Diagnostic Reports Research Articles

Self-critical strategy adjustment based artificial intelligence method in generating diagnostic reports of respiratory diseases.

Humanity faces many health challenges, among which respiratory diseases are one of the leading causes of human death. Existing AI-driven pre-diagnosis approaches can enhance the efficiency of diagnosis but still face challenges. For example, single-modal data suffer from information redundancy or loss, difficulty in learning relationships between features, and revealing the obscure characteristics of complex diseases. Therefore, it is critical to explore a method that can assist clinicians in detecting lesions early and in pre-diagnosing corresponding diseases. This paper introduces a novel network structure, SCSCS-Net, which can effectively extract image features from chest X-ray images and generate medical image descriptions, assist clinicians in analyzing patients' medical imaging information, deeply explore potential disease characteristics, and assist in making pre-diagnostic decisions. The SCSCS-Net consists of a reinforced cross-modal feature representation model (RCMFR) and a self-critical cross-modal alignment model (SCCMA), which are responsible for learning the features interdependence between images and reports by using a multi-subspace self-attention structure and guiding the model in learning report generation strategies to improve the professionalism and consistency of medical terms in generated reports, respectively. We further compare our model with some advanced models on the same dataset, and the results demonstrate that our method achieves better performance. Finally, the CE and NLG metrics further confirm that the proposed method acquires the ability to generate high-quality medical reports with higher clinical consistency in generating medical reports. Our novel method has the potential to improve the early detection and pre-diagnosis of respiratory diseases. The model proposed in this paper allows to narrow the gap between Artificial intelligence technology and clinical medical diagnosis and provides the possibility for in-depth integration.

Cross-modal retrieval of chest X-ray images and diagnostic reports based on report entity graph and dual attention

Cross-modal retrieval of chest X-ray images and diagnostic reports based on report entity graph and dual attention

Automated Chest X-Ray Diagnosis Report Generation with Cross-Attention Mechanism

In the medical field, it is extremely important to use deep learning technology to automatically generate diagnostic reports for chest X-ray images. This technology provides an effective solution to the challenges faced by the medical field in processing large numbers of chest X-ray images. Especially during large-scale outbreaks of epidemics such as the new COVID-19, rapid and accurate screening and diagnosis of cases become important tasks. This study uses deep learning technology to automatically generate diagnostic reports for chest X-ray images, which significantly reduces the workload of doctors, reduces the risk of misdiagnosis and missed diagnosis, and provides technical support for improving public health emergency response capabilities. In this study, we propose an innovative network architecture to address the limitations of traditional image description networks in generating chest X-ray diagnostic reports, especially the large area deviation between abnormal and normal areas, and the lack of effective alignment of the two modalities of image and text. The convolutional block attention module (CBAM) is adopted to effectively alleviate the data bias problem through a sophisticated feature attention mechanism and improve the model’s ability to recognize abnormal image areas. The cross-attention mechanism is adopted to optimize the alignment process between images and texts, ensuring the accuracy and reliability of the diagnosis report.

From Text to Translation: Using Language Models to Prioritize Variants for Clinical Review.

Despite rapid advances in genomic sequencing, most rare genetic variants remain insufficiently characterized for clinical use, limiting the potential of personalized medicine. When classifying whether a variant is pathogenic, clinical labs adhere to diagnostic guidelines that comprehensively evaluate many forms of evidence including case data, computational predictions, and functional screening. While a substantial amount of clinical evidence has been developed for these variants, the majority cannot be definitively classified as 'pathogenic' or 'benign', and thus persist as 'Variants of Uncertain Significance' (VUS). We processed over 2.4 million plaintext variant summaries from ClinVar, employing sentence-level classification to remove content that does not contain evidence and removing uninformative summaries. We developed ClinVar-BERT to discern clinical evidence within these summaries by fine-tuning a BioBERT-based model with labeled records. When validated classifications from this model against orthogonal functional screening data, ClinVar-BERT significantly separated estimates of functional impact in clinically actionable genes, including BRCA1 (p = 1.90 × 10 - 20 ), TP53 (p = 1.14 × 10 - 47 ), and PTEN (p = 3.82 × 10 - 7 ). Additionally, ClinVar-BERT achieved an AUROC of 0.927 in classifying ClinVar VUS against this functional screening data. This suggests that ClinVar-BERT is capable of discerning evidence from diagnostic reports and can be used to prioritize variants for re-assessment by diagnostic labs and expert curation panels.

