Computer-assisted Tools Research Articles

Selected annotated instance segmentation sub-volumes from a large scale CT data-set of a historic aircraft

The Me 163 was a Second World War fighter airplane and is currently displayed in the Deutsches Museum in Munich, Germany. A complete computed tomography (CT) scan was obtained using a large scale industrial CT scanner to gain insights into its history, design, and state of preservation. The CT data enables visual examination of the airplane’s structural details across multiple scales, from the entire fuselage to individual sprockets and rivets. However, further processing requires instance segmentation of the CT data-set. Currently, there are no adequate computer-assisted tools for automated or semi-automated segmentation of such large scale CT airplane data. As a first step, an interactive data annotation process has been established. So far, seven 512 × 512 × 512 voxel sub-volumes of the Me 163 airplane have been annotated, which can potentially be used for various applications in digital heritage, non-destructive testing, or machine learning. This work describes the data acquisition process, outlines the interactive segmentation and post-processing, and discusses the challenges associated with interpreting and handling the annotated data.

A computer-assisted tool for automatically measuring non-native Japanese oral proficiency

This study presents the development of an automated tool for assessing the speech of non-native Japanese speakers. The tool integrated an accent-trained automatic speech recording system based on deep neural networks, audio-text transcription, and natural language processing technologies to perform annotation, analysis, calculation, and scoring. A dataset consisting of audio recordings from 1000 Japanese learners across 16 countries was used for machine training. Validity and reliability tests were parallelly developed to evaluate: (a) the measures’ ability to distinguish test takers with different oral proficiency levels; (b) the agreement between the annotations and scoring performed by the tool and human raters. The results, obtained through precision, recall, F-Score, quadratically-weighted kappa, proportional reduction of mean squared error, Pearson correlation, and multi-faceted Rasch measurement, demonstrated a relatively high reliability of the automated tool. The tool provides users with both a holistic score and individual scores for each criterion, allowing them to gain insights into their overall performance as well as detailed evaluations for each aspect of their proficiency. Consequently, it can be utilized for employment, education, and self-learning purposes.

GI genius endoscopy module: a clinical profile

ABSTRACT Introduction The identification of early-stage colorectal cancers (CRC) and the resection of pre-cancerous neoplastic lesions through colonoscopy allows to decrease both CRC incidence and mortality. However, colonoscopy miss rates up to 26% for adenomas and 9% for advanced adenomas have been reported. In recent years, artificial intelligence (AI) systems have been emerging as easy-to-use tools, potentially lowering the risk of missing lesions. Areas covered This review paper focuses on GI Genius device (Medtronic Co. Minneapolis, MN, U.S.A.) a computer-assisted tool designed to assist endoscopists during standard white-light colonoscopies in detecting mucosal lesions. Expert opinion Randomized controlled trials (RCTs) suggest that GI Genius is a safe and effective tool for improving adenoma detection, especially in CRC screening and surveillance colonoscopies. However, its impact seems to be less significant among experienced endoscopists and in real-world clinical scenarios compared to the controlled conditions of RCTs. Furthermore, it appears that GI Genius mainly enhances the detection of non-advanced, small polyps, but does not significantly impact the identification of advanced and difficult-to-detect adenoma. When using GI Genius, no complications were documented. Only a small number of studies reported an increased in withdrawal time or the removal of non-neoplastic lesions.

Opening the black box: explainable deep-learning classification of wood microscopic image of endangered tree species

