Expert Observers Research Articles

Оценка пользовательских историй на основе декомпозиции сложности с использованием байесовских сетей

Currently, in Scrum, there are different methods to estimate user stories in terms of effort or complexity. Most of the existing techniques consider factors in a fine grain level; these techniques are not always accurate. Although Planning Poker is the most used method in Scrum to estimate user stories, it is primarily effective in experienced teams since the estimation mostly depends on the observation of experts, but it is difficult when is used by inexperienced teams. In this paper, we present a proposal for complexity decomposition in a coarse grain level, in order to consider important factors for complexity estimation. We use a Bayesian network to represent those factors and their relations. The edges of the network are weighted with the judge of professional practitioners about the importance of the factors. The nodes of the network represent the factors. During the user estimation phase, the Scrum team members introduce the values for each factor; in this way, the network generates a value for the complexity of a User story, which is transformed in a Planning Poker card number, which represents the story points. The purpose of this research is to provide to development teams without experience or without historical data, a method to estimate the complexity of user stories through a model focused on the human aspects of developers.

Организационно-технологическая надежность строительного комплекса региона в условиях ограниченности трудовых ресурсов

The main purpose of the study is to identify the impact of limited labor resources in construction on the organizational and technological reliability of the construction complex. Currently, many construction organizations in the region are experiencing a shortage of construction workers, so for the regional investment and construction complex, the issue of attracting local workers to construction is relevant. A study of the trend of reducing the level of organizational and technological reliability from a decrease in the number of construction workers was conducted. Measures are proposed to increase organizational and technological reliability in the investment and construction complex of the Amur Region, including through the use of the region's own resources in conditions of restrictions. For the regional investment and construction complex, the issue of creating incentives for the recruitment and selection of workers is relevant. Improving working conditions combined with well-designed motivational measures can create a solid foundation for increasing the attractiveness of the worker's work. When writing the article, the method of expert evaluation, observation, analysis and synthesis was used.

The aesthetics of the experts - On the relationship of observers’ and performers’ expertise when perceiving motion aesthetics in freerunning skills

An observer’s perception of motion aesthetics strongly relies on the interplay between aspects of the motion stimuli, the sensory and motor expertise of the observer, and the context in which the stimuli are perceived. However, whether a fit in observers’ and performers’ sensory and motor expertise can boost aesthetic motion perception when observing complex motor skills, is still to be investigated. Thus, it was hypothesized that a fit between observers’ and performers’ sensory and motor expertise could boost aesthetic motion perception of complex motor skills. Expert and intermediate freerunners performed three different freerunning skills. Observers with varying levels of expertise were asked to indicate their perception of motion aesthetics when observing video sequences of expert and intermediate freerunning skill performances. Results indicate that a fit between observers’ and performers’ sensory and motor expertise levels does not boost aesthetic motion perception. In contrast, motor skill performances of expert freerunners are perceived as more aesthetically than intermediate freerunning performances from all three observer groups: expert freerunning observers, intermediate freerunning observers, and laypeople. Instead of a fit between the performer’s and the observer’s sensory and motor expertise, it is argued that object-driven parameters of a complex motor skill performance seem to be related to a rather universal embodied aesthetic motion perception.

Temporal and spatial variability of the fidgety movement descriptors and their relation to head position in automized general movement assessment

In clinical practice, motor development in infants is assessed subjectively. Many researchers propose objective methods, which have numerous limitations, by attaching markers or sensors to the child's limbs. The purpose of this study is to attempt to develop objectified numerical indices to describe the limb movements of infants without interfering with spontaneous activity. 20-minute video recordings of three infants' movements who were purposively selected from 51 subjects were included in the study. The procedure of automatic calculation of head position time in 3 positions was applied. Movement features were determined to allow for the delineation of coefficients describing the movement in numerical values. Presented parameters describe three infant's movement aspects: quality (strength), distribution of postural tonus and asymmetry in relation to head position, described as four independent values. Estimated parameters variability over time was weighted up according to expert observations. The presented method is a direct reflection of infants' observation, currently performed by highly educated and experienced therapists. The interpretability and usefulness of the presented parameters were proved. All parameters estimation is fully automated. The conducted research is a prelude to future work related to creating an objective and repeatable tool, initially monitoring and ultimately supporting early diagnosis for differentiating normal and abnormal motor development.

Cross-Sector Partnerships for Innovation and Growth: Can Creative Industries Support Traditional Sector Innovations?

