Clinician Workload Research Articles

Wound imaging software and digital platform to assist review of surgical wounds using patient smartphones: The development and evaluation of artificial intelligence (WISDOM AI study).

Surgical patients frequently experience post-operative complications at home. Digital remote monitoring of surgical wounds via image-based systems has emerged as a promising solution for early detection and intervention. However, the increased clinician workload from reviewing patient-submitted images presents a challenge. This study utilises artificial intelligence (AI) to prioritise surgical wound images for clinician review, aiming to efficiently manage workload. Conducted from September 2023 to March 2024, the study phases included compiling a training dataset of 37,974 images, creating a testing set of 3,634 images, developing an AI algorithm using 'You Only Look Once' models, and conducting prospective tests compared against clinical nurse specialists' evaluations. The primary objective was to validate the AI's sensitivity in prioritising wound reviews, alongside assessing intra-rater reliability. Secondary objectives focused on specificity, positive predictive value (PPV), and negative predictive value (NPV) for various wound features. The AI demonstrated a sensitivity of 89%, exceeding the target of 85% and proving effective in identifying cases requiring priority review. Intra-rater reliability was perfect, achieving 100% consistency in repeated assessments. Observations indicated variations in detecting wound characteristics across different skin tones; sensitivity was notably lower for incisional separation and discolouration in darker skin tones. Specificity remained high overall, with some results favouring darker skin tones. The NPV were similar for both light and dark skin tones. However, the NPV was slightly higher for dark skin tones at 95% (95% CI: 93%-97%) compared to 91% (95% CI: 87%-92%) for light skin tones. Both PPV and NPV varied, especially in identifying sutures or staples, indicating areas needing further refinement to ensure equitable accuracy. The AI algorithm not only met but surpassed the expected sensitivity for identifying priority cases, showing high reliability. Nonetheless, the disparities in performance across skin tones, especially in recognising certain wound characteristics like discolouration or incisional separation, underline the need for ongoing training and adaptation of the AI to ensure fairness and effectiveness across diverse patient groups.

Machine Learning for Aortic Stenosis: Enhancing Diagnostic Accuracy and Security in Health Information Systems

Aortic Stenosis (AS) is a prevalent and potentially life-threatening cardiovascular condition that requires accurate diagnosis for optimal management. Traditional diagnostic methods, while effective, face limitations in terms of precision, timely detection, and clinician workload. The emergence of Machine Learning (ML) offers an innovative solution to these challenges, enhancing diagnostic accuracy and improving patient outcomes. This article explores how ML algorithms can be utilized to refine AS diagnosis, particularly through medical imaging and predictive modeling. In addition, the integration of ML in health information systems must be coupled with robust data security measures to protect sensitive patient information. We discuss the intersection of machine learning and healthcare IT security, focusing on innovative methods for safeguarding health data while improving diagnostic efficiency. The paper examines various ML techniques applied to AS, evaluates their impact on clinical workflows, and identifies the security protocols necessary to ensure compliance with privacy regulations. Finally, the study presents the potential challenges and future directions for integrating ML and health information security in clinical practice.

Artificial Intelligence in Emergency Medicine: A Literature Review

IntroductionArtificial intelligence (AI) is rapidly transforming medical fields, particularly emergency medicine (EM), where timely decision-making is crucial. AI offers potential benefits in diagnostic accuracy, patient care optimization, and workflow efficiency within emergency departments (EDs). Purpose of WorkThis review aims to synthesize recent advancements in AI applications within emergency medicine, focusing on diagnostic support, patient triage, clinical decision support systems (CDSS), and workflow optimization. Additionally, we highlight the potential benefits, challenges, and future directions for AI in EM. Material and MethodsA comprehensive literature search was conducted using PubMed and Google Scholar databases. We reviewed peer-reviewed articles from 2008 to 2024, focusing on AI-driven solutions in EDs. Keywords included "artificial intelligence," "emergency medicine," "machine learning," and "clinical decision support." Studies were selected based on their relevance to AI applications in EM, diagnostic improvements, and operational efficiency. The results highlight the promising role of AI in improving diagnostic accuracy, reducing overcrowding, optimizing triage processes, and addressing clinician workload. However, challenges like ethical concerns, data bias, and the need for clinical validation remain. Further research is necessary to integrate AI more effectively in clinical practice.