BackgroundTraditional method of wood species identification involves the use of hand lens by wood anatomists, which is a time-consuming method that usually identifies only at the genetic level. Computer vision method can achieve "species" level identification but cannot provide an explanation on what features are used for the identification. Thus, in this study, we used computer vision methods coupled with deep learning to reveal interspecific differences between closely related tree species.ResultA total of 850 images were collected from the cross and tangential sections of 15 wood species. These images were used to construct a deep-learning model to discriminate wood species, and a classification accuracy of 99.3% was obtained. The key features between species in machine identification were targeted by feature visualization methods, mainly the axial parenchyma arrangements and vessel in cross section and the wood ray in tangential section. Moreover, the degree of importance of the vessels of different tree species in the cross-section images was determined by the manual feature labeling method. The results showed that vessels play an important role in the identification of Dalbergia, Pterocarpus, Swartzia, Carapa, and Cedrela, but exhibited limited resolutions on discriminating Swietenia species.ConclusionThe research results provide a computer-assisted tool for identifying endangered tree species in laboratory scenarios, which can be used to combat illegal logging and related trade and contribute to the implementation of CITES convention and the conservation of global biodiversity.

The impact of digital imaging and computer-assisted surgery in oral surgery: A narrative review

Digital imaging and computer-assisted surgery (CASS) have ushered in a new era in oral surgery, offering manifold advantages in terms of precision, efficiency, and patient well-being. This narrative review delves into the profound impact of these technologies across various facets of oral surgery, encompassing diagnostics, treatment planning, intraoperative guidance, and postoperative evaluation. We examine the evolution of digital imaging modalities, notably cone beam computed tomography (CBCT), and their seamless integration with computer-assisted tools for meticulous surgical interventions. Furthermore, we explore the benefits conferred by virtual surgical planning (VSP) and 3D printing in refining preoperative strategizing and surgical execution. Additionally, we underscore the pivotal role played by navigation systems and robotic assistance in heightening surgical accuracy and mitigating complications. Notwithstanding the remarkable advantages, we also address the inherent challenges and limitations associated with digital imaging and CASS, including cost considerations, learning curves, and potential errors. Despite these hurdles, the widespread adoption of digital technologies in oral surgery has substantially revolutionized clinical practices, culminating in more predictable outcomes and heightened patient satisfaction. Lastly, we contemplate future avenues for research aimed at optimizing the integration of digital imaging and computer-assisted techniques in oral surgical procedures.

Unveiling ideological shifts in news trans‑editing

Abstract This article expands on previous research on news trans-editing by examining the relationship between ideology and language. Using a conceptual framework that combines critical discourse analysis with narrative analysis, the study analyses a corpus of trans-edited news articles on the 2014 Hong Kong protests. These articles were collected from Reference News, along with their source texts from various English-language international media. Narrative analysis was conducted using NVivo computer-assisted tools, including cluster analysis, word frequency and matrix coding. The findings reveal significant shifts in the narratives between the original and trans-edited versions, indicating that the trans-editors recontextualised the news narratives by determining the narrators, retroversions and frequency of the narrative texts. These results suggest that news trans-editing is not a neutral process, but is rather influenced by the ideological stance of the news outlet.

Automated Assessment of the DWI-FLAIR Mismatch in Patients with Acute Ischemic Stroke: Added Value to Routine Clinical Practice.

The DWI-FLAIR mismatch is used to determine thrombolytic eligibility in patients with acute ischemic stroke when the time since stroke onset is unknown. Commercial software packages have been developed for automated DWI-FLAIR classification. We aimed to use e-Stroke software for automated classification of the DWI-FLAIR mismatch in a cohort of patients with acute ischemic stroke and in a comparative analysis with 2 expert neuroradiologists. In this retrospective study, patients with acute ischemic stroke who had MR imaging and known time since stroke onset were included. The DWI-FLAIR mismatch was evaluated by 2 neuroradiologists blinded to the time since stroke onset and automatically by the e-Stroke software. After 4 weeks, the neuroradiologists re-evaluated the MR images, this time equipped with automated predicted e-Stroke results as a computer-assisted tool. Diagnostic performances of e-Stroke software and the neuroradiologists were evaluated for prediction of DWI-FLAIR mismatch status. A total of 157 patients met the inclusion criteria. A total of 82 patients (52%) had a time since stroke onset of ≤4.5 hours. By means of consensus reads, 81 patients (51.5%) had a DWI-FLAIR mismatch. The diagnostic accuracy (area under the curve/sensitivity/specificity) of e-Stroke software for the determination of the DWI-FLAIR mismatch was 0.72/90.0/53.9. The diagnostic accuracy (area under the curve/sensitivity/specificity) for neuroradiologists 1 and 2 was 0.76/69.1/84.2 and 0.82/91.4/73.7, respectively; both significantly (P < .05) improved to 0.83/79.0/86.8 and 0.89/92.6/85.5, respectively, following the use of e-Stroke predictions as a computer-assisted tool. The interrater agreement (κ) for determination of DWI-FLAIR status was improved from 0.49 to 0.57 following the use of the computer-assisted tool. This automated quantitative approach for DWI-FLAIR mismatch provides results comparable with those of human experts and can improve the diagnostic accuracies of expert neuroradiologists in the determination of DWI-FLAIR status.