At the turn of culture and economics, cultural and creative industries (CCIs) stimulate business, technology, and society and drive innovations within individual regions, as well as on a cross-border level. This makes CCIs and thus culture, creativity, and design significant elements of the modern, post-industrial, and knowledge-based economy. The purpose of the paper is to outline the expectations and needs of entrepreneurs in both the creative and traditional sectors for the future cooperation and implementation of cross-sectoral innovations. It is assumed that there are no guidelines on establishing a cross-sectorial process for the efficient transfer of knowledge for innovation between the two sectors, establishing a strong platform of international cooperation for innovations in the region. To obtain information in this area quantitative and qualitative research was done. A series of expert interviews were conducted, and empirical expert observations were made in the form of qualitative surveys and expert assessments. The presented results are based on the summary reports of empirical research activities. The results of the analysis allowed us to determine that the assumption was true and that representatives of both the traditional and creative sectors lack proven models of cooperation and commercialization for joint innovations.

User Story Estimation Based on the Complexity Decomposition Using Bayesian Networks

Currently, in Scrum, there are different methods to estimate user stories in terms of effort or complexity. Most of the existing techniques consider factors in a fine grain level; these techniques are not always accurate. Although Planning Poker is the most used method in Scrum to estimate user stories, it is primarily effective in experienced teams since the estimation mostly depends on the observation of experts, but it is difficult when is used by inexperienced teams. In this paper, we present a proposal for complexity decomposition in a coarse grain level, in order to consider important factors for complexity estimation. We use a Bayesian network to represent those factors and their relations. The edges of the network are weighted with the judge of professional practitioners about the importance of the factors. The nodes of the network represent the factors. During the user estimation phase, the Scrum team members introduce the values for each factor; in this way, the network generates a value for the complexity of a User story, which is transformed in a Planning Poker card number, which represents the story points. The purpose of this research is to provide to development teams without experience or without historical data, a method to estimate the complexity of user stories through a model focused on the human aspects of developers.

Affordability of services for persons with disabilities in the assisted living facilities of Altai Krai: comparative analysis

The object of this research is the accessibility of services for persons with disabilities in the assisted living facilities. Affordability of social services for persons with disabilities is one of the essential components for normalization of their life. Independent quality assessment of conditions of rendering services by assistant living facilities includes the assessment of affordability of services for persons with disabilities as one of the five criteria. The subject of this research is the comparative analysis of affordability of services for persons with disabilities in different assisted living facilities of Altai Krai. The author took active participation in conducting independent quality assessment of the conditions of rendering services by the assisted living facilities – empirical sociological study, the key method of which was the questionnaire-base survey and expert observation. The article reviews the concept of affordability, its consolidation and implementation in the normative documents of international and federal levels. Based on the acquired data on affordability of services of nursing homes for the elderly and care centers of general type, psycho-neurological facilities, the conclusions is made on correspondence between affordability and imposed requirements. Comparative analysis of the data allowed determining the most problem areas in ensuring affordability of services for persons with disabilities in the assisted living facilities of Altai Krai.

High-resolution Micro-CT Myelography to Assess Spinal Subarachnoid Space Changes After Spinal Cord Injury in Rats.

The spinal subarachnoid space (SSAS) is vital for neurologic function. Although SSAS alterations are known to occur after spinal cord injury (SCI), there is a lack of high-resolution imaging studies of the SSAS after SCI in rodents. Therefore, the aim here was to assess changes in the SSAS of rats subjected to graded SCI, using high-resolution micro-CT myelography. Long-Evans adult rats were subjected to mild or severe spinal cord contusion at T9. Imaging studies of SSAS features were carried out in injured rats at acute (day 1) and subacute (day 15) stages postinjury, as well as in control rats, using high-resolution micro-CT myelography with a contrast-enhanced digital subtraction protocol. We studied a total of 33 rats randomly allocated into five experimental groups. Micro-CT myelograms were assessed by expert observers using both qualitative and quantitative criteria. Qualitative and quantitative analyses showed that SCI induces changes in the SSAS that vary as a function of both injury severity and time elapsed after injury. SSAS blockage was the main alteration detected. Moreover, the method used here allowed fine details to be observed in small animals, such as variations in the preferential pathways for contrast medium flow, neuroimaging nerve root enhancement, and leakage of contrast medium due to tearing of the dural sac. Micro-CT myelography provides high-resolution images of changes in the SSAS after SCI in rats and is a useful tool for further experimental studies involving rat SCI in vivo.

