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.

HyM3D: A hybrid method for the automatic 3D reconstruction of a defective cranial vault.

BACKGROUND AND OBJECTIVE: The ability to accomplish a consistent restoration of a missing or deformed anatomical area is a fundamental step for defining a custom implant, especially in the maxillofacial and cranial reconstruction where the aesthetical aspect is crucial for a successful surgical outcome. At the same time, this task is also the most difficult, time-consuming, and complicated across the whole reconstruction process. This is mostly due to the high geometric complexity of the anatomical structures, insufficient references, and significant interindividual anatomical heterogeneity. Numerous solutions, specifically for the neurocranium, have been put forward in the scientific literature to address the reconstruction issue, but none of them has yet been persuasive enough to guarantee an easily automatable approach with a consistent shape reconstruction. METHODS: This work aims to present a novel reconstruction method (named HyM3D) for the automatic restoration of the exocranial surface by ensuring both the symmetry of the resulting skull and the continuity between the reconstructive patch and the surrounding bone. To achieve this goal, the strengths of the Template-based methods are exploited to provide knowledge of the missing or deformed region and to guide a subsequent Surface Interpolation-based algorithm. HyM3D is an improved version of a methodology presented by the authors in a previous publication for the restoration of unilateral defects. Differently from the first version, the novel procedure applies to all kinds of cranial defects, whether they are unilateral or not. RESULTS: The presented method has been tested on several test cases, both synthetic and real, and the results show that it is reliable and trustworthy, providing a consistent outcome with no user intervention even when dealing with complex defects. CONCLUSIONS: HyM3D method proved to be a valid alternative to the existing approaches for the digital reconstruction of a defective cranial vault; furthermore, with respect to the current alternatives, it demands less user interaction since the method is landmarks-independent and does not require any patch adaptation.

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.

A computational method for the investigation of burn scars topology based on 3D optical scan.

Burn scar treatment is a difficult subject to address since the improper therapy can have a significant impact on people's quality of life. The evaluation of medical therapy over time should be based on objective measurement of the severity of burn scars and their progression. Unfortunately, most clinical assessments of scars are still reliant on physicians' subjective exams of patients. A profitable method to overcome the limitations of subjective assessment could be to leverage 3D scanning technologies. These could be used to retrieve the surface topology of burns. Accordingly, the goal of this study is to provide an objective approach for analysing the surface topology of burn scars using 3D scanning and roughness-based evaluation. In particular, two types of ISO-compliant profile and surface filters (Gaussian and Wavelet) derived from the analysis of roughness in the mechanical sector are implemented to discriminate form from roughness of scars. Once retrieved, the roughness surface is processed to derive a set of statistical parameters describing the scar surface topology. Three case studies were used to derive these parameters (a synthetic surface, an ostrich-skin surface and a set of scars). After the surface's roughness was determined, a comparison between healthy and unhealthy skin could be established. The devised methods prove their effectiveness in correctly retrieving the main surface characteristics of a burn scar. Therefore, by using the proposed method it will be possible to evaluate the effectiveness of medical therapy by comparing the healthy and scarred skin of a single subject.

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.

3D-Printed Patient-Specific Casts for the Distal Radius in Children: Outcome and Pre-Market Survey.

BACKGROUND: Orthopaedic and Trauma surgery is expected to undergo profound transformation as a result of the adoption of 3D technology. Among the various applications, patient specific manufacturing of splints and casts would appear to be, particularly in children, an interesting implementation. This study aims to assess the safety of patient specific 3D casts obtained with a newly developed 3D-scanning devise in a small case series. We therefore conducted a clinical outcome and pre-marketing study in 10 consecutive patients with distal radius fractures treated at an Academic Level I Pediatric Trauma Center. After the application of the 3D cast, patients underwent three consecutive evaluations in the following 21 days. The main outcome measurements were: pain, skin lesions and general comfort, and acceptance of the cast. The three domains were measured with the Visual Analogue Scale (VAS), the NPUAP/EPUAP classification and the Positive affect-Negative affect Scale for Children (PANAS-C), the Self-Assessment Manikin (SAM) clinical psychology tests and a Likert-type five item questionnaire, respectively. A final mechanical analysis of the cast was carried out to confirm product integrity. RESULTS: The results obtained were consistently positive in the investigated domains of general comfort, efficacy of contention and mechanical integrity of the 3D-printed cast as well as in the practicability of the supply chain. CONCLUSIONS: This study provides Level IV evidence that patient specific 3D printed casts obtained with a specifically designed software were safe in the management of "buckle" fractures of the distal radius in children. These results encourage to extend the technology to the treatment of more demanding fractures.