Development of deep learning framework for anatomical landmark detection and guided dissection line during laparoscopic cholecystectomy

BackgroundBile duct injuries during laparoscopic cholecystectomy can arise from misinterpretation of biliary anatomy, leading to dissection in improper areas. The integration of a deep learning framework into laparoscopic procedures offers the potential for real-time anatomical landmark recognition, ensuring accurate dissection. The objective of this study is to develop a deep learning framework that can precisely identify anatomical landmarks, including Rouviere's sulcus and the liver base of segment IV, and provide a guided dissection line during laparoscopic cholecystectomy. MethodsWe retrospectively collected 40 laparoscopic cholecystectomy videos and extracted 80 images form each video to establish the dataset. Three surgeons annotated the bounding boxes of anatomical landmarks on a total of 3200 images. The YOLOv7 model was trained to detect Rouviere's sulcus and the liver base of segment IV as anatomical landmarks. Additionally, the guided dissection line was generated between these two landmarks by the proposed algorithm. To evaluate the performance of the detection model, mean average precision (mAP), precision, and recall were calculated. Furthermore, the accuracy of the guided dissection line was evaluated by three surgeons. The performance of the detection model was compared to the scaled-YOLOv4 and YOLOv5 models. Finally, the proposed framework was deployed in the operating room for real-time detection and visualization. ResultsThe overall performance of the YOLOv7 model on validation set and testing set were 98.1 % and 91.3 %, respectively. Surgeons accepted the visualization of guide dissection line with a rate of 95.71 %. In the operating room, the well-trained model accurately identified the anatomical landmarks and generated the guided dissection line in real-time. ConclusionsThe proposed framework effectively identifies anatomical landmarks and generates a guided dissection line in real-time during laparoscopic cholecystectomy. This research underscores the potential of using deep learning models as computer-assisted tools in surgery, providing an assistant tool to accommodate with surgeons.

A novel computer-assisted tool for 3D imaging of programmed death-ligand 1 expression in immunofluorescence-stained and optically cleared breast cancer specimens

BackgroundProgrammed death-1 (PD-1) and programmed death-ligand 1 (PD-L1) are the two most common immune checkpoints targeted in triple-negative breast cancer (BC). Refining patient selection for immunotherapy is non-trivial and finding an appropriate digital pathology framework for spatial analysis of theranostic biomarkers for PD-1/PD-L1 inhibitors remains an unmet clinical need.MethodsWe describe a novel computer-assisted tool for three-dimensional (3D) imaging of PD-L1 expression in immunofluorescence-stained and optically cleared BC specimens (n = 20). The proposed 3D framework appeared to be feasible and showed a high overall agreement with traditional, clinical-grade two-dimensional (2D) staining techniques. Additionally, the results obtained for automated immune cell detection and analysis of PD-L1 expression were satisfactory.ResultsThe spatial distribution of PD-L1 expression was heterogeneous across various BC tissue layers in the 3D space. Notably, there were six cases (30%) wherein PD-L1 expression levels along different layers crossed the 1% threshold for admitting patients to PD-1/PD-L1 inhibitors. The average PD-L1 expression in 3D space was different from that of traditional immunohistochemistry (IHC) in eight cases (40%). Pending further standardization and optimization, we expect that our technology will become a valuable addition for assessing PD-L1 expression in patients with BC.ConclusionVia a single round of immunofluorescence imaging, our approach may provide a considerable improvement in patient stratification for cancer immunotherapy as compared with standard techniques.

