Manual Input Research Articles

Web-Based Antam Gold Savings and Pawn Calculation Application

The demand for a user-friendly website that enables the withdrawal of funds from gold savings accounts at Pegadaian has experienced substantial growth amidst the ongoing epidemic. Due to its inherent value and stability, gold assumes particular significance in times of economic uncertainty, allowing individuals to liquidate their savings when necessary. The old approach entailed the manual input of customer information through Excel, which consequently required face-to-face interactions for payment processing. Nevertheless, a significant shift has transpired through the integration of an internet-based platform, facilitating the provision of personal data by clients and thereby establishing virtual profiles to conduct online transactions. Implementing this transformation has resulted in a more streamlined process, enhancing efficiency and improving accessibility for clients.
 The research methodology employed for this project is descriptive, encompassing direct field observations. Furthermore, the system employs the Object-Oriented Analysis and Design (OOAD) methodology to analyze and design the system. The main objective is to develop a system that streamlines the procedures of gold pawning and online savings for consumers. The data input process utilizes the PHP programming language, whereby the data is safely stored within a MySQL database. The integration of these elements guarantees a system that is both secure and efficient, effectively addressing the requirements of both customers and administrators.

How mothers help children learn to use everyday objects.

Children must learn specific motor actions to use everyday objects as their designers intended. However, designed actions are not obvious to children and often are difficult to implement. Children must know what actions to do and how to execute them. Previous work identified a protracted developmental progression in learning designed actions-from nondesigned exploratory actions, to display of the designed action, to successful implementation. Presumably, caregivers can help children to overcome the challenges in discovering and implementing designed actions. Mothers of 12-, 18- to 24-, and 30- to 36-month-olds (N=74) were asked to teach their children to open containers with twist-off or pull-off lids. Mothers' manual and verbal input aligned with the developmental progression and with children's actions in the moment, pointing to the role of attuned social information in helping children learn to use objects for activities of daily living. However, mothers sometimes "overhelped" by implementing designed actions for children instead of getting children to do it themselves, highlighting the challenges of teaching novices difficult motor actions.

A parallel ensemble optimization and transfer learning based intelligent fault diagnosis framework for bearings

Transfer learning (TL) is an important method to accurately identify the bearing health status in cross-domain and ensure the safe operation of machinery. With the advancement in research, it will become a trend to choose different neural networks or optimization functions to improve and re-model fault diagnosis methods. However, the variants of these fault diagnostic methods are less capable of generalizing input dimensions and do not significantly increase demand for machinery expertise. The idea of ensemble learning solves the problem of low generalization. In this research, a parallel ensemble optimization loss function and multi-source TL based model are proposed to solve the problem of unknown distribution difference between source domain and target domain, thus improving the generalization of optimization objectives. Firstly, based on the signal demodulation method, an adaptive input module is constructed to automatically select the input length from the original vibration signal. Secondly, a TL network with low-dimensional features reuse is constructed to achieve weight and bias sharing. Thirdly, a parallel ensemble optimization loss function is developed to align the data whose distribution is unknown between source and target domains. Finally, two cases with multi-source, unsupervised, and cross-domain TL are used to verify the performance of the proposed method. The average accuracy in case 1 and case 2 is 99.81 % and 99.17 % respectively. It is proved that the proposed method can not only get rid of the limitation of manual input length setting, but also overcome the limitation of optimization function, which is more effective than the existing intelligent fault diagnosis models.

Automatic anatomical foot and ankle coordinate toolbox.

