Automatic Assessment Research Articles

Objective and automatic assessment of bradykinesia in Parkinson’s patients using new repetitive pointing task with machine learning approach

Objective and automatic assessment of bradykinesia in Parkinson’s patients using new repetitive pointing task with machine learning approach

A deep learning-based automatic image quality assessment method for respiratory phase on computed tomography chest images.

Computed tomography (CT) chest scans have become commonly used in clinical diagnosis. Image quality assessment (IQA) for CT images plays an important role in CT examination. It is worth noting that IQA is still a manual and subjective process, and even experienced radiologists make mistakes due to human limitations (fatigue, perceptual biases, and cognitive biases). There are also kinds of biases because of poor consensus among radiologists. Excellent IQA methods can reliably give an objective evaluation result and also reduce the workload of radiologists. This study proposes a deep learning (DL)-based automatic IQA method, to assess whether the image quality of respiratory phase on CT chest images are optimal or not, so that the CT chest images can be used in the patient's physical condition assessment. This retrospective study analysed 212 patients' chest CT images, with 188 patients allocated to a training set (150 patients), validation set (18 patients), and a test set (20 patients). The remaining 24 patients were used for the observer study. Data augmentation methods were applied to address the problem of insufficient data. The DL-based IQA method combines image selection, tracheal carina segmentation, and bronchial beam detection. To automatically select the CT image containing the tracheal carina, an image selection model was employed. Afterward, the area-based approach and score-based approach were proposed and used to further optimize the tracheal carina segmentation and bronchial beam detection results, respectively. Finally, the score about the image quality of the patient's respiratory phase images given by the DL-based automatic IQA method was compared with the mean opinion score (MOS) given in the observer study, in which four blinded experienced radiologists took part. The DL-based automatic IQA method achieved good performance in assessing the image quality of the respiratory phase images. For the CT sequence of the same patient, the DL-based IQA method had an accuracy of 92% in the assessment score, while the radiologists had an accuracy of 88%. The Kappa value of the assessment score between the DL-based IQA method and radiologists was 0.75, with a sensitivity of 85%, specificity of 91%, positive predictive value (PPV) of 92%, negative predictive value (NPV) of 93%, and accuracy of 88%. This study develops and validates a DL-based automatic IQA method for the respiratory phase on CT chest images. The performance of this method surpassed that of the experienced radiologists on the independent test set used in this study. In clinical practice, it is possible to reduce the workload of radiologists and minimize errors caused by human limitations.

Cover Image

The cover image is based on the Research Article SVTNet: Automatic bone age assessment network based on TW3 method and vision transformer by Jindong Wu et al., https://doi.org/10.1002/ima.22990image

Pediatric Critical Care Fellow Perception of Learning through Virtual Reality Bronchoscopy.

Virtual reality (VR) simulators have revolutionized training in bronchoscopy, offering unrestricted availability in a low-stakes learning environment and frequent assessments represented by automatic scoring. The VR assessments can be used to monitor and support learners' progression. How trainees perceive these assessments needs to be clarified. The objective of this study was to examine what assessments learners select to document and receive feedback on and what influences their decisions. We used a sequential explanatory mixed methods strategy. All participants were pediatric critical care medicine trainees requiring competency in bronchoscopy skills. During independent simulation practice, we collected the number of learning-focused practice attempts (scores not recorded), assessment-focused practice (scores recorded and reviewed by the instructor for feedback), and the amount of time each attempt lasted. After simulation training, we conducted interviews to explore learners' perceptions of assessment. There was no significant difference in the number of attempts for each practice type. The average time per learning-focused attempt was almost three times longer than the assessment-focused attempt (mean [standard deviation] 16 ± 1 min vs. 6 ± 3 min, respectively; P < 0.05). Learners perceived documentation of their scores as high stakes and only recorded their better scores. Learners felt safer experimenting if their assessments were not recorded. During independent practice, learners took advantage of automatic assessments generated by the VR simulator to monitor their progression. However, the recording of scores from the simulation program to document learners' trajectory to a set goal was perceived as high stakes, discouraging learners from seeking supervisor feedback.