Health Impact Assessment in Personalized Prevention: three applications on pharmacogenomic testing

Abstract Background In recent years, literature on the efficacy of genetic and genomic tests has expanded, yet the impact of personalized prevention programs that include such tests remains understudied. Our study aims to test the use of the Health Impact Assessment (HIA) framework, traditionally applied in non-health policy evaluation, to assess the impacts of personalized prevention policies. Methods We selected a policy mandating dihydropyrimidine dehydrogenase (DPYD) genetic test use before prescribing fluoropyrimidine for colorectal cancer (CRC) patients. Three HIAs are being conducted in Italy, Portugal, and Finland to assess the applicability across different contexts. The assessment involves literature review, comparative risk modeling, and stakeholder interviews. Three national steering committees, comprising representatives from key interest groups and experts, will evaluate evidence to gauge policy impacts and offer recommendations. Results The potential impacts identified include CRC patients’ outcomes, economic, organizational, patient experience, clinician workload, and equity. Preliminary results show a consensus in literature and national guidelines on the ability of the DPYD test to reduce adverse events and enhance the quality of life of CRC patients. However, the assessment reveals context-specific effects regarding equity, influenced by the prevalence of ethnic groups and variations in access to health services across regions. Differences also emerged on organizational aspects like the availability of resources such as laboratories and personnel. A lack of literature was observed on patient acceptability and clinician attitudes towards the test. Conclusions The use of HIA in personalized prevention might assist in identifying policy impacts often overlooked through the involvement of key interest groups in the assessment process. Such effort can further emphasize the significance of evaluating these programs while considering the geographical context. Key messages • Our study innovatively applies the Health Impact Assessment framework to evaluate personalized prevention programs in pharmacogenomic, addressing a critical research gap. • Preliminary findings underscore the importance of considering geographical factors in public health policy evaluation, highlighting nuanced context-specific effects.

Can large language models address unmet patient information needs and reduce provider burnout in the management of thyroid disease?

BackgroundPatient electronic messaging has increased clinician workload contributing to burnout. Large language models can respond to these patient queries, but no studies exist on large language model responses in thyroid disease. MethodsThis cross-sectional study randomly selected 33 of 52 patient questions found on Reddit/askdocs. Questions were found through a “thyroid + cancer” or “thyroid + disease” search and had verified-physician responses. Additional responses were generated using ChatGPT-3.5 and GPT-4. Questions and responses were anonymized and graded for accuracy, quality, and empathy using a 4-point Likert scale by blinded providers, including 4 surgeons, 1 endocrinologist, and 2 physician assistants (n = 7). Results were analyzed using a single-factor analysis of variance. ResultsFor accuracy, the results averaged 2.71/4 (standard deviation 1.04), 3.49/4 (0.391), and 3.66/4 (0.286) for physicians, GPT-3.5, and GPT-4, respectively (P < .01), where 4 = completely true information, 3 = greater than 50% true information, and 2 = less than 50% true information. For quality, the results were 2.37/4 (standard deviation 0.661), 2.98/4 (0.352), and 3.81/4 (0.36) for physicians, GPT-3.5, and GPT-4, respectively (P < .01), where 4 = provided information beyond what was asked, 3 = completely answers the question, and 2 = partially answers the question. For empathy, the mean scores were 2.37/4 (standard deviation 0.661), 2.80/4 (0.582), and 3.14/4 (0.578) for physicians, GPT-3.5, and GPT-4, respectively (P < .01), where 4 = anticipates and infers patient feelings from the expressed question, 3 = mirrors the patient's feelings, and 2 = contains no dismissive comments. Responses by GPT were ranked first 95% of the time. ConclusionsLarge language model responses to patient queries about thyroid disease have the potential to be more accurate, complete, empathetic, and consistent than physician responses.

Model-agnostic confidence measurement for aggregating multimodal ensemble models in automatic diagnostic systems

Automatic diagnostic systems (ADSs) have been garnering increased attention because they can alleviate the workload of clinicians by assisting in diagnosis and offering low-cost access to healthcare for people in medically underserved areas. ADS can suggest potential diseases by analyzing a patient's self-report. Previous research on ADS has leveraged diagnostic case data from various patients and medical knowledge to diagnose diseases, with multimodal ensemble methods proving particularly effective. However, the existing multimodal ensemble method combines the probabilities of different models in the aggregating process, which can not properly combine the probabilities that are produced by different criteria. To address these issues, we propose an effective aggregation framework for multimodal ensembles that can properly aggregate model-agnostic confidence scores and predictions from each model. Our framework transforms probability scores from different criteria into unified aggregation rule-based scores and reflects the gap between the probabilities that may be blurred in the aggregation process through the confidence score. In particular, The proposed confidence measurement method employs a post-analysis approach with the developed model or algorithm, making it adaptable in a model-agnostic manner and suitable for multimodal ensemble learning that utilizes heterogeneous prediction results. Our experimental results demonstrate that our framework outperforms existing approaches by more effectively leveraging the strengths of each ensemble member.