Accurate analysis of bone position and orientation in foot and ankle studies relies on anatomical coordinate systems (ACS). Reliable ACSs are necessary for many biomechanical and clinical studies, especially those including weightbearing computed tomography and biplane fluoroscopy. Existing ACS approaches suffer from limitations such as manual input, oversimplifications, or non-physiological methods. To address these shortcomings, we introduce the Automatic Anatomical Foot and Ankle Coordinate Toolbox (AAFACT), a MATLAB-based toolbox that automates the calculation of ACSs for the major fourteen foot and ankle bones. In this manuscript, we present the development and evaluation of AAFACT, aiming to provide a standardized coordinate system toolbox for foot and ankle studies. The AAFACT was evaluated using a dataset of fifty-six models from seven pathological groups: asymptomatic, osteoarthritis, pilon fracture, progressive collapsing foot deformity, clubfoot, Charcot Marie Tooth, and cavovarus. Three analyses were conducted to assess the reliability of AAFACT. Firstly, ACSs were compared between automatically and manually segmented bone models to assess consistency. Secondly, ACSs were compared between individual bones and group mean bones to assess within-population precision. Lastly, ACSs were compared between the overall mean bone and group mean bones to assess the overall accuracy of anatomical representation. Statistical analyses, including statistical shape modeling, were performed to evaluate the reliability, accuracy, and precision of AAFACT. The comparison between automatically and manually segmented bone models showed consistency between the calculated ACSs. Additionally, the comparison between individual bones and group mean bones, as well as the comparison between the overall mean bone and group mean bones, revealed accurate and precise ACSs calculations. The AAFACT offers a practical and reliable solution for foot and ankle studies in clinical and engineering settings. It accommodates various foot and ankle pathologies while accounting for bone morphology and orientation. The automated calculation of ACSs eliminates the limitations associated with manual input and non-physiological methods. The evaluation results demonstrate the robustness and consistency of AAFACT, making it a valuable tool for researchers and clinicians. The standardized coordinate system provided by AAFACT enhances comparability between studies and facilitates advancements in foot and ankle research.

P092 Automation in Equipment Asset Management for Laboratory Efficiencies

Abstract Introduction Asset management of laboratory equipment is essential to patient safety. Traceability enables any patient affected by equipment that has been recalled, or subsequently been found faulty, to be retested. Sleep equipment in home, ward or laboratory needs to be clearly documented but this paperwork is tedious and manual. Methods A portable barcode scanner with software was added to the laboratory asset management and the time efficiencies were evaluated. Results The time reduction of using a barcode scanner to manual data entry was found to decrease time approximately fourfold, from 8 mins (std 1.5mins) to 2.2 mins (std 0.5mins). This is for each sleep laboratory testing bed and can equate to 35 minutes in a six-bed sleep laboratory. Accuracy of data is assured with a barcode scanner, compared to manual input. Discussion Using automation to replace manual tasks in a sleep laboratory saves time and ensures data accuracy. Further advances in automation for tagging medical devices would improve this in areas with high use of home or ward devices.

A novel automated peak frequency annotation algorithm for identifying deceleration zones and ventricular tachycardia ablation sites

BackgroundCurrent annotation of local fractionated signals during ventricular electroanatomic mapping (EAM) requires manual input subject to variability and error. ObjectivesThe purpose of this study was to evaluate a novel peak frequency (PF) annotation software for its ability to automatically detect late potentials (LPs) and local abnormal ventricular activity (LAVA), determine an optimal range for display, and assess its impact on isochronal late activation mapping (ILAM). MethodsEAM data from 25 patients who underwent ventricular tachycardia (VT) ablation were retrospectively analyzed. Samplings of electrogram PFs from areas of normal bipolar voltage, areas of low voltage, and areas of low voltage with fractioned signals were performed. An optimal range of frequency display was identified from these patients and applied to a validation cohort of 10 prospective patients to assess high PF within scar as a predictor of VT ablation target sites, in particular deceleration zones (DZs) identified by ILAM, LP, and LAVA. ResultsVoltage and PF ranges of normal endocardial tissue varied widely. Using 220 Hz as a frequency cutoff value in areas of low bipolar voltage, areas of high fractionation were identified with sensitivity of 91% and specificity of 85% There was no significant reduction in targeted DZ surface areas, and colocalization with DZs was observed in all cases. Applied to the prospective cohort, PF predicted fractionated areas and DZ in 9 of 10 patients. ConclusionA PF annotation algorithm with a cutoff of 220 Hz accurately identifies areas of fractioned signals and accurately predicts DZs during ILAM.

U-Net based vessel segmentation for murine brains with small micro-magnetic resonance imaging reference datasets.