Introducing a Software-Based Template for Standardized Structured Reports of Urinary Cytology and Its Impact on Turnaround Time in a Tertiary Center.

Standardized pathology reports enhance completeness and readability, contributing to the overall improvement in the management of patients. The standardization of urine cytology reporting has gained widespread acceptance with the use of the Paris system (TPS) for reporting urinary cytology, primarily focused on detecting high-grade urothelial carcinoma (HGUC). The next phase at the Institute of Pathology, Medical Faculty, University of Ljubljana, was the implementation of TPS, including standardized additional findings on non-neoplastic changes, into a software-based standardized structured reporting (SBSSR) system. The implementation of SBSSR for urinary cytology at the Institute of Pathology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia, began in March 2023. Before daily usage, the SBSSR template was tested and approved by all cytopathology team members. An informative briefing for clinicians was organized to facilitate inter-specialty communication. To assess the impact of the SBSSR system on reporting efficiency, the turnaround time was tracked from the receipt of the cytology sample in the lab to the time of digital signature on its diagnostic report during two consecutive 11-month periods (04/01/2022-03/01/2023 and 04/01/2023-03/01/2024). All team members easily adopted the new SBSSR for urinary cytology. Reports are generated, costs are calculated, and they are digitally signed in a single step. The adoption of SBSSR for urinary cytology led to the replacement of dictation and administrative tasks. The average turnaround time for urine cytology has been reduced from 1,8 days (based on 2843 samples) to 1,5 days (based on 3438 samples). The introduction of SBSSR for urinary cytology increased the quality of patient care with a shorter time to diagnosis and a higher level of report clarity.

Clinical Presentation of Patients with Hepatocellular Carcinoma in Tertiary level Hospital of Bangladesh

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, accounting for 75% - 86% of all cases. Men are affected approximately two to three times more than women. It is considered as the fifth most common cancer in men and the ninth in women. The prognosis for liver cancer is very poor which has made it globally the second most common cause of death from cancer. The highest liver cancer rates are found in East and Southeast Asia and in Middle and Western Africa. Western world is complaining of rising HCC prevalence as a result of migration from HBV-endemic regions, hepatitis C virus (HCV) infection, alcoholic cirrhosis, and non-alcoholic steatohepatitis associated with the obesity epidemics. The accurate diagnosis of HCC dependent on clinical presentation, biochemical, imaging, cytological and/ or histopathological examination. This was a descriptive study of hepatocellular carcinoma patients those attended Dhaka Medical College Hospital and Shaheed Suhrawardy Medical College Hospital, Dhaka, Bangladesh from September 2006 to march 2008. Thirty (30) consecutive biopsy proven hepatocellular carcinoma patients were enrolled in this study. This study collected data by reviewing Medical records (prescriptions and diagnostic reports) and interviewing patients for various present symptoms/ signs, past complaints (symptoms/ signs) they experienced in different duration and for confirmation of HCC. Among the symptoms almost all 29(96.66%) of the patients had experienced loss of appetite and most of the patients were experienced generalized weakness 28(93.33%) followed by weight loss 24(80%). More than two-third of the patients had complained abdominal pain 22(73.33%), abdominal fullness 21(70%), abdominal mass 20(66.66%) and nausea and vomiting 20(66.66%). Various signs of HCC were found in examination during interview and from their past experience/ reviewing medical record of illness; where most of them 27(90%) had hepatomegaly and half of them 15(50%) had splenomegaly and ascites. Among the others sign of HCC, one-third (33.33%) of them had yellow discolouration of eyes, yellowish urine, jaundice and fever. Several signs were also observed like Leg swelling 9(30%), cough 8(26.66%), alteration of bowel habit 6(20%). Signs like blood vomiting 1(3.33%) and leg oedema 4(13.33%) were also observed. Information regarding clinical presentation from this study may be utilized as indicator for referral, case definition in research works and probable diagnosis of HCC. This clinical presentation may be a strong tool for symptomatic surveillance of HCC and prevention of HCC including chronic hepatitis B and chronic hepatitis C virus infection. Bangladesh Med J. 2023 Sept; 52(3): 8-13

PhraseAug: An Augmented Medical Report Generation Model with Phrasebook.