Abstract 15550: Fully Automated "Hands-off" Validation of Left Ventricular Ejection Fraction in Children

Introduction: Three-dimensional echocardiography (3DE) has been shown to be superior to 2DE when quantifying chamber dimensions. However, the clinical utility of 3DE has been limited by time-consuming analysis and need for 3D-specific training. This study aims to assess the clinical utility of a “hands-off,” artificial intelligence-driven software program, Philips Dynamic HeartModelA.I. (DHM), in a pediatric population. Hypothesis: We hypothesize that this program will be able to accurately calculate LVEF in children when compared to Cardiac MRI (CMRI) used as a gold standard. Methods: To date, 10 patients (Average Age 14.8±1.9) have undergone CMRI immediately followed by echocardiography at the Children’s Hospital of Philadelphia. Echocardiographic images were analyzed via DHM without manual editing and from full-volume 3D border tracing (3DEF). We excluded 3D imaging of 2 patients due to poor image quality. 2D LVEF was also evaluated visually in all patients by three experts, and by 2D Simpson’s Biplane method. All analyses were timed with a stopwatch. Each technique was correlated with CMRI. Results: Our results showed that LVEF measured via DHM was rapid and correlated very well with CMRI (R=0.85). DHM had a stronger correlation with CMRI than Biplane, 3DEF, and visual EF (R=0.56; R=0.68; R=0.57). Moreover, DHM analysis was faster than both Biplane and 3DEF (32±8 seconds vs 100±19 seconds, p<0.005; 32±8 seconds vs 98±16 seconds, p<0.005) (Figure 1). Notably, 3DEF correlated very well with visual estimation by expert observers (R=0.83). Conclusions: Based on our pilot data, DHM analysis in children was faster than both 3DEF and biplane method likely due to its hands-off nature. Furthermore, its correlation with CMRI was superior to both 3DEF and Biplane. Based on further patient enrollment by us and others, this completely automated method will likely be clinically useful as it has potential to become a standard application in large volume, busy echo labs.

Parkinson's Disease Diagnosis and Severity Assessment Using Ground Reaction Forces and Neural Networks.

Gait analysis plays a key role in the diagnosis of Parkinson’s Disease (PD), as patients generally exhibit abnormal gait patterns compared to healthy controls. Current diagnosis and severity assessment procedures entail manual visual examinations of motor tasks, speech, and handwriting, among numerous other tests, which can vary between clinicians based on their expertise and visual observation of gait tasks. Automating gait differentiation procedure can serve as a useful tool in early diagnosis and severity assessment of PD and limits the data collection to solely walking gait. In this research, a holistic, non-intrusive method is proposed to diagnose and assess PD severity in its early and moderate stages by using only Vertical Ground Reaction Force (VGRF). From the VGRF data, gait features are extracted and selected to use as training features for the Artificial Neural Network (ANN) model to diagnose PD using cross validation. If the diagnosis is positive, another ANN model will predict their Hoehn and Yahr (H&Y) score to assess their PD severity using the same VGRF data. PD Diagnosis is achieved with a high accuracy of 97.4% using simple network architecture. Additionally, the results indicate a better performance compared to other complex machine learning models that have been researched previously. Severity Assessment is also performed on the H&Y scale with 87.1% accuracy. The results of this study show that it is plausible to use only VGRF data in diagnosing and assessing early stage Parkinson’s Disease, helping patients manage the symptoms earlier and giving them a better quality of life.

Validity of a Novel Chicken and Porcine Model for Laparoscopic Neobladder Reconstruction.

Introduction: Simulation-based training in laparoscopic urology is essential, as these surgeries require a skill set different from routine urologic procedures. We aim to describe and validate the chicken and porcine intestine model for laparoscopic neobladder reconstruction. Materials and Methods: Prospective observational study was conducted at our institute. Twenty novice and 20 trained laparoscopic surgeons were included in the study. The relevant chicken anatomy and surgical steps were described to all the surgeons. The surgeons were asked to fill a nine-point questionnaire after completing the exercise comprising bowel organization, ability to do urethroneovesical anastomosis, suturing time, suturing similarity, quality of suturing, tissue feel, integrity of anastomosis, realism, and usefulness of model after finishing the procedure, and score it on a scale of 1-5. Time taken to perform the entire exercise was noted in addition. An independent expert observer retrospectively rated the urethroneovesical anastomosis on a scale of 1-5. Results: All the participants in the study gave a mean score of 3.5 or more to all the questions asked in the questionnaire. Both the groups rated the usefulness of the model highly with a mean score of 4.6 and 4.45, respectively. The mean score of the questionnaire was 35.9 and 36, respectively, for both the groups. The quality of urethroneovesical anastomosis as observed by an expert was better in the expert group (p = 0.001). Conclusion: The chicken and porcine model for laparoscopic neobladder reconstruction is a useful and effective training tool. This model has face, content, and construct validity to be used as a teaching and training tool in laparoscopic urology.