A Deep Learning-Based Mobile Application for Monkeypox Detection

The recent outbreak of monkeypox has raised significant concerns in the field of public health, primarily because it has quickly spread to over 40 countries outside of Africa. Detecting monkeypox in its early stages can be quite challenging because its symptoms can resemble those of chickenpox and measles. However, there is hope that potential use of computer-assisted tools may be used to identify monkeypox cases rapidly and efficiently. A promising approach involves the use of technology, specifically deep learning methods, which have proven effective in automatically detecting skin lesions when sufficient training examples are available. To improve monkeypox diagnosis through mobile applications, we have employed a particular neural network called MobileNetV2, which falls under the category of Fully Connected Convolutional Neural Networks (FCCNN). It enables us to identify suspected monkeypox cases accurately compared to classical machine learning approaches. The proposed approach was evaluated using the recall, precision, F score, and accuracy. The experimental results show that our architecture achieves an accuracy of 0.99%, a Recall of 1.0%, an F-score of 0.98%, and a Precision of 0.95%. We believe that such experimental evaluation will contribute to the medical domain and many use cases.

Digital Humanities and Game Ethics: Investigating the Impact of Representation in Gaming

“Digital humanities” is about the usage of computer-assisted tools to preserve knowledge and disseminate them through technological resources. An extension of these techniques can be seen in today’s hyper realistic games with high-concept storytelling. These games immerse the players in a world that enables them to experience a different reality and often ask them to make choices that have real-life social impact. They have empowered artists to create worlds that could become a testing ground for philosophical theories. This paper concentrates on the impact of gaming on real-world ethical and political questions. This paper primarily focuses on the impact of The Last of Us Part II- an action-adventure game developed by Naughty dog- on the discussion of the inclusion of LGBTQ+ characters on already existing IP (Intellectual Property). The response that the game received among the players can be used to understand the general perspective of these elite communities on LGBTQ+ issues. This paper also includes the positive impact of such inclusion and argues that the diversification of characters in already existing IP substantially contributes to inclusivity and equality.

DIAGNOSE EYES DISEASES USING VARIOUS FEATURES EXTRACTION APPROACHES AND MACHINE LEARNING ALGORITHMS

Ophthalmic diseases like glaucoma, diabetic retinopathy, and cataracts are the main cause of visual impairment worldwide. With the use of the fundus images, it could be difficult for a clinician to detect eye diseases early enough. By other hand, the diagnoses of eye disease are prone to errors, challenging and labor-intensive. Thus, for the purpose of identifying various eye problems with the use of the fundus images, a system of automated ocular disease detection with computer-assisted tools is needed. Due to machine learning (ML) algorithms' advanced skills for image classification, this kind of system is feasible. An essential area of artificial intelligence)AI (is machine learning. Ophthalmologists will soon be able to deliver accurate diagnoses and support individualized healthcare thanks to the general capacity of machine learning to automatically identify, find, and grade pathological aspects in ocular disorders. This work presents a ML-based method for targeted ocular detection. The Ocular Disease Intelligent Recognition (ODIR) dataset, which includes 5,000 images of 8 different fundus types, was classified using machine learning methods. Various ocular diseases are represented by these classes. In this study, the dataset was divided into 70% training data and 30% test data, and preprocessing operations were performed on all images starting from color image conversion to grayscale, histogram equalization, BLUR, and resizing operation. The feature extraction represents the next phase in this study ,two algorithms are applied to perform the extraction of features which includes: SIFT(Scale-invariant feature transform) and GLCM(Gray Level Co-occurrence Matrix), ODIR dataset is then subjected to the classification techniques Naïve Bayes, Decision Tree, Random Forest, and K-nearest Neighbor. This study achieved the highest accuracy for binary classification (abnormal and normal) which is 75% (NB algorithm), 62% (RF algorithm), 53% (KNN algorithm), 51% (DT algorithm) and achieved the highest accuracy for multiclass classification (types of eye diseases) which is 88% (RF algorithm), 61% (KNN algorithm) 42% (NB algorithm), and 39% (DT algorithm).