Medical report generation is a valuable and challenging task, which automatically generates accurate and fluent diagnostic reports for medical images, reducing workload of radiologists and improving efficiency of disease diagnosis. Fine-grained alignment of medical images and reports facilitates the exploration of close correlations between images and texts, which is crucial for cross-modal generation. However, visual and linguistic biases caused by radiologists' writing styles make cross-modal image-text alignment difficult. To alleviate visual-linguistic bias, this paper discretizes medical reports and introduces an intermediate modality, i.e. phrasebook, consisting of key noun phrases. As discretized representation of medical reports, phrasebook contains both disease-related medical terms, and synonymous phrases representing different writing styles which can identify synonymous sentences, thereby promoting fine-grained alignment between images and reports. In this paper, an augmented two-stage medical report generation model with phrasebook (PhraseAug) is developed, which combines medical images, clinical histories and writing styles to generate diagnostic reports. In the first stage, phrasebook is used to extract semantically relevant important features and predict key phrases contained in the report. In the second stage, medical reports are generated according to the predicted key phrases which contain synonymous phrases, promoting our model to adapt to different writing styles and generating diverse medical reports. Experimental results on two public datasets, IU-Xray and MIMIC-CXR, demonstrate that our proposed PhraseAug outperforms state-of-the-art baselines.

Improving the Diagnostic Performance and Breast Imaging Reporting and Data System Category Agreement of Less Experienced Radiologists by Utilizing Computer-Aided Diagnosis Software for Breast Ultrasound

Abstract This study aimed to assess the effectiveness of intelligence-based computer-aided diagnosis (CAD) software in ultrasound (US) and its potential to improve the diagnostic performance of less experienced radiologists, as well as the agreement on Breast Imaging Reporting and Data System (BI-RADS) categories with the experienced radiologist. Images of 385 breast lesions in 351 female taken from January 2019 to December 2020 were included. Two less experienced radiologists independently reviewed US images with and without CAD assistance, recording final assessments using the BI-RADS category. The diagnostic performance of CAD and radiologists were calculated and compared. Kappa statistics were used to determine agreement between the experienced radiologist and the less experienced radiologists, based on BI-RADS category before and after using CAD software. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of CAD software were 95.5%, 71.5%, 81.3%, 69.8%, and 95.9%, respectively, and those were improved in junior radiologist and intermediate-level radiologist after the addition of CAD. Additionally, with the assistance of CAD, the area under the curve was improved for both the junior radiologist and radiologist (0.704 vs 0.847 and 0.876 vs 0.900, P = 0.009, 0.005), although it remained lower than the senior radiologist. The agreement of BI-RADS category between the less experienced and the experienced radiologists showed a significant improvement (P = 0.04, 0.000). The CAD on US could improve less experienced radiologists' diagnostic performance and agreement on BI-RADS categories, making it an effective decision-making tool in clinical practice.

Generative Artificial Intellegence (AI) in Pathology and Medicine: A Deeper Dive.

This review article builds upon the introductory piece in our seven-part series, delving deeper into the transformative potential of generative artificial intelligence (Gen AI) in pathology and medicine. The article explores the applications of Gen AI models in pathology and medicine, including the use of custom chatbots for diagnostic report generation, synthetic image synthesis for training new models, dataset augmentation, hypothetical scenario generation for educational purposes, and the use of multimodal along with multi-agent models. This article also provides an overview of the common categories within generative AI models, discussing open-source and closed-source models, as well as specific examples of popular models such as GPT-4, Llama, Mistral, DALL-E, Stable Diffusion and their associated frameworks (e.g. transformers, GANs, diffusion-based neural networks), along with their limitations and challenges, especially within the medical domain. We also review common libraries, and tools that are currently deemed necessary to build and integrate such models. Finally, we look to the future, discussing the potential impact of generative AI on healthcare, including benefits, challenges, and concerns related to privacy, bias, ethics, API costs, and security measures.

Deep learning-assisted non-invasive pediatric tic disorder diagnosis using EEG features extracted by residual neural networks

Early diagnosis of pediatric tic disorders (TD) is crucial for effective therapeutic intervention and management, which can significantly improve neurological development and psychological well-being from childhood through adulthood. However, current pediatric TD diagnostic methodologies suffer from low specificity and sensitivity, as they rely primarily on the subjective expertise of clinicians. Herein, we demonstrated a non-invasive approach for deep learning-assisted diagnosis of pediatric TD. A residual neural network model was developed to predict TD using electroencephalogram (EEG) signals. The optimized model analyzed preprocessed EEG data to generate diagnostic reports indicating the probability of TD occurrence, thus providing deep learning-assisted support for clinical decisions. The clinical features of EEG signals in pediatric TD are elucidated through extensive analysis. Predictive accuracy of EEG decreases over time, with short-term EEG indicating that right hemisphere EEG activity is a predominant clinical feature of TD. A computer-based application was developed and implemented to calculate the probability of TD based on individual EEG patterns, thereby assisting clinicians with diagnostic decision-making in real-world scenarios. This work not only proposes a non-invasive and accurate approach for TD diagnosis but also contributes to the early intervention and long-term management of neurological and psychological health in affected individuals.