Developing an AI-assisted digital auscultation tool for automatic assessment of the severity of mitral regurgitation: protocol for a cross-sectional, non-interventional study

IntroductionMitral regurgitation (MR) is the most common valvular heart disorder, with a morbidity rate of 2.5%. While echocardiography is commonly used in assessing MR, it has many limitations, especially for...

Development of Automatic Assessment Framework for Spine Deformity Using Freehand 3-D Ultrasound Imaging System.

A 3-D ultrasound (US) imaging technique has been studied to facilitate the diagnosis of spinal deformity without radiation. The objective of this article is to propose an assessment framework to automatically estimate spinal deformity in US spine images. The proposed framework comprises four major components, a US spine image generator, a novel transformer-based lightweight spine detector network, an angle evaluator, and a 3-D modeler. The principal component analysis (PCA) and discriminative scale space tracking (DSST) method are first adopted to generate the US spine images. The proposed detector is equipped with a redundancy queries removal (RQR) module and a regularization item to realize accurate and unique detection of spine images. Two clinical datasets, a total of 273 images from adolescents with idiopathic scoliosis, are used for the investigation of the proposed framework. The curvature is estimated by the angle evaluator, and the 3-D mesh model is established by the parametric modeling technique. The accuracy rate (AR) of the proposed detector can be achieved at 99.5%, with a minimal redundancy rate (RR) of 1.5%. The correlations between automatic curve measurements on US spine images from two datasets and manual measurements on radiographs are 0.91 and 0.88, respectively. The mean absolute difference (MAD) and standard deviation (SD) are 2.72° ± 2.14° and 2.91° ± 2.36° , respectively. The results demonstrate the effectiveness of the proposed framework to advance the application of the 3-D US imaging technique in clinical practice for scoliosis mass screening and monitoring.

Advancing Emergency Supplies Management: A Blockchain‐Based Traceability System for Cold‐Chain Medicine Logistics

AbstractAdvancing cold‐chain medicine logistics and emergency supplies management necessitates a robust blockchain‐based traceability system. This specialized information system is essential for enhancing quality control and operational efficiency. Despite challenges like insufficient supplies, ineffective distribution, and delays, blockchain's transparency and traceability offer significant potential to revolutionize national emergency management. This paper presents a blockchain‐based traceability system tailored for cold‐chain medicine logistics. This system achieves traceability and automatic medicine quality assessment by integrating blockchain principles and leveraging hazard analysis theory combined with IoT technology. This system also enhances data reliability with a multi‐sensor data fusion algorithm, improving temperature accuracy beyond conventional methods. Additionally, the feasibility and necessity of integrating blockchain into cold‐chain pharmaceutical traceability processes is thoroughly examined. The proposed system fulfills traceability needs for emergency cold‐chain medicine while addressing industry challenges.

A case-control study on the clinical characteristics of granisetron-related arrhythmias and the development of a predictive nomogram.

Automatic monitoring and assessment are increasingly employed in drug safety evaluations using hospital information system data. The increasing concern about granisetron-related arrhythmias requires real-world studies to improve our understanding of its safety. This study aimed to analyze the incidence, clinical characteristics, and risk factors of granisetron-related arrhythmias in hospitalized patients using real-world data obtained from the Adverse Drug Event Active Surveillance and Assessment System-II (ADE-ASAS-II) and concurrently aimed to develop and validate a nomogram to predict the occurrence of arrhythmias. Retrospective automatic monitoring of inpatients using granisetron was conducted in a Chinese hospital from January 1, 2017, to December 31, 2021, to determine the incidence of arrhythmias using ADE-ASAS- II. Propensity score matching was used to balance confounders and analyze clinical characteristics. Based on risk factors identified through logistic regression analysis, a prediction nomogram was established and internally validated using the Bootstrap method. Arrhythmias occurred in 178 of 72,508 cases taking granisetron with an incidence of 0.3%. Independent risk factors for granisetron-related arrhythmias included medication duration, comorbid cardiovascular disease, concomitant use of other 5-hydroxytryptamine 3 receptor antagonists, alanine aminotransferase > 40 U/L, and blood urea nitrogen > 7.5mmol/L. The nomogram demonstrated good differentiation and calibration, with enhanced clinical benefit observed when the risk threshold ranged from 0.10 to 0.82. The nomogram, based on the five identified independent risk factors, may be valuable in predicting the risk of granisetron-related arrhythmias in the administered population, offering significant clinical applications.