Relationship between electrocardiogram changes and myocardial edema in tako-tsubo syndrome

Abstract Introduction During the acute phase of Tako-tsubo syndrome (TTS) electrocardiogram (ECG) features change and myocardial edema is a common finding in Cardiac Magnetic Resonance (CMR). However, the relationship between ECG changes and myocardial edema is still unclear. Purpose To define if the presence and extension of myocardial edema might be predicted by electrocardiographic changes since admission to CMR. Methods Consecutive patients with TTS undergoing CMR during hospitalization were included. All patients underwent serial electrocardiography during the acute phase. Myocardial edema presence and extension was assessed by expert observers using the 17-segments cardiac segmentation model. ECG were analysed by expert observers blinded to the presence of myocardial edema. ECG pattern as predictors of myocardial edema presence and its extension were analysed by a binary regression and Student's t test. Results A total of 42 patients were included. Admission cQT was 472±60 ms. Thirty patients (71%) developed widespread negative T-waves and 55% showed cQT prolongation (cQT 476±58 ms) by the time of CMR. The median (IQR) time from admission to CMR imaging was 5 days (2–7 days). Myocardial edema was found in 30 patients (71%) and mean number of myocardial segments affected by edema was 4.8±2.5. Widespread negative T-wave development at the time of CMR imaging was associated with a smaller number of myocardial segments with edema. However no significant correlation was found between extension of myocardial edema and QT prolongation. No ECG pattern predicted a full recovery of myocardial edema (Table). Conclusions Development of widespread negative T-wave at the time of CMR predicts smaller myocardial edema extension. However, no other ECG features by the time of CMR were able to predict the full recovery of myocardial edema. Funding Acknowledgement Type of funding source: None

Association Between MRI-Based Tibial Slope Measurements and Mucoid Degeneration of the Anterior Cruciate Ligament: A Propensity Score-Matched Case-Control Study.

Background:The cause of mucoid degeneration (MD) of the anterior cruciate ligament (ACL), which is commonly observed on magnetic resonance imaging (MRI) of patients with knee pain, has yet to be elucidated. Despite the limited evidence on the relationship between ACL lesions (injury and MD) and tibial morphologic features (ie, posterior tibial slope), the potential association between the presence of ACL MD and medial and lateral tibial slope (MTS and LTS) has not been well-established.Purpose:To investigate whether MTS and LTS measurements are associated with the presence of ACL MD.Study Design:Cross-sectional study; Level of evidence, 3.Methods:Consecutive knee MRI examinations of patients referred by an orthopaedic surgeon for potential internal joint derangements were identified within a 4-year period. The presence of ACL MD and the MTS/LTS values were assessed by independent expert observers in consensus in a blinded fashion. From 413 consecutive knee MRI scans, a sample of 80 knees, including 32 knees with ACL MD (cases) and 48 knees with normal ACL (controls), were selected using propensity score matching method for age, sex, body mass index, and presence of severe medial tibiofemoral compartment cartilage damage. The association between ACL MD and MTS/LTS was evaluated using conditional regression models.Results:Knees with ACL MD had higher values of LTS (mean ± SD, 7.18° ± 3.58°) in comparison with control knees (5.32° ± 3.35°). Conditional regression analysis revealed a significant association between LTS measurements (not MTS) and ACL MD; every 1° increase in LTS was associated with a 17% (95% CI, 1%-35%) higher probability of having ACL MD.Conclusion:Excessive LTS was associated with the presence of ACL MD, independent of participants’ age, sex, BMI, and cartilage damage severity.