An Organ-aware Diagnosis Framework for Radiology Report Generation.

Radiology report generation (RRG) is crucial to save the valuable time of radiologists in drafting the report, therefore increasing their work efficiency. Compared to typical methods that directly transfer image captioning technologies to RRG, our approach incorporates organ-wise priors into the report generation. Specifically, in this paper, we propose Organ-aware Diagnosis (OaD) to generate diagnostic reports containing descriptions of each physiological organ. During training, we first develop a task distillation (TD) module to extract organ-level descriptions from reports. We then introduce an organ-aware report generation module that, for one thing, provides a specific description for each organ, and for another, simulates clinical situations to provide short descriptions for normal cases. Furthermore, we design an auto-balance mask loss to ensure balanced training for normal/abnormal descriptions and various organs simultaneously. Being intuitively reasonable and practically simple, our OaD outperforms SOTA alternatives by large margins on commonly used IU-Xray and MIMIC-CXR datasets, as evidenced by a 3.4% BLEU-1 improvement on MIMIC-CXR and 2.0% BLEU-2 improvement on IU-Xray.

Prospective Evaluation of Artificial Intelligence Triage of Intracranial Hemorrhage on Noncontrast Head CT Examinations.

BACKGROUND. Retrospective studies evaluating artificial intelligence (AI) algorithms for intracranial hemorrhage (ICH) detection on noncontrast CT (NCCT) have shown promising results but lack prospective validation. OBJECTIVE. The purpose of this article was to evaluate the impact on radiologists' real-world aggregate performance for ICH detection and report turnaround times for ICH-positive examinations of a radiology department's implementation of an AI triage and notification system for ICH detection on head NCCT examinations. METHODS. This prospective single-center study included adult patients who underwent head NCCT examinations from May 12, 2021, to June 30, 2021 (phase 1), or from September 30, 2021, to December 4, 2021 (phase 2). Before phase 1, the radiology department implemented a commercial AI triage system for ICH detection that processed head NCCT examinations and notified radiologists of positive results through a widget with a floating pop-up display. Examinations were interpreted by neuroradiologists or emergency radiologists, who evaluated examinations without and with AI assistance in phases 1 and 2, respectively. A panel of radiologists conducted a review process for all examinations with discordance between the radiology report and AI and a subset of remaining examinations to establish the reference standard. Diagnostic performance and report turnaround times were compared using the Pearson chi-square test and Wilcoxon rank sum test, respectively. Bonferroni correction was used to account for five diagnostic performance metrics (adjusted significance threshold, .01 [α = .05/5]). RESULTS. A total of 9954 examinations from 7371 patients (mean age, 54.8 ± 19.8 [SD] years; 3773 women, 3598 men) were included. In phases 1 and 2, 19.8% (735/3716) and 21.9% (1368/6238) of examinations, respectively, were positive for ICH (p = .01). Radiologists without versus with AI showed no significant difference in accuracy (99.5% vs 99.2%), sensitivity (98.6% vs 98.9%), PPV (99.0% vs 97.5%), or NPV (99.7% vs 99.7%) (all p > .01); specificity was higher for radiologists without than with AI (99.8% vs 99.3%, respectively, p = .004). Mean report turnaround time for ICH-positive examinations was 147.1 minutes without AI versus 149.9 minutes with AI (p = .11). CONCLUSION. An AI triage system for ICH detection did not improve radiologists' diagnostic performance or report turnaround times. CLINICAL IMPACT. This large prospective real-world study does not support use of AI assistance for ICH detection.

Deep learning model for the automated detection and classification of central canal and neural foraminal stenosis upon cervical spine magnetic resonance imaging.