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.

Wearable Robots: An Original Mechatronic Design of a Hand Exoskeleton for Assistive and Rehabilitative Purposes.

Robotic devices are being employed in more and more sectors to enhance, streamline, and augment the outcomes of a wide variety of human activities. Wearable robots arise indeed as of-vital-importance tools for telerehabilitation or home assistance targeting people affected by motor disabilities. In particular, the field of "Robotics for Medicine and Healthcare" is attracting growing interest. The development of such devices is a primarily addressed topic since the increasing number of people in need of rehabilitation or assistive therapies (due to population aging) growingly weighs on the healthcare systems of the nation. Besides, the necessity to move to clinics represents an additional logistic burden for patients and their families. Among the various body parts, the hand is specially investigated since it most ensures the independence of an individual, and thus, the restoration of its dexterity is considered a high priority. In this study, the authors present the development of a fully wearable, portable, and tailor-made hand exoskeleton designed for both home assistance and telerehabilitation. Its purpose is either to assist patients during activities of daily living by running a real-time intention detection algorithm or to be used for remotely supervised or unsupervised rehabilitation sessions by performing exercises preset by therapists. Throughout the mechatronic design process, special attention has been paid to the complete wearability and comfort of the system to produce a user-friendly device capable of assisting people in their daily life or enabling recorded home rehabilitation sessions allowing the therapist to monitor the state evolution of the patient. Such a hand exoskeleton system has been designed, manufactured, and preliminarily tested on a subject affected by spinal muscular atrophy, and some results are reported at the end of the article.

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.

Outpatient monitoring of Pectus Excavatum: a Neural Network-based approach.

Pectus Excavatum (PE) is a congenital anomaly of the ribcage, at the level of the sterno-costal plane, which consists of an inward angle of the sternum, in the direction of the spine. PE is the most common of all thoracic malformations, with an incidence of 1 in 300-400 people. To monitor the progress of the pathology, severity indices, or thoracic indices, have been used over the years. Among these indices, recent studies focus on the calculation of optical measures, calculated on the optical scan of the patient's chest, which can be very accurate without exposing the patient to invasive treatments such as CT scans. In this work, data from a sample of PE patients and corresponding doctors' severity assessments have been collected and used to create a decision tool to automatically assign a severity value to the patient. The idea is to provide the physician with an objective and easy to use measuring instrument that can be exploited in an outpatient clinic context. Among several classification tools, a Probabilistic Neural Network was chosen for this task for its simple structure and learning mode.

CAD-based automatic modelling of customized cutting templates for Pectus Arcuatum surgical correction.

Pectus Arcuatum (PA) is a congenital chest wall deformity which produces a superior manubrial and sternal protrusion, particularly at the sternal angle. PA surgical correction to reduce the angle of the sternum always includes the removal of bone portion by means of horizontal sternal osteotomies, resection of deformed rib cartilage and finally stabilization of the anterior thoracic wall. Within this process an incorrect assessment of the sternotomy angle during the procedure may lead to the need for bone or cartilage grafts to fill the left voids. This problem has been addressed with a patient-specific cutting template, realized with Reverse Engineering and Additive Manufacturing techniques, which proved to be a key element to simplify the procedure and avoid the occurrence of this type of complications. In this work is presented and validated a procedure that, through common CAD operations, realizes in a completely automatic way the CAD model of the custom cutting template, so as to make non-expert users independent in the realization of the medical device.

Scene Acquisition with Multiple 2D and 3D Optical Sensors: A PSO-Based Visibility Optimization.