Identification and quantitative segmentation of individual blood vessels in mice visualized with preclinical imaging techniques is a tedious, manual or semiautomated task that can require weeks of reviewing hundreds of levels of individual data sets. Preclinical imaging, such as micro-magnetic resonance imaging (μMRI) can produce tomographic datasets of murine vasculature across length scales and organs, which is of outmost importance to study tumor progression, angiogenesis, or vascular risk factors for diseases such as Alzheimer's. Training a neural network capable of accurate segmentation results requires a sufficiently large amount of labelled data, which takes a long time to compile. Recently, several reasonably automated approaches have emerged in the preclinical context but still require significant manual input and are less accurate than the deep learning approach presented in this paper-quantified by the Dice score. In this work, the implementation of a shallow, three-dimensional U-Net architecture for the segmentation of vessels in murine brains is presented, which is (1) open-source, (2) can be achieved with a small dataset (in this work only 8 μMRI imaging stacks of mouse brains were available), and (3) requires only a small subset of labelled training data. The presented model is evaluated together with two post-processing methodologies using a cross-validation, which results in an average Dice score of 61.34% in its best setup. The results show, that the methodology is able to detect blood vessels faster and more reliably compared to state-of-the-art vesselness filters with an average Dice score of 43.88% for the used dataset.

The visuo-sensorimotor substrate of co-speech gesture processing

Co-speech gestures are integral to human communication and exhibit diverse forms, each serving a distinct communication function. However, existing literature has focused on individual gesture types, leaving a gap in understanding the comparative neural processing of these diverse forms. To address this, our study investigated the neural processing of two types of iconic gestures: those representing attributes or event knowledge of entity concepts, beat gestures enacting rhythmic manual movements without semantic information, and self-adaptors. During functional magnetic resonance imaging, systematic randomization and attentive observation of video stimuli revealed a general neural substrate for co-speech gesture processing primarily in the bilateral middle temporal and inferior parietal cortices, characterizing visuospatial attention, semantic integration of cross-modal information, and multisensory processing of manual and audiovisual inputs. Specific types of gestures and grooming movements elicited distinct neural responses. Greater activity in the right supramarginal and inferior frontal regions was specific to self-adaptors, and is relevant to the spatiomotor and integrative processing of speech and gestures. The semantic and sensorimotor regions were least active for beat gestures. The processing of attribute gestures was most pronounced in the left posterior middle temporal gyrus upon access to knowledge of entity concepts. This fMRI study illuminated the neural underpinnings of gesture-speech integration and highlighted the differential processing pathways for various co-speech gestures.

IMPLEMENTASI PENGINPUTAN DATA COMPONENT ELECTRONICA BERBASIS WEB MENGGUNAKAN METODE AGILE

ABSTRACT
 Along with the rapid development of the times, human needs that are always increasing, especially increasingly advanced technology encourages every change that is much more modern. A need that is indispensable for an organization, company, agency, and so on is like a website. Where the website becomes a necessity both in business activities but also needed in every sales promotion and helps in work within an organization or company. PT. PCI is one of the companies that develops in the field of technology, where the company still inputs work component data manually so that the data input is less accurate. With the help of artificial intelligence can provide solutions in inputting data Entering component data and providing innovation using website-based technology can provide learning for every employee to be able to increase knowledge about technology, changes in a website-based method with manual input are very different, data that was originally input manually but can be made manually automatic. AGILE is known as a form of data development, the development works collaboratively, structured and organized so that the development is able to provide ease of work. With this research carried out in order to introduce how to input data properly and correctly. Artificial intelligence used on this website that produces input component data properly and correctly for use by employees in the work process, the use of artificial intelligence is expected to be an alternative media, so that it becomes more interesting in inputting data. 
 
 Keywords: PT.PCI; Artificial Intelligence; Website; Agilel; Data component

IMPLEMENTASI APLIKASI PENGINPUTAN INVENTORY MENGGUNAKAN METODE AGILE BERBASIS WEB

The process of collecting manual input data info Microsoft Exel takes a long time, data management cannot be done by several warehouse admins at the same time, making inventory reports is often not on time from schedule because they have to recapitulate goods receipt data and goods expenditure data, submit inventory reports to the department takes a long time, data management errors often occur in entering data, deleting data. Microsoft Excel also cannot store data and information centrally in a database, so there is often a discrepancy between the information obtained and data loss caused by human error. Agile development methods are a methodology in software development that is based on an iterative process consisting of rules and agreed solutions. With the aim to produce a software that has a high selling value but uses the lowest possible manufacturing costs. So as to obtain research results in the form of applications built for inventory input systems, namely with web-based agile methods.

S748 Automated Documentation of Colonoscopy Quality Measures With Machine Learning Is More Accurate Than Manual Human Input

S748 Automated Documentation of Colonoscopy Quality Measures With Machine Learning Is More Accurate Than Manual Human Input

3D reconstruction of the scapula from biplanar X-rays for pose estimation and morphological analysis.