Automatic segmentation of the maxillary sinus on cone beam computed tomographic images with U-Net deep learning model

BackgroundMedical imaging segmentation is the use of image processing techniques to expand specific structures or areas in medical images. This technique is used to separate and display different textures or shapes in an image. The aim of this study is to develop a deep learning-based method to perform maxillary sinus segmentation using cone beam computed tomography (CBCT) images. The proposed segmentation method aims to provide better image guidance to surgeons and specialists by determining the boundaries of the maxillary sinus cavities. In this way, more accurate diagnoses can be made and surgical interventions can be performed more successfully.MethodsIn the study, axial CBCT images of 100 patients (200 maxillary sinuses) were used. These images were marked to identify the maxillary sinus walls. The marked regions are masked for use in the maxillary sinus segmentation model. U-Net, one of the deep learning methods, was used for segmentation. The training process was carried out for 10 epochs and 100 iterations per epoch. The epoch and iteration numbers in which the model showed maximum success were determined using the early stopping method.ResultsAfter the segmentation operations performed with the U-Net model trained using CBCT images, both visual and numerical results were obtained. In order to measure the performance of the U-Net model, IoU (Intersection over Union) and F1 Score metrics were used. As a result of the tests of the model, the IoU value was found to be 0.9275 and the F1 Score value was 0.9784.ConclusionThe U-Net model has shown high success in maxillary sinus segmentation. In this way, fast and highly accurate evaluations are possible, saving time by reducing the workload of clinicians and eliminating subjective errors.

Issues that clinical decision support system must address

Clinical decision support system (CDSS) is one important application for artificial intelligence. Its utilization in clinic evokes controversial argument. Obviously, its development is not as good as expected to reduce the workload of clinicians, to improve the accuracy of clinical decision, to reduce misdiagnosis etc. Here arises the calling on ideology innovation for building CDSS to improve its performance in clinic. Being aware of that problem-solving in clinic is a prerequisite for CDSS to complete its tasks, issues from six aspects that CDSS must address were identified, which might help to find a right way to construct CDSS. It is more than a technic work, it is the cognition over CDSS and requires effort from clinicians, software scientists, and medical scientist together.

Development and validation of an artificial intelligence model for the classification of hip fractures using the AO-OTA framework.

Artificial intelligence (AI) has the potential to aid in the accurate diagnosis of hip fractures and reduce the workload of clinicians. We primarily aimed to develop and validate a convolutional neural network (CNN) for the automated classification of hip fractures based on the 2018 AO-OTA classification system. The secondary aim was to incorporate the model's assessment of additional radiographic findings that often accompany such injuries. 6,361 plain radiographs of the hip taken between 2002 and 2016 at Danderyd University Hospital were used to train the CNN. A separate set of 343 radiographs representing 324 unique patients was used to test the performance of the network. Performance was evaluated using area under the curve (AUC), sensitivity, specificity, and Youden's index. The CNN demonstrated high performance in identifying and classifying hip fracture, with AUCs ranging from 0.76 to 0.99 for different fracture categories. The AUC for hip fractures ranged from 0.86 to 0.99, for distal femur fractures from 0.76 to 0.99, and for pelvic fractures from 0.91 to 0.94. For 29 of 39 fracture categories, the AUC was ≥ 0.95. We found that AI has the potential for accurate and automated classification of hip fractures based on the AO-OTA classification system. Further training and modification of the CNN may enable its use in clinical settings.

Pediatric anesthesia in China.

In China, healthcare has lagged relative to its economic boom during the past 40 years. While the top tier hospitals offer pediatric perioperative care like high-income countries, lower-tier hospitals deliver lesser services of variable quality and safety related to equipment, supplies, clinician education, and availability. The national residency training program and the pediatric anesthesia fellowship program was established in 2013 and 2018 respectively. Increasing clinician workload from patient demand and a lack of consistency in quality and capability between rural and urban areas remain challenging.