Ankle fracture classification using deep learning: automating detailed AO Foundation/Orthopedic Trauma Association (AO/OTA) 2018 malleolar fracture identification reaches a high degree of correct classification

Background and purpose — Classification of ankle fractures is crucial for guiding treatment but advanced classifications such as the AO Foundation/Orthopedic Trauma Association (AO/OTA) are often too complex for human observers to learn and use. We have therefore investigated whether an automated algorithm that uses deep learning can learn to classify radiographs according to the new AO/OTA 2018 standards. Method — We trained a neural network based on the ResNet architecture on 4,941 radiographic ankle examinations. All images were classified according to the AO/OTA 2018 classification. A senior orthopedic surgeon (MG) then re-evaluated all images with fractures. We evaluated the network against a test set of 400 patients reviewed by 2 expert observers (MG, AS) independently. Results — In the training dataset, about half of the examinations contained fractures. The majority of the fractures were malleolar, of which the type B injuries represented almost 60% of the cases. Average area under the area under the receiver operating characteristic curve (AUC) was 0.90 (95% CI 0.82–0.94) for correctly classifying AO/OTA class where the most common major fractures, the malleolar type B fractures, reached an AUC of 0.93 (CI 0.90–0.95). The poorest performing type was malleolar A fractures, which included avulsions of the fibular tip. Interpretation — We found that a neural network could attain the required performance to aid with a detailed ankle fracture classification. This approach could be scaled up to other body parts. As the type of fracture is an important part of orthopedic decision-making, this is an important step toward computer-assisted decision-making.

A new approach to monitor and assess the damage caused by two-spotted spider mite.

This study uses an image-processing technique to determine the damage level caused by two-spotted spider mite (Tetranychus urticae Koch) to cucumber plants and changes in the number of mites in a greenhouse. Firstly, a new agricultural platform was developed to ensure camera stability for capturing quality images. The images of 50 leaves infested with T. urticae were capturedweekly for 5weeks with the platform, which resulted in 250 images. Fifty of these captured images were randomly selected and processed with an image-processing algorithm developed using an image processing toolbox module of MATLAB. The results obtained from the image processing algorithm were compared with expert observations. The image-processing method predicted the damage with 3.91 root mean squared error (RMSE). A highly significant positive relationship was found between image processing and expert observations. The results indicate that this new image-processing method may be successfully used in place of expert observation to determine T. urticae damage in greenhouses.

Adaptation of Algorithms for Diagnostics of Steam Turbine Unit Equipment to Specific Conditions at Thermal Power Stations

At present, steam turbine manufacturers are interested in delivering diagnostic equipment systems for steam turbine units (STU) together with automatic control systems. These diagnostic systems should be adapted to a specific turbine size (or modification). The diagnostic system adaptation depends on the algorithm type used to detect defects. The following types of algorithms can be singled out: computational ones determining the defect using a computational model; expert ones based on probabilistic methods (using the summation of weighting coefficients of malfunction symptoms and the Bayes theorem); and digital ones based on the phenomenological relationships among process parameters, equipment state parameters, and other indicators obtained over the test period of equipment’s operation. The computational algorithms can be integrated into the turbine APCS and executed online (such as control system algorithms and steam admission system algorithms) or be used for handling postoperative tasks (such as diagnostics of auxiliary equipment or solving technical and economic problems). Expert algorithms are employed when development of a computational model involves great difficulties (e.g., in diagnosing a turbine thermal-expansion monitoring system or vibration-monitoring system). Digital algorithms are not related to the type of tasks, to specific equipment, or its features or operating modes. The degree of definiteness of a detected defect depends on the type of algorithm. The need to adapt diagnostic systems to other equipment involves not only the development of new diagnostic algorithms but also the formalization of the discourse. By this is meant the selection and justification of a standard state (prototype), i.e., a model state, to be used as a reference for development of diagnostic symptoms and a digital description of defect symptoms determined from the measurements and expert observations and opinions required for diagnosing.

Use of Detomidine Oromucosal Gel for Alleviation of Acute Anxiety and Fear in Horses: A Pilot Study.

The aim of this randomized, double-blind, placebo-controlled, parallel group clinical field study was to evaluate the effect of detomidine oromucosal gel in alleviating anxiety and fear in horses. Sixteen horses with a history of acute anxiety and fear associated with firework-related noise entered the study. On New Year's Eve, eight horses were treated with 30 μg/kg detomidine gel and eight horses with placebo gel. When fireworks were present, 75% (6/8) of the detomidine-treated horses were scored by their owners as having a good or excellent treatment effect on anxiety and fear, while 50% (3/6) of horses receiving placebo were scored to have a good effect. Horses' behavior was video-recorded and assessed with a focal animal continuous method by a treatment-blind expert observer. Results showed that when fireworks were present, walking behavior decreased significantly (p < 0.05) after treatment with detomidine and that horses of the placebo group, overall, showed more restlessness, vocalization, and signs of colic (Wilcoxon matched-pairs test on the first PC, p = 0.007). This study indicates that detomidine oromucosal gel can be used to alleviate acute noise-related anxiety and fear in horses, but larger treatment groups are needed to confirm the results.