Optimized Swarm Enabled Deep Learning Technique for Bone Tumor Detection using Histopathological Image

Cancer subjugates a community that lacks proper care. It remains apparent that research studies enhance novel benchmarks in developing a computer-assisted tool for prognosis in radiology yet an indication of illness detection should be recognized by the pathologist. In bone cancer (BC), Identification of malignancy out of the BC’s histopathological image (HI) remains difficult because of the intricate structure of the bone tissue (BTe) specimen. This study proffers a new approach to diagnosing BC by feature extraction alongside classification employing deep learning frameworks. In this, the input is processed and segmented by Tsallis Entropy for noise elimination, image rescaling, and smoothening. The features are excerpted employing Efficient Net-based Convolutional Neural Network (CNN) Feature Extraction. ROI extraction will be employed to enhance the precise detection of atypical portions surrounding the affected area. Next, for classifying the accurate spotting and for grading the BTe as typical and a typical employing augmented XGBoost alongside Whale optimization (WOA). HIs gathering out of prevailing scales patients is acquired alongside texture characteristics of such images remaining employed for training and testing the Neural Network (NN). These classification outcomes exhibit that NN possesses a hit ratio of 99.48 percent while this occurs in BT classification.

Overcoming academic vocabulary errors through online corpus consultation: the case of Saudi English majors

In the past two decades, corpora have been proposed as valuable computer-assisted tools for teaching and learning academic writing in English at the university level. This article reports on an empirical study that sought to examine the effectiveness of direct corpus consultation in overcoming vocabulary errors in academic writing. This study is part of a larger research project that also investigated students’ attitudes towards direct corpus consultation activities in terms of overcoming vocabulary errors and promoting their learning autonomy as well as the difficulties they encountered. A quantitative quasi-experimental design was used in the present study, which involved 32 Saudi female undergraduates majoring in English for nine weeks of the fourteen-week writing course. Data collection instruments included a pre-post vocabulary test and pre-post writing tests. The results of quantitative and content analyses suggested that direct corpus consultation decreased vocabulary errors and improved students’ academic writing quality. The corpus consultation tasks significantly affected students’ ability to overcome the four targeted types of vocabulary errors (collocation, connotation, word form, and spelling). Furthermore, the results showed that the effectiveness of corpus consultation tasks in improving students’ vocabulary knowledge and usage varied depending on the type of vocabulary error. On the vocabulary test, collocation errors had the smallest decrease and medium effect size, whereas, on the writing post-test, connotations were the least developed type. The study concluded with some pedagogical implications for using direct corpus consultation to develop students’ vocabulary knowledge and usage, as well as recommendations for future research.

A Space-Refine Paradigm for Automatic Carotid Artery Centerline Extraction in Magnetic Resonance Imaging.

Vessel centerline extraction is essential for carotid stenosis assessment and atherosclerotic plaque identification in clinical diagnosis. Simultaneously, it provides a region of interest identification and boundary initialization for computer-assisted diagnosis tools. In magnetic resonance imaging (MRI) cross-sectional images, the lumen shape and vascular topology result in a challenging task to extract the centerline accurately. To this end, we propose a space-refine framework, which exploits the positional continuity of the carotid artery from frame to frame to extract the carotid artery centerline. The proposed framework consists of a detector and a refinement module. Specifically, the detector roughly extracts the carotid lumen region from the original image. Then, we introduce a refinement module that uses the cascade of regressors from a detector to perform sequence realignment of lumen bounding boxes for each subject. It improves the lumen localization results and further enhances the centerline extraction accuracy. Verified by large carotid artery data, the proposed framework achieves state-of-the-art performance compared to conventional vessel centerline extraction methods or standard convolutional neural network approaches.Clinical relevance- Our proposed framework can be used as an important aid for physicians to quantitatively analyze the carotid artery in clinical practice. It is also used as a new paradigm for extracting the centerline of carotid vessels in computer-assisted tools.