Automated Clinical Trial Cohort Definition and Evaluation with CQL and CDS-Hooks.

Patient recruitment for clinical trials faces major challenges with current methods being costly and often requiring time-consuming acquisition of medical histories and manual matching of potential subjects. Designing and implementing an Electronic Health Record (EHR) and domain-independent automation architecture using Clinical Decision Support (CDS) standards that allows researchers to effortlessly enter standardized trial criteria to retrieve eligibility statistics and integration into a clinician workflow to automatically trigger evaluation without added clinician workload. Cohort criteria are translated into the Clinical Quality Language (CQL) and integrated into Measures and CDS-Hooks for patient- and population-level evaluation. Successful application of simplified real-world trial criteria to Fast Healthcare Interoperability Resources (FHIR®) test data shows the feasibility of obtaining individual patient eligibility and trial details as well as population eligibility statistics and a list of qualifying patients. Employing CDS standards for automating cohort definition and evaluation shows promise in streamlining patient selection, aligning with increasing legislative demands for standardized healthcare data.

PalScDiff: A diffusion-based framework with progressive augmentation learning and semantic consistency for breast ultrasound tumor segmentation

Background: Breast cancer diagnosis relies on accurate lesion segmentation in medical images. Automated computer-aided diagnosis reduces clinician workload and improves efficiency, but existing image segmentation methods face challenges in model performance and generalization. Objective: This study aims to develop a generative framework using a denoising diffusion model for efficient and accurate breast cancer lesion segmentation in medical images. Methods: We design a novel generative framework, PalScDiff, that leverages a denoising diffusion probabilistic model to reconstruct the label distribution for medical images, thereby enabling the sampling of diverse, plausible segmentation outcomes. Specifically, with the condition of the corresponding image, PalScDiff learns to estimate the masses region probability through denoising step by step. Furthermore, we design a Progressive Augmentation Learning strategy to incrementally handle segmentation challenges of irregular and blurred tumors. Moreover, multi-round sampling is employed to achieve robust breast mass segmentation. Results: Our experimental results show that PalScDiff outperforms established models such as U-Net and transformer-based alternatives, achieving an accuracy of 95.15%, precision of 79.74%, Dice coefficient of 77.61%, and Intersection over Union (IOU) of 81.51% . Conclusion: The proposed model demonstrates promising capabilities for accurate and efficient computer-aided segmentation of breast cancer.

Improving Medical Speech-to-Text Accuracy using Vision-Language Pre-training Models.

Automatic Speech Recognition (ASR) is a technology that converts spoken words into text, facilitating interaction between humans and machines. One of the most common applications of ASR is Speech-To-Text (STT) technology, which simplifies user workflows by transcribing spoken words into text. In the medical field, STT has the potential to significantly reduce the workload of clinicians who rely on typists to transcribe their voice recordings. However, developing an STT model for the medical domain is challenging due to the lack of sufficient speech and text datasets. To address this issue, we propose a medical-domain text correction method that modifies the output text of a general STT system using the Vision Language Pre-training (VLP) method. VLP combines textual and visual information to correct text based on image knowledge. Our extensive experiments demonstrate that the proposed method offers quantitatively and clinically significant improvements in STT performance in the medical field. We further show that multi-modal understanding of image and text information outperforms single-modal understanding using only text information.

PulmonU-Net: a semantic lung disease segmentation model leveraging the benefit of multiscale feature concatenation and leaky ReLU

Pulmonary diseases impact lung functionality and can cause health complications. X-ray imaging is an initial diagnostic approach for evaluating lung conditions. Manual segmentation of lung infections from X-rays is time-consuming and subjective. Automated segmentation has gained interest to reduce clinician workload. Semantic segmentation involves labelling individual pixels in X-rays to highlight infected regions. This article presents PulmonU-Net, an innovative semantic segmentation model using PulmonNet modules as the base network to highlight infected areas in chest X-rays. PulmonNet modules leverage global and local chest X-ray characteristics to create intricate feature maps. Incorporating leaky ReLU activation enables uninterrupted neuron functioning during learning. By adding PulmonNet modules in the encoder's deeper layers, the model addresses vanishing gradients and improves dice similarity coefficient to 94.25%. Real-time testing and prediction visualization demonstrate PulmonU-Net's effectiveness for automated lung infection segmentation from chest X-rays.