Patient-specific scapular shape in functional posture can be highly relevant to clinical research. Biplanar radiography is a relevant modality for that purpose with already two existing assessment methods. However, they are either time-consuming or lack accuracy. The aim of this study was to propose a new, more user-friendly and accurate method to determine scapular shape. The proposed method relied on simplified manual inputs and an upgraded version of the first 3D estimate based on statistical inferences and Moving-Least Square (MLS) deformation of a template. Then, manual adjustments, with real-time MLS algorithm and contour matching adjustments with an adapted minimal path method, were added to improve the match between the projected 3D model and the radiographic contours. The accuracy and reproducibility of the method were assessed (with 6 and 12 subjects, respectively). The shape accuracy was in average under 2mm (1.3mm in the glenoid region). The reproducibility study on the clinical parameters found intra-observer 95% confidence intervals under 3mm or 3° for all parameters, except for glenoid inclination and Critical Shoulder Angle, ranging between 3° and 6°. This method is a first step towards an accurate reconstruction of the scapula to assess clinical parameters in a functional posture. This can already be used in clinical research on non-pathologic bones to investigate the scapulothoracic joint in functional position.

Advancing Urban Wastewater Management: Optimizing Sewer Performance through Innovative Material Selection for the Armlet with a Wet Circuit Measurement System

Rainwater infiltration presents substantial challenges for urban wastewater management systems. This article delves into enhancing the quality of wastewater systems by proposing a novel device designed to tackle this issue comprehensively. The focal point of our research revolves around the conceptualization, construction, rigorous testing, and the potential multifaceted applications of this innovative wastewater device. Our study is dedicated to assessing the viability of a cutting-edge apparatus that empowers municipal entities in swiftly identifying rainwater ingress points within channels during precipitation events. Our findings vividly showcase the device’s remarkable capability to directly measure moisture levels along the channel’s path, eliminating the arduous need for manual data input, extensive data collection, and intricate water analysis procedures. To ensure the seamless flow of both sewage and water within the sewer channel, the use of a relatively slender strap is conventionally favored. However, factoring in the requisite structural robustness, we recommend a minimum thickness of 4 mm for 3D printing applications. For instances where maintaining the channel’s cross-sectional area integrity is paramount, opting for an armlet with a wet circuit measurement thickness of up to 7 mm is vital. In the realm of material selection, our investigation advocates for the utilization of PC/ABS (polycarbonate/Acrylonitrile Butadiene Styrene), ABS, ASA (Acrylonitrile Styrene Acrylate), or HIPS (High Impact Polystyrene) for strap housing. For sewer diameters surpassing 315 mm, the application of thin-walled PVC (Poly Vinyl Chloride) emerges as a practical recommendation. Notably, the incorporation of PVC flat bars is discouraged, as their presence might potentially hinder the fluidity of sewage flow, thereby compromising the precision of wet circuit measurements. The pivotal innovation lies in the armlet with a wet circuit measurement system, harboring immense potential for broad-scale integration across municipal facilities. This solution emerges as a streamlined and efficient strategy, offering a comprehensive avenue for continuously monitoring, fine-tuning, and optimizing the structural soundness and operational efficacy of sewer systems.

Assessing the Compliance and Accuracy of a National Australian Head and Neck Cancer (HNC) Database

Accurate cancer databases enable auditing of patient management, and this knowledge facilitates optimizing care. A multi-institutional organization, the largest single provider of radiation oncology services in Australia, has developed its own national database (ND). All patients are entered on the ND as a prerequisite for generating a radiotherapy prescription. A significant component of the ND is automated, but manual input from the treating radiation oncologist (RO) is also required. The purpose of this study was to assess the compliance and accuracy of the data entered on this ND for head and neck cancer (HNC) patients. We included all HNC patients with either oral cavity cancer or oropharynx cancer (ICD-10 coding) treated between September 2021 and September 2022 to assess compliance. We randomly selected 25% of these cases and assigned them to 3 HNC ROs to manually review the accuracy of all clinical data points. There were 166 HNC patients, 139 oropharynx and 27 oral cavity. Compliance in the 166 patients was excellent (94% or higher) for the majority of data points - age, gender, diagnosis ICD code, diagnosis date, laterality, TNM classification, radiotherapy dose, fractionation and technique and start and completion dates. Compliance was good (85% or more) for smoking history, use of chemotherapy, and p16 status (oropharynx). Compliance was poor (43%) for specific chemotherapy regimens. Accuracy was high (92% or higher) for diagnosis ICD code, smoking history, use of chemotherapy; good (87% or higher) for p16 status (oropharynx), laterality and histopathology; and poor for date of diagnosis (75%), TNM classification (62%) and specific chemotherapy regimens (29%). The ND is a powerful tool for assessing patient care. Overall, compliance was very good. Accuracy was very good for most items, and we have highlighted areas where improvements can be made. This study shows that a compliant and accurate ND is achievable and supports the next goal of additional items to be included in the ND, specifically patient outcome data.