Spino-pelvic radiological parameters: Comparison of measurements obtained by radiologists using the traditional method versus spine surgeons using a semi-automated software (Surgimap).

Spinopelvic balance measurement is a key point to get an appropriate diagnosis and treatment in a group of spine pathologies; thus, it seems necessary the evaluation of different methods for obtaining the most reliable values. For that reason, different automatic and semi-automatic computer-assisted tools have been developed, and one example of them is Surgimap. To demonstrate that the sagittal balance measurements with Surgimap are equal and more time-efficient than with Agfa-Enterprise. Retrospective-prospective study. Biased comparative analysis of radiographic measurements performed on two different occasions (96h interval), between two spine surgeons using Surgimap and two radiologists using the traditional Cobb method (TCM) with the Agfa-Enterprise program in 36 full spine lateral X-ray, determining inter- and intra-observer reliability and the mean time required to obtain the measurements. Measurements with both methods demonstrated an excellent intra-observer correlation (Surgimap: PCC 0.95 [0.85-0.99]; TCM: PCC 0.90 [0.81-0.99]). Inter-observer correlation also demonstrated an excellent relationship (PCC >0.95). Thoracic kyphosis (TK) demonstrated the lowest levels of inter-observer correlation (PCC: 0.75). The average time in seconds with TCM was 154.6, while with the Surgimap it was 41.8s. Surgimap proved to be equally reliable and 3.5 times faster. Therefore, in consistency with the available literature, our results would allow us to promote the use of Surgimap as a clinical diagnostic tool considering precision and efficiency.

DIGITAL DENTISTRY – A REVIEW

Digital Dentistry is a new technology in the dental field that will improve patients' treatment options in the future. It makes digital dental equipment for cosmetic use and implants in dentistry. The practise and potential future of a dentist will be determined by whether or not they accept modern technology. The phrase “digital dentistry” is a wide name that refers to any dental technology including the use of hardware and software that is computer-based technology. The objective is to enable dental professionals to deliver care utilizing computer-assisted tools. This article's reviews the investigation ideas of digital dentistry, its merits and demerits, and to offer claims and observations about particular aspects of digital dentistry that are supported by research. The knowledge is of not only the dentist but also the whole dental staffs are crucial to the success of introducing new technology in the dentistry

Multi-Parametric Radiomic Model to Predict 1p/19q Co-Deletion in Patients with IDH-1 Mutant Glioma: Added Value to the T2-FLAIR Mismatch Sign.

The T2-FLAIR mismatch sign has shown promise in determining IDH mutant 1p/19q non-co-deleted gliomas with a high specificity and modest sensitivity. To develop a multi-parametric radiomic model using MRI to predict 1p/19q co-deletion status in patients with newly diagnosed IDH1 mutant glioma and to perform a comparative analysis to T2-FLAIR mismatch sign+. In this retrospective study, patients with diagnosis of IDH1 mutant gliomas with known 1p/19q status who had preoperative MRI were included. T2-FLAIR mismatch was evaluated independently by two board-certified neuroradiologists. Texture features were extracted from glioma segmentation of FLAIR images. eXtremeGradient Boosting (XGboost) classifiers were used for model development. Leave-one-out-cross-validation (LOOCV) and external validation performances were reported for both the training and external validation sets. A total of 103 patients were included for model development and 18 patients for external testing validation. The diagnostic performance (sensitivity/specificity/accuracy) in the determination of the 1p/19q co-deletion status was 59%/83%/67% (training) and 62.5%/70.0%/66.3% (testing) for the T2-FLAIR mismatch sign. This was significantly improved (p = 0.04) using the radiomics model to 77.9%/82.8%/80.3% (training) and 87.5%/89.9%/88.8% (testing), respectively. The addition of radiomics as a computer-assisted tool resulted in significant (p = 0.02) improvement in the performance of the neuroradiologist with 13 additional corrected cases in comparison to just using the T2-FLAIR mismatch sign. The proposed radiomic model provides much needed sensitivity to the highly specific T2-FLAIR mismatch sign in the determination of the 1p/19q non-co-deletion status and improves the overall diagnostic performance of neuroradiologists when used as an assistive tool.