Small Patients but a Heavy Lift: Workload and Burnout of Advanced Practice Providers and Physicians in a Level IV Neonatal Intensive Care Unit.

This study explored the association between workload and the level of burnout reported by clinicians in our neonatal intensive care unit (NICU). A qualitative analysis was used to identify specific factors that contributed to workload and modulated clinician workload in the NICU. We conducted a study utilizing postshift surveys to explore workload of 42 NICU advanced practice providers and physicians over a 6-month period. We used multinomial logistic regression models to determine associations between workload and burnout. We used a descriptive qualitative design with an inductive thematic analysis to analyze qualitative data. Clinicians reported feelings of burnout on nearly half of their shifts (44%), and higher levels of workload during a shift were associated with report of a burnout symptom. Our study identified 7 themes related to workload in the NICU. Two themes focused on contributors to workload, 3 themes focused on modulators of workload, and the final 2 themes represented mixed experiences of clinicians' workload. We found an association between burnout and increased workload. Clinicians in our study described common contributors to workload and actions to reduce workload. Decreasing workload and burnout along with improving clinician well-being requires a multifaceted approach on unit and systems levels.

Two is better than one: longitudinal detection and volumetric evaluation of brain metastases after Stereotactic Radiosurgery with a deep learning pipeline.

Close MRI surveillance of patients with brain metastases following Stereotactic Radiosurgery (SRS) treatment is essential for assessing treatment response and the current disease status in the brain. This follow-up necessitates the comparison of target lesion sizes in pre- (prior) and post-SRS treatment (current) T1W-Gad MRI scans. Our aim was to evaluate SimU-Net, a novel deep-learning model for the detection and volumetric analysis of brain metastases and their temporal changes in paired prior and current scans. SimU-Net is a simultaneous multi-channel 3D U-Net model trained on pairs of registered prior and current scans of a patient. We evaluated its performance on 271 pairs of T1W-Gad MRI scans from 226 patients who underwent SRS. An expert oncological neurosurgeon manually delineated 1,889 brain metastases in all the MRI scans (1,368 with diameters > 5mm, 834 > 10mm). The SimU-Net model was trained/validated on 205 pairs from 169 patients (1,360 metastases) and tested on 66 pairs from 57 patients (529 metastases). The results were then compared to the ground truth delineations. SimU-Net yielded a mean (std) detection precision and recall of 1.00±0.00 and 0.99±0.06 for metastases > 10mm, 0.90±0.22 and 0.97±0.12 for metastases > 5mm of, and 0.76±0.27 and 0.94±0.16 for metastases of all sizes. It improves lesion detection precision by 8% for all metastases sizes and by 12.5% for metastases < 10mm with respect to standalone 3D U-Net. The segmentation Dice scores were 0.90±0.10, 0.89±0.10 and 0.89±0.10 for the above metastases sizes, all above the observer variability of 0.80±0.13. Automated detection and volumetric quantification of brain metastases following SRS have the potential to enhance the assessment of treatment response and alleviate the clinician workload.

Cumulated time to chart closure: a novel electronic health record-derived metric associated with clinician burnout

Abstract Objective We sought to determine whether average cumulated time to chart closure (CTCC), a novel construct to measure clinician workload burden, and electronic health record (EHR) measures were associated with a validated measure of burnout. Materials and methods Physicians at a large academic institution participated in a well-being survey that was linked to their EHR use data. CTCC was defined as the average time from the start of patient encounters to chart closure over a set of encounters. Established EHR use measures including daily total time in the EHR (EHR-Time8), time in the EHR outside scheduled hours, work outside of work (WOW8), and time spent on inbox (IB-Time8) were calculated. We examined the relationship between CTCC, EHR use metrics, and burnout using descriptive statistics and adjusted logistic regression models. Results We included data from 305 attendings, encompassing 242 432 ambulatory encounters (2021). Among them, 42% (128 physicians) experienced burnout. The median CTCC for all clinicians was 32.5 h. Unadjusted analyses revealed significant associations between CTCC, WOW8, IB-Time8, and burnout. In a final adjusted model, only CTCC remained statistically significant with an odds ratio estimate of 1.42 (95% CI, 1.00-2.01). Discussion These results suggest that CTCC is predictive of burnout and that purely measuring duration of interaction with the EHR itself is not sufficient to capture burnout. Conclusion Workload burden as manifested by average CTCC has the potential to be a practical, quantifiable measure that will allow for identification of clinicians at risk of burnout and to assess the success of interventions designed to address burnout.