Automatic assessment of infant carrying and holding using at-home wearable recordings

Assessing infant carrying and holding (C/H), or physical infant-caregiver interaction, is important for a wide range of contexts in development research. An automated detection and quantification of infant C/H is particularly needed in long term at-home studies where development of infants’ neurobehavior is measured using wearable devices. Here, we first developed a phenomenological categorization for physical infant-caregiver interactions to support five different definitions of C/H behaviors. Then, we trained and assessed deep learning-based classifiers for their automatic detection from multi-sensor wearable recordings that were originally used for mobile assessment of infants’ motor development. Our results show that an automated C/H detection is feasible at few-second temporal accuracy. With the best C/H definition, the automated detector shows 96% accuracy and 0.56 kappa, which is slightly less than the video-based inter-rater agreement between trained human experts (98% accuracy, 0.77 kappa). The classifier performance varies with C/H definition reflecting the extent to which infants’ movements are present in each C/H variant. A systematic benchmarking experiment shows that the widely used actigraphy-based method ignores the normally occurring C/H behaviors. Finally, we show proof-of-concept for the utility of the novel classifier in studying C/H behavior across infant development. Particularly, we show that matching the C/H detections to individuals’ gross motor ability discloses novel insights to infant-parent interaction.

Automatic quantitative stroke severity assessment based on Chinese clinical named entity recognition with domain-adaptive pre-trained large language model

Background:Stroke is a prevalent disease with a significant global impact. Effective assessment of stroke severity is vital for an accurate diagnosis, appropriate treatment, and optimal clinical outcomes. The National Institutes of Health Stroke Scale (NIHSS) is a widely used scale for quantitatively assessing stroke severity. However, the current manual scoring of NIHSS is labor-intensive, time-consuming, and sometimes unreliable. Applying artificial intelligence (AI) techniques to automate the quantitative assessment of stroke on vast amounts of electronic health records (EHRs) has attracted much interest. Objective:This study aims to develop an automatic, quantitative stroke severity assessment framework through automating the entire NIHSS scoring process on Chinese clinical EHRs. Methods:Our approach consists of two major parts: Chinese clinical named entity recognition (CNER) with a domain-adaptive pre-trained large language model (LLM) and automated NIHSS scoring. To build a high-performing CNER model, we first construct a stroke-specific, densely annotated dataset “Chinese Stroke Clinical Records” (CSCR) from EHRs provided by our partner hospital, based on a stroke ontology that defines semantically related entities for stroke assessment. We then pre-train a Chinese clinical LLM coined “CliRoberta” through domain-adaptive transfer learning and construct a deep learning-based CNER model that can accurately extract entities directly from Chinese EHRs. Finally, an automated, end-to-end NIHSS scoring pipeline is proposed by mapping the extracted entities to relevant NIHSS items and values, to quantitatively assess the stroke severity. Results:Results obtained on a benchmark dataset CCKS2019 and our newly created CSCR dataset demonstrate the superior performance of our domain-adaptive pre-trained LLM and the CNER model, compared with the existing benchmark LLMs and CNER models. The high F1 score of 0.990 ensures the reliability of our model in accurately extracting the entities for the subsequent automatic NIHSS scoring. Subsequently, our automated, end-to-end NIHSS scoring approach achieved excellent inter-rater agreement (0.823) and intraclass consistency (0.986) with the ground truth and significantly reduced the processing time from minutes to a few seconds. Conclusion:Our proposed automatic and quantitative framework for assessing stroke severity demonstrates exceptional performance and reliability through directly scoring the NIHSS from diagnostic notes in Chinese clinical EHRs. Moreover, this study also contributes a new clinical dataset, a pre-trained clinical LLM, and an effective deep learning-based CNER model. The deployment of these advanced algorithms can improve the accuracy and efficiency of clinical assessment, and help improve the quality, affordability and productivity of healthcare services.