Virtual Reality Assessment for Obsessive Compulsive Disorder: A Review

The use of advance computer-based technology is becoming necessary to address the growing complexity of human problems and enhance effective communication. The recent pandemic COVID-19 not only induces many morbidities and mortalities but also intensifies mental health problem worldwide. Due to the increasing benefits of virtual reality (VR) in addressing medical condition, it is believed that VR can be used as a diagnostic tool to assess numerous medical conditions and psychiatric disorders. To date, there is still scarce evidence of VR as a diagnostic tool to assess obsessive compulsive disorder (OCD). In this study, we had conducted a systematic review to investigate the use of VR as a diagnostic tool for OCD and assess its benefits and weaknesses in comparison to computer-assisted tools. Comprehensive searches of electronic databases including PubMed and Google Scholar were undertaken to discover peer review evidence of computer-based simulation tasks in detecting OCD symptoms. Twelve out of 9325 papers were screened and reviewed. Five articles reported on computerised tools and seven articles described VR tools. In comparison to computer-based tasks, VR is a promising assessment tool due to specific virtual environments and high resolutions which are able to induce anxiety symptoms. Despite numerous shortcomings, assessment can be utilised in computerised form to detect and generate a variety of psychiatry diagnoses among the general population. Although computerised assessment task and VR show promising results, the finding are uneven due to study design differences, wide variability content task use, small sample size, several methodological issue with the computerised tasks and lack of appropriate control groups. In conclusion, the choice to use computerisation or VR for OCD assessment will depend on aim, content, technical equipment and budget. More in-depth studies of these issues are required.

Factors that Play a Role in Improving the Efficiency of Continuous Audits using Computer Assisted Audit Techniques in enterprises including healthcare units

Internal auditing is a key function of any business enterprise, including healthcare units, being responsible for highlighting and bringing to notice of the upper management a fair evaluation of the organization’s internal control environment. With stakeholder satisfaction, compliance requirements, and value delivery increasingly taking centre stage, it functionally requires many organizations to adopt and implement a continuous auditing solution. Continuous auditing solutions often call for the adoption of various computer-assisted tools and techniques to help get the job done. The usage of these tools and techniques is subject to many different factors. This paper explores some of the factors that play a role in improving the efficiency of continuous audits using such tools and techniques. Auditors widely recognize Computer-assisted auditing techniques (CAATs). However, CAATs are not being used as widely as expected. The technology–organization–environment framework is used to identify and assess the organisational elements that influence CAAT utilisation after adoption. The purpose of this study is to see if using CAATs helps the auditing process. The usage of CAATs by audit firms and audit performance are both moderated by top management commitment. The data for this study was collected from Jordanian audit companies’ auditors. According to the conclusions of the investigation, CAAT adoption is determined by the cost–benefit of technology, company size, preparedness, and competitive pressure. The technical compatibility and complexity of client accounting information systems, on the other hand, appear to have little impact on CAAT utilisation. Top management has a direct impact on audit performance and is a key influencer in how CAATs are used by auditors. However, there is no correlation between audit firm usage of CAATs and audit performance (top management audit firm’s use of CAATs). Furthermore, the adoption of CAATs helps the whole audit process of audit companies.