GlassMessaging

Communicating with others while engaging in simple daily activities is both common and natural for people. However, due to the hands- and eyes-busy nature of existing digital messaging applications, it is challenging to message someone while performing simple daily activities. We present GlassMessaging, a messaging application on Optical See-Through Head-Mounted Displays (OHMDs), to support messaging with voice and manual inputs in hands- and eyes-busy scenarios. GlassMessaging is iteratively developed through a formative study identifying current messaging behaviors and challenges in common multitasking with messaging scenarios. We then evaluated this application against the mobile phone platform on varying texting complexities in eating and walking scenarios. Our results showed that, compared to phone-based messaging, GlassMessaging increased messaging opportunities during multitasking due to its hands-free, wearable nature, and multimodal input capabilities. The affordance of GlassMessaging also allows users easier access to voice input than the phone, which thus reduces the response time by 33.1% and increases the texting speed by 40.3%, with a cost in texting accuracy of 2.5%, particularly when the texting complexity increases. Lastly, we discuss trade-offs and insights to lay a foundation for future OHMD-based messaging applications.

Joint network for specular highlight detection and adversarial generation of specular-free images trained with polarimetric data

Specular highlights in images pose a significant challenge in algorithms for image segmentation, object detection and other image-based decision-making systems. However, most systems ignore this particular scenario and neglect input images with specular highlights instead of mitigating it in the pre-processing stage. In this paper, we leverage deep neural networks and take advantage of the varying illumination information in polarimetric images for synthesizing specular-free images. We propose a multi-domain Specular Highlight Mitigation Generative Adversarial Network (SHMGAN) with self-attention. SHMGAN consists of a single generator–discriminator pair trained simultaneously using polarimetric images. The proposed GAN uses a dynamically generated attention mask based on a specularity segmentation network without requiring additional manual input. The network is able to learn the illumination variation between the four polarimetric images and a pseudo-diffuse image. Once trained, SHMGAN is able to generate specular-free images from a single RGB image as input; without requiring any additional external labels. The proposed network is trained and tested publicly available datasets of real-world images. SHMGAN is able to accurately identify the specularity affected pixels and generates high visual quality images with mitigated specular reflections. The generated images are realistic and have very low noise, distortions and aberrations compared to the existing state-of-the-art methods for specular highlight removal.

A practical example of an integrated interoperable neuromonitoring system based on IEEE 11073 SDC and HL7

Abstract Purpose: With the ongoing work on the Health Level Seven (HL7) standards and the IEEE 11073 Serviceoriented Device Connectivity (SDC) family, the demand for open integration of medical devices in the operating room (OR) has become clear. Nevertheless, there are very few interoperable medical devices available to date. This work describes a practical example of connecting an intraoperative neuromonitoring (IONM) device with a radiofrequency (RF) surgical device based on SDC and with the hospital information system (HIS) based on HL7. Methods: Before starting the surgery, patient-related data must be entered into the neuromonitoring system. To minimize manual input, we integrated an automized patient data query based on HL7, which completes all other necessary data provided by the HIS after entering the patient identification number. During the surgery, while mapping neural structures in the situs using IONM, the parallel operation of RF devices for coagulation generates artifacts in the neuromonitoring signals, which makes a reliable interpretation of the IONM signals impossible. Therefore, we developed an IEEE 11073 SDC interface for the neuromonitoring device and implemented an SDC-based OR control panel. While placing the hand probe for mapping neural structures in the situs, the OR control panel suppresses the coagulation of the electrosurgical instrument and only reenables it, after the mapping has been terminated. After the surgery, the generated IONM report can be uploaded into the HIS using HL7. Therewith it is assigned automatically to the previously selected patient. Result: With the SDC- and HL7-enabled neuromonitoring system, we showed a practical use case of interoperable medical devices to optimize surgical workflow.