Investigating the relationship between math literacy and linguistic synchrony in online mathematical discussions through large‐scale data analytics

AbstractPrevious literature has associated math literacy with linguistic factors such as verbal ability and phonological skills. However, few studies have investigated linguistic synchrony, shown in mathematical discussions. This study modelled math literacy and examined the relationship of math literacy with linguistic synchrony between students and facilitators. We retrieved data from 20,776 online mathematical discussion threads at a secondary school level. First, we assessed students' math literacy based on their discussions and classified them into high‐ and low‐math literacy groups. Then, we conducted Cross‐Recurrence Quantification Analysis (CRQA) to calculate linguistic synchrony within each thread. The result implies that students with high math literacy are more likely to share common words (eg, mathematical terms) with facilitators. At the same time, they would paraphrase the facilitators' words rather than blindly mimic them as the exact sentences or phrases. On the other hand, students with low math literacy tend to use overlapping words with facilitators less frequently and are more likely to repeat the exact same phrases from the facilitators. The findings provide an empirical data analysis and insights into mathematical discussions and linguistic synchrony. In addition, this paper implies the directions to improve online mathematical discussions and foster math literacy. Practitioner notesWhat is already known about this topic Mathematical discussions are known to be an effective way to promote math literacy. Math literacy and linguistic skills have a strong link. Linguistic synchrony is related to better collaboration and common knowledge building. What this paper adds Reveals the relationship between math literacy and linguistic synchrony and deepens the understanding of digital communication in online learning environments. Provides empirical analysis of natural language data in group discussions using CRQA. Conceptualizes linguistic synchrony with three sub‐concepts: linguistic concurrence, predictability, and complexity. Implications for practice and/or policy Educators and practitioners could utilize the automatic formative assessment of math literacy based on the student's language use in mathematical discussions. Educational technology researchers and designers could include CRQA indices and recurrence plots in the dashboard design to provide information to support teachers and learners. Teachers would be able to provide real‐time interventions to promote effective mathematical communication and foster math literacy throughout mathematical discussions.

Automatic rehabilitation assessment method of upper limb motor function based on posture and distribution force.

The clinical rehabilitation assessment methods for hemiplegic upper limb motor function are often subjective, time-consuming, and non-uniform. This study proposes an automatic rehabilitation assessment method for upper limb motor function based on posture and distributed force measurements. Azure Kinect combined with MediaPipe was used to detect upper limb and hand movements, and the array distributed flexible thin film pressure sensor was employed to measure the distributed force of hand. This allowed for the automated measurement of 30 items within the Fugl-Meyer scale. Feature information was extracted separately from the affected and healthy sides, the feature ratios or deviation were then fed into a single/multiple fuzzy logic assessment model to determine the assessment score of each item. Finally, the total score of the hemiplegic upper limb motor function assessment was derived. Experiments were performed to evaluate the motor function of the subjects' upper extremities. Bland-Altman plots of physician and system scores showed good agreement. The results of the automated assessment system were highly correlated with the clinical Fugl-Meyer total score (r = 0.99, p < 0.001). The experimental results state that this system can automatically assess the motor function of the affected upper limb by measuring the posture and force distribution.

How can valid and reliable automatic formative assessment predict the acquisition of learning outcomes?