ABR-Attention: An Attention-Based Model for Precisely Localizing Auditory Brainstem Response.

Auditory Brainstem Response (ABR) is an evoked potential in the brainstem's neural centers in response to sound stimuli. Clinically, characteristic waves, especially Wave V latency, extracted from ABR can objectively indicate auditory loss and diagnose diseases. Several methods have been developed for the extraction of characteristic waves. To ensure the effectiveness of the method, most of the methods are time-consuming and rely on the heavy workloads of clinicians. To reduce the workload of clinicians, automated extraction methods have been developed. However, the above methods also have limitations. This study introduces a novel deep learning network for automatic extraction of Wave V latency, named ABR-Attention. ABR-Attention model includes a self-attention module, first and second-derivative attention module, and regressor module. Experiments are conducted on the accuracy with 10-fold cross-validation, the effects on different sound pressure levels (SPLs), the effects of different error scales and the effects of ablation. ABR-Attention shows efficacy in extracting Wave V latency of ABR, with an overall accuracy of 96.76 ± 0.41 % and an error scale of 0.1ms, and provides a new solution for objective localization of ABR characteristic waves.

Multi-task learning framework to predict the status of central venous catheter based on radiographs

Hospital patients can have catheters and lines inserted during the course of their admission to give medicines for the treatment of medical issues, especially the central venous catheter (CVC). However, malposition of CVC will lead to many complications, even death. Clinicians always detect the status of the catheter to avoid the above issues via X-ray images. To reduce the workload of clinicians and improve the efficiency of CVC status detection, a multi-task learning framework for catheter status classification based on the convolutional neural network (CNN) is proposed. The proposed framework contains three significant components which are modified HRNet, multi-task supervision including segmentation supervision and heatmap regression supervision as well as classification branch. The modified HRNet maintaining high-resolution features from the start to the end can ensure to generation of high-quality assisted information for classification. The multi-task supervision can assist in alleviating the presence of other line-like structures such as other tubes and anatomical structures shown in the X-ray image. Furthermore, during the inference, this module is also considered as an interpretation interface to show where the framework pays attention to. Eventually, the classification branch is proposed to predict the class of the status of the catheter. A public CVC dataset is utilized to evaluate the performance of the proposed method, which gains 0.823 AUC (Area under the ROC curve) and 82.6% accuracy in the test dataset. Compared with two state-of-the-art methods (ATCM method and EDMC method), the proposed method can perform best.

Convolutional-neural-network-based radiographs evaluation assisting in early diagnosis of the periodontal bone loss via periapical radiograph

Background/PurposeThe preciseness of detecting periodontal bone loss is examiners dependent, and this leads to low reliability. The need for automated assistance systems on dental radiographic images has been increased. To the best of our knowledge, no studies have quantitatively and automatically staged periodontitis using dental periapical radiographs. The purpose of this study was to evaluate periodontal bone loss and periodontitis stage on dental periapical radiographs using deep convolutional neural networks (CNNs). Materials and methods336 periapical radiographic images (teeth: 390) between January 2017 and December 2019 were collected and de-identified. All periapical radiographic image datasets were divided into training dataset (n = 82, teeth: 123) and test dataset (n = 336, teeth: 390). For creating an optimal deep CNN algorithm model, the training datasets were directly used for the segmentation and individual tooth detection. To evaluate the diagnostic power, we calculated the degree of alveolar bone loss deviation between our proposed method and ground truth, the Pearson correlation coefficients (PCC), and the diagnostic accuracy of the proposed method in the test datasets. ResultsThe periodontal bone loss degree deviation between our proposed method and the ground truth drawn by the three periodontists was 6.5 %. In addition, the overall PCC value of our proposed system and the periodontists’ diagnoses was 0.828 (P < 0.01). The total diagnostic accuracy of our proposed method was 72.8 %. The diagnostic accuracy was highest for stage III (97.0 %). ConclusionThis tool helps with diagnosis and prevents omission, and this may be especially helpful for inexperienced younger doctors and doctors in underdeveloped countries. It could also dramatically reduce the workload of clinicians and timely access to periodontist care for people requiring advanced periodontal treatment.