A step-by-step method for predicting the spectrophotometric response of a carded fabric composed by differently colored raw materials.

The comparison of the finished product's color to the color demanded by a client or provided by a catalogue is one of the most critical steps of the production of carded fibers. This is currently determined by calculating the colorimetric distance between the finished article, which is made by mixing some colored raw fibers to achieve a homogenous color, and the desired one. When this colorimetric distance exceeds a value specified by the customer, the recipe must be modified to close the gap between the end product's color and the required one. The above mentioned trial and error procedure used to determine the appropriate quantities of colored fibers for achieving a desired final color of a fiber lacks a computer-aided tool to assist colorists. Therefore, it is a bottleneck in the process of creating carded fabrics. The present work offers a novel method for predicting the spectrophotometric response of a carded fiber based on the spectrophotometric response of the raw components used to make the fiber. The method is described step-by-step.•The method is applicable to predict the spectrophotometric response of a fabric composed by any number of pre-colored fibers.

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.

Towards the Development of a Device for Assessing the Pliability of Burn Scars.

Burn injuries requires post-accident medical treatment. However, the treatment of burns does not end with first aid because scarred skin must be managed for many years, and in some circumstances, for life. The methods used to evaluate the state of a burn scar based, for instance, on Patient and Observer Scar Assessment Scale or similar ones, often lacks in univocally assessing the scarred skin's state of health. As a result, the primary aim of this research is to design and build a prototype that can support the doctor during scar assessment, and eventually therapy, by providing objective information on the state of the lesion, particularly the value of skin pliability. The developed tool is based on the depressomassage treatment probe named LPG, currently used to treat burn scars in a number of hospitals. It consists of a non-invasive massage technique using a mechanical device to suction and mobilize scar tissue and is used as a post-operative treatment to speed up the healing process to make the mark of the scar less visible. The prototype is specifically designed to be manufactured using Additive Manufacturing and was validated comparing its performances against the ones of a certified instrument (i.e., the Romer Absolute ARM with RS1 probe). Validation was carried out by designing and developing a tool to put the RS1 probe in the same measurement conditions of the new prototype probe. Tests performed to assess the performance of the devised prototype show that the probe developed in this work is able to provide measurements with a sufficient degree of accuracy (maximum error ±0.1 mm) to be adopted for a reliable estimation of the pliability value in a hospital environment.

Case Report: Three-Dimensional Printing Model for Surgical Planning of Left Ventricular Aneurysm: Evolution Toward Tailoring Surgery.

A 59-year-old woman was admitted to the emergency department for heart failure (HF), New York Heart Association (NYHA) IV, showing an anterior, evolved myocardial infarction (MI) with a wide apical left ventricular aneurysm (LVA), ejection fraction (EF) 24%, and global longitudinal strain (GLS) -5. 5% by echo. Cardiac magnetic resonance imaging (MRI) confirmed an apical LVA without thrombus, EF 20%, and a transmural delayed enhancement in the myocardium wall. Coronarography showed a three-vessel disease with occluded proximal left anterior descending (LAD) and proximal right coronary artery (RCA). Based on the cardiac CT scan, we decided to generate a three-dimensional (3D) print model of the heart, for better prediction of residual LV volumes. After LVA surgery plus complete functional revascularization, an optimal agreement was found between predicted and surgical residual LV end-diastolic (24.7 vs. 31.8 ml/m2) and end-systolic (54.1 vs. 69.4 ml/m2) volumes, with an improvement of NYHA class, from IV to I. The patient was discharged uneventfully and at 6- and 12-month follow-up, the NYHA class, and LV volumes were found unchanged. This is a second report describing the use of the 3D print model for the preoperative planning of surgical management of LVA; the first report was described by Jacobs et al. among three patients, one with a malignant tumor and the remaining two patients with LVA. This article focused on the use of the 3D print model to optimize surgical planning and individualize treatment of LVA associated with complete functional revascularization, leading to complete recovery of LV function with a favorable outcome.

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.

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.

RGB-D-Based Method for Measuring the Angular Range of Hip and Knee Joints during Home Care Rehabilitation.

Home-based rehabilitation is becoming a gold standard for patient who have undergone knee arthroplasty or full knee replacement, as it helps healthcare costs to be minimized. Nevertheless, there is a chance of increasing adverse health effects in case of home care, primarily due to the patients' lack of motivation and the doctors' difficulty in carrying out rigorous supervision. The development of devices to assess the efficient recovery of the operated joint is highly valued both for the patient, who feels encouraged to perform the proper number of activities, and for the doctor, who can track him/her remotely. Accordingly, this paper introduces an interactive approach to angular range calculation of hip and knee joints based on the use of low-cost devices which can be operated at home. First, the patient's body posture is estimated using a 2D acquisition method. Subsequently, the 3D posture is evaluated by using the depth information coming from an RGB-D sensor. Preliminary results show that the proposed method effectively overcomes many limitations by fusing the results obtained by the state-of-the-art robust 2D pose estimation algorithms with the 3D data of depth cameras by allowing the patient to be correctly tracked during rehabilitation exercises.