AbstractBackgroundSound learning design should be based on the constructive alignment of intended learning outcomes (LOs), teaching and learning activities and formative and summative assessment. Assessment validity strongly relies on its alignment with LOs. Valid and reliable formative assessment can be analysed as a predictor of students' academic performance, but the question is how significant its predictive power is, and what other elements can affect predictions.ObjectivesOur aim was to investigate the predictive power of formative assessment for summative assessment, measuring the acquisition of LOs.MethodsWe analysed formative assessment results (quizzes, homework), together with log data (video and other material use, class attendance), to determine the most influential predictors and establish a reliable predictive learning analytics model. We used the Random Forest algorithm. The model is based on the data from two university mathematical courses, delivered at different years and levels of study, incorporating 813 students in two consecutive years.Results and ConclusionsOur results show that formative assessment, together with previous summative assessment, is a stronger predictor of summative assessment results than other data on students' engagement. The study pointed to the importance of completeness and quality of data, and clear links between assessment and LOs when making predictions of student results. It suggested that predictions are less reliable for the lowest and the highest performing students. It was noted that other factors can also affect predictions, like the level of LOs, or factors not easily extracted from digital data, like the learning environment and individual students' strategies.

Automated Grading and Feedback Tools for Programming Education: A Systematic Review

We conducted a systematic literature review on automated grading and feedback tools for programming education. We analysed 121 research papers from 2017 to 2021 inclusive and categorised them based on skills assessed, approach, language paradigm, degree of automation, and evaluation techniques. Most papers assess the correctness of assignments in object-oriented languages. Typically, these tools use a dynamic technique, primarily unit testing, to provide grades and feedback to the students or static analysis techniques to compare a submission with a reference solution or with a set of correct student submissions. However, these techniques’ feedback is often limited to whether the unit tests have passed or failed, the expected and actual output, or how they differ from the reference solution. Furthermore, few tools assess the maintainability, readability, or documentation of the source code, with most using static analysis techniques, such as code quality metrics, in conjunction with grading correctness. Additionally, we found that most tools offered fully automated assessment to allow for near-instantaneous feedback and multiple resubmissions, which can increase student satisfaction and provide them with more opportunities to succeed. In terms of techniques used to evaluate the tools’ performance, most papers primarily use student surveys or compare the automatic assessment tools to grades or feedback provided by human graders. However, because the evaluation dataset is frequently unavailable, it is more difficult to reproduce results and compare tools to a collection of common assignments.

Automatic speech-based assessment to discriminate Parkinson’s disease from essential tremor with a cross-language approach

Parkinson’s disease (PD) and essential tremor (ET) are prevalent movement disorders that mainly affect elderly people, presenting diagnostic challenges due to shared clinical features. While both disorders exhibit distinct speech patterns—hypokinetic dysarthria in PD and hyperkinetic dysarthria in ET—the efficacy of speech assessment for differentiation remains unexplored. Developing technology for automatic discrimination could enable early diagnosis and continuous monitoring. However, the lack of data for investigating speech behavior in these patients has inhibited the development of a framework for diagnostic support. In addition, phonetic variability across languages poses practical challenges in establishing a universal speech assessment system. Therefore, it is necessary to develop models robust to the phonetic variability present in different languages worldwide. We propose a method based on Gaussian mixture models to assess domain adaptation from models trained in German and Spanish to classify PD and ET patients in Czech. We modeled three different speech dimensions: articulation, phonation, and prosody and evaluated the models’ performance in both bi-class and tri-class classification scenarios (with the addition of healthy controls). Our results show that a fusion of the three speech dimensions achieved optimal results in binary classification, with accuracies up to 81.4 and 86.2% for monologue and /pa-ta-ka/ tasks, respectively. In tri-class scenarios, incorporating healthy speech signals resulted in accuracies of 63.3 and 71.6% for monologue and /pa-ta-ka/ tasks, respectively. Our findings suggest that automated speech analysis, combined with machine learning is robust, accurate, and can be adapted to different languages to distinguish between PD and ET patients.