Designing an acquisition system for 2D or 3D information, based on the integration of data provided by different sensors is a task that requires a labor-intensive initial design phase. Indeed, the definition of the architecture of such acquisition systems needs to start from the identification of the position and orientation of the sensors observing the scene. Their placement is carefully studied to enhance the efficacy of the system. This often coincides with the need to maximize the surfaces observed by the sensors or some other metric. An automatic optimization procedure based on the Particle Swarm Optimization (PSO) algorithm, to seek the most convenient setting of multiple optical sensors observing a 3D scene, is proposed. The procedure has been developed to provide a fast and efficient tool for 2D and 3D data acquisition. Three different objective functions of general validity, to be used in future applications, are proposed and described in the text. Various filters are introduced to reduce computational times of the whole procedure. The method is capable of handling occlusions from undesired obstacle in the scene. Finally, the entire method is discussed with reference to 1) the development of a body scanner for the arm-wrist-hand district and 2) the acquisition of an internal environment as case studies.

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.

Pectus Carinatum: a non-invasive and objective measurement of severity.

To date, standard methods for assessing the severity of chest wall deformities are mostly linked to X-ray and CT scans. However, the use of radiations limits their use when there is a need to monitor the development of the pathology over time. This is particularly important when dealing with patients suffering from Pectus Carinatum, whose treatment mainly requires the use of corrective braces and a systematic supervision. In recent years, the assessment of severity of chest deformities by means of radiation-free devices became increasingly popular but not yet adopted as standard clinical practice. The present study aims to define an objective measure by defining a severity index (named External Pectus Carinatum Index) used to monitor the course of the disease during treatment. Computed on the optical acquisition of the patients' chest by means of an appositely devised, fast and easy-to-use, body scanner, the proposed index has been validated on a sample composed of a control group and a group of Pectus Carinatum patients. The index proved to be reliable and accurate in the characterization of the pathology, enabling the definition of a threshold that allows to distinguish the cases of patients with PC from those of healthy subjects. Graphical abstract.

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.

Metrological and Critical Characterization of the Intel D415 Stereo Depth Camera.

Low-cost RGB-D cameras are increasingly being used in several research fields, including human⁻machine interaction, safety, robotics, biomedical engineering and even reverse engineering applications. Among the plethora of commercial devices, the Intel RealSense cameras have proven to be among the most suitable devices, providing a good compromise between cost, ease of use, compactness and precision. Released on the market in January 2018, the new Intel model RealSense D415 has a wide acquisition range (i.e., ~160⁻10,000 mm) and a narrow field of view to capture objects in rapid motion. Given the unexplored potential of this new device, especially when used as a 3D scanner, the present work aims to characterize and to provide metrological considerations for the RealSense D415. In particular, tests are carried out to assess the device performance in the near range (i.e., 100⁻1000 mm). Characterization is performed by integrating the guidelines of the existing standard (i.e., the German VDI/VDE 2634 Part 2) with a number of literature-based strategies. Performance analysis is finally compared against the latest close-range sensors, thus providing a useful guidance for researchers and practitioners aiming to use RGB-D cameras in reverse engineering applications.

Customized Cutting Template to Assist Sternotomy in Pectus Arcuatum.

PURPOSE: Pectus arcuatum is an anterior chest wall deformity that requires transverse wedge sternotomy. Determining and delivering the correct cutting angle are crucial for successful correction. This report describes the early clinical experience with a novel cutting template technology able to deliver the optimal cutting angle. DESCRIPTION: From patients' computed tomographic scans, the optimal cutting angle is obtained using computer-aided design. A template comprising slots tilted at the right cutting angle and a safety block to avoid damaging the posterior periosteum is printed through additive manufacturing. EVALUATION: The template allows surgeons to perform a precise wedge sternotomy, safely sparing the posterior periosteum in all patients, without complications. Postoperative chest roentgenograms and clinical photographs demonstrate optimal sternal realignment and cosmetic outcome. In this report, the mean operative time was 110 minutes. All patients were successfully discharged, with a mean length of stay of 4 days. CONCLUSIONS: Transverse wedge sternotomy aided by a computer-aided design-devised cutting template may reduce the technical challenge of this procedure, thereby increasing its safety and reducing operative times and hospital stay. Further research on long-term patient outcomes is necessary.