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.

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.

Specific Cutting Forces of Isotropic and Orthotropic Engineered Wood Products by Round Shape Machining.

The set-up of machining parameters for non-ferric materials such as wood and wood-based materials is not yet defined on a scientific basis. In this paper, a new rapid experimental method to assess the specific cutting coefficients when routing isotropic and orthotropic wood-based materials is presented. The method consists of routing, with different depths of cut, a given material previously machined to a round shape after having it fixed on a dynamometric platform able to measure the cutting forces. The execution of subsequent cuts using different depths of cut allows the calculation of the specific cutting coefficients. With the measurement being done during real routing operations, a method to remove machine vibrations was also developed. The specific cutting coefficients were computed for the whole set of grain orientations for orthotropic materials and as an average for isotropic ones. The aim of this paper is to present and validate the whole method by machining selected materials such as Polytetrafluoroethylene-PTFE (isotropic), Medium Density Fiberboard-MDF (isotropic), beech Laminate Veneer Lumber-LVL (orthotropic) and poplar LVL (orthotropic). The method and the proposed analysis have been shown to work very effectively and could be used for optimization and comparison between materials and processes.

Surgery of complex craniofacial defects: A single-step AM-based methodology.

BACKGROUND AND OBJECTIVE: The purpose of the present paper is to pave the road to the systematic optimization of complex craniofacial surgical intervention and to validate a design methodology for the virtual surgery and the fabrication of cranium vault custom plates. Recent advances in the field of medical imaging, image processing and additive manufacturing (AM) have led to new insights in several medical applications. The engineered combination of medical actions and 3D processing steps, foster the optimization of the intervention in terms of operative time and number of sessions needed. Complex craniofacial surgical intervention, such as for instance severe hypertelorism accompanied by skull holes, traditionally requires a first surgery to correctly "resize" the patient cranium and a second surgical session to implant a customized 3D printed prosthesis. Between the two surgical interventions, medical imaging needs to be carried out to aid the design the skull plate. Instead, this paper proposes a CAD/AM-based one-in-all design methodology allowing the surgeons to perform, in a single surgical intervention, both skull correction and implantation. METHODS: A strategy envisaging a virtual/mock surgery on a CAD/AM model of the patient cranium so as to plan the surgery and to design the final shape of the cranium plaque is proposed. The procedure relies on patient imaging, 3D geometry reconstruction of the defective skull, virtual planning and mock surgery to determine the hypothetical anatomic 3D model and, finally, to skull plate design and 3D printing. RESULTS: The methodology has been tested on a complex case study. Results demonstrate the feasibility of the proposed approach and a consistent reduction of time and overall cost of the surgery, not to mention the huge benefits on the patient that is subjected to a single surgical operation. CONCLUSIONS: Despite a number of AM-based methodologies have been proposed for designing cranial implants or to correct orbital hypertelorism, to the best of the authors' knowledge, the present work is the first to simultaneously treat osteotomy and titanium cranium plaque.

A Novel Objective Approach to the External Measurement of Pectus Excavatum Severity by Means of an Optical Device.

BACKGROUND: Current approaches to quantifying the severity of pectus excavatum require internal measurements based on cross-sectional imaging. The aim of this study is to exploit a novel index evaluated on the external surface of the chest with a three-dimensional (3D) optical scanner. METHODS: Fifty-one children (41 male, 10 female) between 2 and 17 years of age were evaluated with a 3D optical scanner. Pectus excavatum severity was calculated by using an ad hoc instant 3D scanner and defining an automatic procedure to generate an optical 3D correction index (CI3D). For the latter, an ideal threshold was derived from a statistical analysis, and five blind surveys were collected from pediatric specialists on chest wall deformities. The CI3D was then correlated with blind clinical assessments of PE severity. RESULTS: The cutoff thresholds were determined to optimally discriminate between six degrees of severity of PE patients by a correlation analysis. The correlation coefficient obtained by matching the CI3D with the average subjective severity shows that the proposed method outperforms traditional approaches. CONCLUSIONS: The optical 3D index has a good match with the average subjective assessment in distinguishing patients with mild to severe PE. This innovative approach offers several advantages over existing indices, as it is repeatable and does not require cross-sectional imaging. The index might be particularly suitable for monitoring the efficacy of nonoperative treatment and, in the future, for designing an optimal personalized usage of therapeutic devices.