A Motion-Aware DNN Model with Edge Focus Loss and Quality Control for Short-Axis Left Ventricle Segmentation of Cine MR Sequences.

Accurate segmentation of the left ventricle myocardium is the key step of automatic assessment of cardiac function. However, the current methods mainly focus on the end-diastolic and the end-systolic frames in cine MR sequences and lack the attention to myocardial motion in the cardiac cycle. Additionally, due to the lack of fine segmentation tools, the simplified approach, excluding papillary muscles and trabeculae from myocardium, is applied in clinical practice. To solve these problems, we propose a motion-aware DNN model with edge focus loss and quality control in this paper. Specifically, the bidirectional ConvLSTM layer and a new motion attention layer are proposed to encode motion-aware feature maps, and an edge focus loss function is proposed to train the model to generate the fine segmentation results. Additionally, a quality control method is proposed to filter out the abnormal segmentations before subsequent analyses. Compared with state-of-the-art segmentation models on the public dataset and the in-house dataset, the proposed method has obtained high segmentation accuracy. On the 17-segment model, the proposed method has obtained the highest Pearson correlation coefficient at 14 of 17 segments, and the mean PCC of 85%. The experimental results highlight the segmentation accuracy of the proposed method as well as its availability to substitute for the manually annotated boundaries for the automatic assessment of cardiac function.

A Cyber-physical-social systems approach to the semantic segmentation of pulmonary embolism

Cyber-Physical-Social Systems (CPSS) epitomize the modern era’s intelligent connectivity. They integrate physical devices, computer networks, and social networks, forming an innovative paradigm for intelligent systems. Utilizing CPSS to enhance intelligence, automation, and remote services in healthcare represents a primary research focus. Pulmonary embolism, a prevalent condition resulting from the blockage of the pulmonary artery and its branches by emboli, leads to a spectrum of clinical syndromes marked by impaired pulmonary circulation and right heart dysfunction, contributing to sudden and unpredictable fatalities. Nevertheless, the diagnosis of pulmonary embolism remains challenging due to non-specific clinical presentations, constrained diagnostic capabilities, delayed diagnoses, insufficient physician knowledge, and suboptimal diagnostic techniques. Consequently, we introduce the innovative LSCU-Net architecture within the CPSS framework, designed to develop an automated segmentation and intelligent assessment system for pulmonary embolism, facilitating its automated and intelligent detection. The experimental findings demonstrate that the model accurately segments pulmonary embolism, evidenced by a Jaccard index of 0.6958, a Dice coefficient of 0.8193, a Mean Pixel Accuracy (mPA) of 0.8519, and an accuracy of 0.9993. Empirical studies reveal that our proposed model substantially surpasses existing models in performance. Consequently, this model can aid physicians in the diagnosis of pulmonary embolism during clinical practice. The established pulmonary embolism automatic segmentation and assessment system also showcases the application successes of CPSS in intelligent remote healthcare. The system’s development and deployment not only streamline physicians’ diagnostic processes but also elevate public health standards and advance CPSS research within the medical domain.

A Quick Capture Evaluation System for the Automatic Assessment of Work-Related Musculoskeletal Disorders for Sanitation Workers

Musculoskeletal disorders not only impact workers’ health but also result in significant economic losses to society. Sanitation workers often have to lift waste bags from containers, leading to shoulder joint flexion of 90° or more, exposing them to hazardous environments for extended periods. This study combines deep learning and image recognition to create a Quick Capture Evaluation System (QCES). By comparing body angles captured in the sanitation workers’ work environment with those from OptiTrack motion capture, the system showed an average Root Mean Square Error of 5.64 for 18 different postures, and an average Spearman’s rho of 0.87, indicating its precision. Compared with scores assessed by three experts, the system demonstrated an average Cohen’s kappa of 0.766, proving its reliability. Practical assessments of sanitation workers revealed that tilting the waste containers could significantly improve their posture and reduce the risk of Work-Related Musculoskeletal Disorders. It proves that the QCES system can accurately and rapidly assess the on-site posture of a particular occupation.