A generative adversarial network-based (GAN-based) architecture for automatic fiducial marker detection in prostate MRI-only radiotherapy simulation images.

Clinical sites utilizing magnetic resonance imaging (MRI)-only simulation for prostate radiotherapy planning typically use fiducial markers for pretreatment patient positioning and alignment. Fiducial markers appear as small signal voids in MRI images and are often difficult to discern. Existing clinical methods for fiducial marker localization require multiple MRI sequences and/or manual interaction and specialized expertise. In this study, we develop a robust method for automatic fiducial marker detection in prostate MRI simulation images and quantify the pretreatment alignment accuracy using automatically detected fiducial markers in MRI. In this study, a deep learning-based algorithm was used to convert MRI images into labeled fiducial marker volumes. Seventy-seven prostate cancer patients who received marker implantation prior to MRI and CT simulation imaging were selected for this study. Multiple-Echo T1 -VIBE MRI images were acquired, and images were stratified (at the patient level) based on the presence of intraprostatic calcifications. Ground truth (GT) contours were defined by an expert on MRI using CT images. Training was done using the pix2pix generative adversarial network (GAN) image-to-image translation package and model testing was done using fivefold cross validation. For performance comparison, an experienced medical dosimetrist and a medical physicist each manually contoured fiducial markers in MRI images. The percent of correct detections and F1 classification scores are reported for markers detected using the automatic detection algorithm and human observers. The patient positioning errors were quantified by calculating the target registration errors (TREs) from fiducial marker driven rigid registration between MRI and CBCT images. Target registration errors were quantified for fiducial marker contours defined on MRI by the automatic detection algorithm and the two expert human observers. Ninety-six percent of implanted fiducial markers were correctly identified using the automatic detection algorithm. Two expert raters correctly identified 97% and 96% of fiducial markers, respectively. The F1 classification score was 0.68, 0.75, and 0.72 for the automatic detection algorithm and two human raters, respectively. The main source of false discoveries was intraprostatic calcifications. The mean TRE differences between alignments from automatic detection algorithm and human detected markers and GT were <1mm. We have developed a deep learning-based approach to automatically detect fiducial markers in MRI-only simulation images in a clinically representative patient cohort. The automatic detection algorithm-predicted markers can allow for patient setup with similar accuracy to independent human observers.

Bloodstain Pattern: An open source of Evidence A Case Study

Abstract:The purpose of this paper is to present a case study of a crime committed in a remote area of Pakistanwhere a woman was found dead and her death was reported as a suicide. Investigation agency collectedthe data including the pictures from the crime scene. These pictures were sent to crime scene unit fortheir recommendations. Forensic experts studied and analyzed these pictures and by applying thetechniques of Bloodstain Pattern Analysis (BPA). As blood behaves according to certain scientificprinciples, trained bloodstain pattern analysts can examine the blood evidence left behind and drawconclusions as to how the blood may have been shed. It is done by examining the distribution, location,shape and size of the bloodstains and it helps in determining about what did or did not happen. Becauseblood behaves according to certain scientific principles, trained bloodstain pattern analysts can examinethe blood evidence left behind [and draw conclusions as to how the blood may have been shed]. BPAprovides information not only about what happened, but just as importantly, what could not havehappened.A detailed analysis gave enough evidence to the experts that the submitted case was not a suicide rather itwas a case of a homicide. The paper will attempt to study the observations of experts while analyzing thebloodstain pattern submitted to them and the conclusions they subsequently drawn, leading towards aclear evidence of a murder case.

Photographing Alopecia: How Many Pixels Are Needed for Clinical Evaluation?

Determining the minimum image resolution needed for clinical assessment is crucial for computational efficiency, image standardization, and storage needs alleviation. In this paper, we explore the image resolution requirements for the assessment of alopecia by analyzing how clinicians detect the presence of characteristics needed to quantify the disorder in the clinic. By setting the image resolution as a function of width of the patient's head, we mimicked experiments conducted in the computer vision field to understand human perception in the context of scene recognition and object detection and asked 6 clinicians to identify the regions of interest on a set of retrospectively collected de-identified images at different resolutions. The experts were able to detect the presence of alopecia at very low resolutions, while significantly higher resolution was required to identify the presence of vellus-like hair. Furthermore, the accuracy with which alopecia was detected as a function of resolution followed the same trend as the one obtained when we classified normal versus abnormal hair density using a standard neural network architecture, hinting that the resolution needed by an expert human observer may also provide an upper bound for future image processing algorithms.