Leveraging railway topology to automatically generate track geometric information models from airborne LiDAR data

Geometric information modelling from point cloud data (PCD) is a fundamental step of the digital twinning process for rail infrastructure. Currently, this onerous procedure outweighs the anticipated benefits of the resulting model and expends 74% of the modellers' effort on converting PCD to a model. The cost of the resulting geometric information models (GIM) can be reduced by automating the modelling process. State-of-the-art methods cannot offer large-scale GIM generation required over kilometres without forfeiting precision and manual cost. This paper addresses the challenge of achieving such automation by leveraging the highly standardised topology of railways to automatically generate GIMs of rail track structures.The method first automatically segments rails and track beds, delivering labelled point clusters of track structure elements. Next, it converges the segmented rails with pre-defined parametric assemblies of different rail profiles and uses a mesh-based approach to reconstruct the geometry of the track bed, delivering industry foundation classes (IFC) files of railway track structure elements. Experiments on 18.5 km railway PCDs yielded an average segmentation of 98.1% and 94.9% F1 scores and overall modelling accuracy of 3.5 cm and 2.8 cm root mean square error (RMSE)s for rails and track beds. The proposed method can realise an estimated time savings of 88.9% without needing any manual inputs.

MA-QC: Free online software for prostate MR quality control and PI-QUAL assessment

PurposeTo validate MRI Analyzer Quality Control (MA-QC), a free and open-source online software designed to facilitate MR data acquisition quality control and PI-QUAL score calculation. Material and methodsMA-QC is a web-based software, designed for analysing DICOM data related to MR acquisition parameters. The software allows automatic extraction of 18 technical criteria, and manual input of 12 visual criteria, to calculate the PI-QUAL score. We collected 100 prostate MRI datasets from four MR device manufacturers to test data compatibility, automatic sequence recognition, and robustness of technical criteria extraction from DICOM data. The main issue was to determine the spatial resolution in the phase and frequency directions, due to variable encoding of the DICOM datasets. ResultsAcquisition data could be extracted from all sample examinations (100%), with a median analysis speed of 15.2 ± 4.4 images per second and mean processing time of 96 [11–326] seconds per examination. MA-CQ automatically detected the optimal T2-w, DWI and DCE sequences in 71 out of 100 (71%) cases, and required manual selection of at least one sequence in 29 out of 100 (29%) cases to get the best parameters. Display of technical criteria for the 3 sequences was instantaneous. PI-QUAL score could be calculated in all cases. ConclusionThis software brings substantial help in the quality assessment of prostate MRI examinations, by providing fast extraction of series data and the 18 technical parameters of PI-QUAL. PI-QUAL scoring can be performed in less than two minutes, helping to focus on the visual criteria, allowing use of this software in the clinical workflow in the aim of improving overall image quality in prostate MR imaging.

ReProMSig: an integrative platform for development and application of reproducible multivariable models for cancer prognosis supporting guideline-based transparent reporting.

Adequate reporting is essential for evaluating the performance and clinical utility of a prognostic prediction model. Previous studies indicated a prevalence of incomplete or suboptimal reporting in translational and clinical studies involving development of multivariable prediction models for prognosis, which limited the potential applications of these models. While reporting templates introduced by the established guidelines provide an invaluable framework for reporting prognostic studies uniformly, there is a widespread lack of qualified adherence, which may be due to miscellaneous challenges in manual reporting of extensive model details, especially in the era of precision medicine. Here, we present ReProMSig (Reproducible Prognosis Molecular Signature), a web-based integrative platform providing the analysis framework for development, validation and application of a multivariable prediction model for cancer prognosis, using clinicopathological features and/or molecular profiles. ReProMSig platform supports transparent reporting by presenting both methodology details and analysis results in a strictly structured reporting file, following the guideline checklist with minimal manual input needed. The generated reporting file can be published together with a developed prediction model, to allow thorough interrogation and external validation, as well as online application for prospective cases. We demonstrated the utilities of ReProMSig by development of prognostic molecular signatures for stage II and III colorectal cancer respectively, in comparison with a published signature reproduced by ReProMSig. Together, ReProMSig provides an integrated framework for development, evaluation and application of prognostic/predictive biomarkers for cancer in a more transparent and reproducible way, which would be a useful resource for health care professionals and biomedical researchers.