologically impaired children (NI) can render mobilization of the lower oesophagus and oesophago-jejunal anastomosis a technically demanding exercise both at open surgery and laparoscopy. This paper presents the Modified Oesophago-Gastric Dissociation (M-OGD) as a less complex technical modification of the original Total Oesophago-Gastric Dissociation (TOGD). The stomach is detached from the oesophago-gastric junction with an articulated 5-mm stapler, leaving a 5-mm strip of stomach attached to the oesophagus. An end-to-side isoperistaltic oesophago-jejunostomy is created between the gastric stump and the isoperistaltic jejunal Roux loop. A jejuno-jejunal anastomosis restores bowel continuity. Between May 2018 and February 2020, M-OGD was performed on 3 NI patients with a weight of 9-27.3 kg (median = 14 kg). Median age at surgery was 60 months (18-180), median surgical time 170 min (146-280), median re-feeding time was 3 days (2-5), and median length of stay was 20 days (11-25). All patients healed primarily and after a median follow-up of 3 months, there were no problems related to the oesophago-jejunal anastomosis. M-OGD reduces the difficulties of redo oesophageal surgery following failed anti-reflux procedures, with a safer oesophago-jejunal anastomosis and a good long-term outcome.

Current Practice in Preoperative Virtual and Physical Simulation in Neurosurgery.

In brain tumor surgery, an appropriate and careful surgical planning process is crucial for surgeons and can determine the success or failure of the surgery. A deep comprehension of spatial relationships between tumor borders and surrounding healthy tissues enables accurate surgical planning that leads to the identification of the optimal and patient-specific surgical strategy. A physical replica of the region of interest is a valuable aid for preoperative planning and simulation, allowing the physician to directly handle the patient's anatomy and easily study the volumes involved in the surgery. In the literature, different anatomical models, produced with 3D technologies, are reported and several methodologies were proposed. Many of them share the idea that the employment of 3D printing technologies to produce anatomical models can be introduced into standard clinical practice since 3D printing is now considered to be a mature technology. Therefore, the main aim of the paper is to take into account the literature best practices and to describe the current workflow and methodology used to standardize the pre-operative virtual and physical simulation in neurosurgery. The main aim is also to introduce these practices and standards to neurosurgeons and clinical engineers interested in learning and implementing cost-effective in-house preoperative surgical planning processes. To assess the validity of the proposed scheme, four clinical cases of preoperative planning of brain cancer surgery are reported and discussed. Our preliminary results showed that the proposed methodology can be applied effectively in the neurosurgical clinical practice both in terms of affordability and in terms of simulation realism and efficacy.

Ear Reconstruction Simulation: From Handcrafting to 3D Printing.

Microtia is a congenital malformation affecting one in 5000 individuals and is characterized by physical deformity or absence of the outer ear. Nowadays, surgical reconstruction with autologous tissue is the most common clinical practice. The procedure requires a high level of manual and artistic techniques of a surgeon in carving and sculpting of harvested costal cartilage of the patient to recreate an auricular framework to insert within a skin pocket obtained at the malformed ear region. The aesthetic outcomes of the surgery are highly dependent on the experience of the surgeon performing the surgery. For this reason, surgeons need simulators to acquire adequate technical skills out of the surgery room without compromising the aesthetic appearance of the patient. The current paper aims to describe and analyze the different materials and methods adopted during the history of autologous ear reconstruction (AER) simulation to train surgeons by practice on geometrically and mechanically accurate physical replicas. Recent advances in 3D modelling software and manufacturing technologies to increase the effectiveness of AER simulators are particularly described to provide more recent outcomes.