A scoping assessment of dental services at designated head and neck cancer centres in Ontario, Canada

BackgroundDentists serve a crucial role in managing treatment complications for patients with head and neck cancer, including post-radiation caries and oral infection. To date, dental services for head and neck cancer patients in Ontario, Canada have not been well characterized and considerable disparities in allocation, availability, and funding are thought to exist. The current study aims to describe and assess the provision of dental services for head and neck cancer patients in Ontario.MethodsA mixed methods scoping assessment was conducted. A purposive sample of dentist-in-chiefs at each of Ontario’s 9 designated head and neck cancer centres (tertiary centres which meet provincially-set quality and safety standards) was invited to participate. Participants completed a 36-item online survey and 60-minute semi-structured interview which explored perceptions of dental services for head and neck cancer patients at their respective centres, including strengths, gaps, and inequities. If a centre did not have a dentist-in-chief, an alternative stakeholder who was knowledgeable on that centre’s dental services participated instead. Thematic analysis of the interview data was completed using a mixed deductive-inductive approach.ResultsSurvey questionnaires were completed at 7 of 9 designated centres. A publicly funded dental clinic was present at 5 centres, but only 2 centres provided automatic dental assessment for all patients. Survey data from 2 centres were not captured due to these centres’ lack of active dental services. Qualitative interviews were conducted at 9 of 9 designated centres and elicited 3 themes: (1) lack of financial resources; (2) heterogeneity in dentistry care provision; and (3) gaps in the continuity of care. Participants noted concerning under-resourcing and limitations/restrictions in funding for dental services across Ontario, resulting in worse health outcomes for vulnerable patients. Extensive advocacy efforts by champions of dental services who have sought to mitigate current disparities in dentistry care were also described.ConclusionsInequities exist in the provision of dental services for head and neck cancer patients in Ontario. Data from the current study will broaden the foundation for evidence-based decision-making on the allocation and funding of dental services by government health care agencies.

Automatic Assessment of Ultrasound Curvature Angle for Scoliosis Detection Using 3-D Ultrasound Volume Projection Imaging

Scoliosis is a spinal deformation in which the spine takes a lateral curvature, generating an angle in the coronal plane. The conventional method for detecting scoliosis is measurement of the Cobb angle in spine images obtained by anterior X-ray scanning. Ultrasound imaging of the spine is found to be less ionising than traditional radiographic modalities. For posterior ultrasound scanning, alternate indices of the spinous process angle (SPA) and ultrasound curve angle (UCA) were developed and have proven comparable to those of the traditional Cobb angle. In SPA, the measurements are made using the spinous processes as an anatomical reference, leading to an underestimation of the traditionally used Cobb angles. Alternatively, in UCA, more lateral features of the spine are employed for measurement of the main thoracic and thoracolumbar angles; however, clear identification of bony features is required. The current practice of UCA angle measurement is manual. This research attempts to automate the process so that the errors related to human intervention can be avoided and the scalability of ultrasound scoliosis diagnosis can be improved. The key objective is to develop an automatic scoliosis diagnosis system using 3-D ultrasound imaging. The novel diagnosis system is a three-step process: (i) finding the ultrasound spine image with the most visible lateral features using the convolutional RankNet algorithm; (ii) segmenting the bony features from the noisy ultrasound images using joint spine segmentation and noise removal; and (iii) calculating the UCA automatically using a newly developed centroid pairing and inscribed rectangle slope method. The proposed method was evaluated on 109 patients with scoliosis of different severity. The results obtained had a good correlation with manually measured UCAs (R2=0.9784 for the main thoracic angle andR2=0.9671 for the thoracolumbar angle) and a clinically acceptable mean absolute difference of the main thoracic angle (2.82 ± 2.67°) and thoracolumbar angle (3.34 ± 2.83°). The proposed method establishes a very promising approach for enabling the applications of economic 3-D ultrasound volume projection imaging for mass screening of scoliosis.