A deep learning (DL) model that can automatically detect and classify cervical canal and neural foraminal stenosis using cervical spine magnetic resonance imaging (MRI) can improve diagnostic accuracy and efficiency. A method comprising region-of-interest (ROI) detection and cascade prediction was formulated for diagnosing cervical spinal stenosis based on a DL model. First, three part-specific convolutional neural networks were employed to detect the ROIs in different parts of the cervical MR images. Cascade prediction of the stenosis categories was subsequently performed to record the stenosis level and position on each patient slice. Finally, the results were combined to obtain a patient-level diagnostic report. Performance was evaluated based on the accuracy (ACC), area under the curve (AUC), sensitivity, specificity, F1 Score, diagnosis time of the DL model, and recall rate for ROI detection localization. The average recall rate of the ROI localization was 89.3% (neural foramen) and 99.7% (central canal) under the five-fold cross-validation of the DL model. In the dichotomous classification (normal or mild vs. moderate or severe), the ACC and AUC of the DL model were comparable to those of the radiologists, and the F1 score (84.8%) of the DL model was slightly higher than that of the radiologists (83.8%) for the central canal. Diagnosing whether the central canal or neural foramen of a slice is narrowed in the cervical MRI scan required an average of 15 and 0.098s for the radiologists and DL model, respectively. The DL model demonstrated comparable performance with subspecialist radiologists for the detection and classification of central canal and neural foraminal stenosis on cervical spine MRI. Moreover, the DL model demonstrated significant timesaving ability.

Auto-Rad: End-to-End Report Generation from Lumber Spine MRI Using Vision-Language Model.

Background: Lumbar spinal stenosis (LSS) is a major cause of chronic lower back and leg pain, and is traditionally diagnosed through labor-intensive analysis of magnetic resonance imaging (MRI) scans by radiologists. This study aims to streamline the diagnostic process by developing an automated radiology report generation (ARRG) system using a vision-language (VL) model. Methods: We utilized a Generative Image-to-Text (GIT) model, originally designed for visual question answering (VQA) and image captioning. The model was fine-tuned to generate diagnostic reports directly from lumbar spine MRI scans using a modest set of annotated data. Additionally, GPT-4 was used to convert semistructured text into coherent paragraphs for better comprehension by the GIT model. Results: The model effectively generated semantically accurate and grammatically coherent reports. The performance was evaluated using METEOR (0.37), BERTScore (0.886), and ROUGE-L (0.3), indicating its potential to produce clinically relevant content. Conclusions: This study highlights the feasibility of using vision-language models to automate report generation from medical imaging, potentially reducing the diagnostic workload for radiologists.

The impact of different radiology report formats on patient information processing: a systematic review.

Since radiology reports are primarily written for health professionals, patients may experience difficulties understanding jargon and terminology used, leading to anxiety and confusion. This review evaluates the impact of different radiology report formats on outcomes related to patient information processing, including perception, decision (behavioral intention), action (actual health behavior), and memory (recall of information). PubMed, Web of Science, EMBASE, and PsycInfo were searched for relevant qualitative and quantitative articles describing or comparing ways of presenting diagnostic radiology reports to patients. Two reviewers independently screened for relevant articles and extracted data from those included. The quality of articles was assessed using the Mixed Methods Appraisal Tool. Eighteen studies, two qualitative and sixteen quantitative, were included. Sixteen studies compared multiple presentation formats, most frequently traditional unmodified reports (n = 15), or reports with anatomic illustrations (n = 8), lay summaries (n = 6) or glossaries (n = 6). Glossaries, illustrations, lay summaries, lay reports or lay conclusions all significantly improved participants' cognitive perception and perception of communication of radiology reports, compared to traditional reports. Furthermore, these formats increased affective perception (e.g., reduced anxiety and worry), although only significant for lay reports and conclusions. Modifying traditional radiology reports with glossaries, illustrations or lay language enhances patient information processing. Question Identifying the impact of different radiology report formats on outcomes related to patient information processing to enhance patient engagement through online access to radiology reports. Findings Lay language summaries, glossaries with patient-oriented definitions, and anatomic illustrations increase patients' satisfaction with and understanding of their radiology reports. Clinical relevance To increase patients' satisfaction, perceived usefulness and understanding with radiology reports, the use of lay language summaries, glossaries with patient-oriented definitions, and anatomic illustrations is recommended. These modifications decrease patients' unnecessary insecurity, confusion, anxiety and physician consultations after viewing reports.

Oncointerpreter.ai enables interactive, personalized summarization of cancer diagnostics data.

Cancer diagnosis comes as a shock to many patients, and many of them feel unprepared to handle the complexity of the life-changing event, understand technicalities of the diagnostic reports, and fully engage with the clinical team regarding the personalized clinical decision-making. We develop Oncointerpreter.ai an interactive resource to offer personalized summarization of clinical cancer genomic and pathological data, and frame questions or address queries about therapeutic opportunities in near-real time via a graphical interface. It is built on the Mistral-7B and Llama-2 7B large language models trained on a local database trained using a large, curated corpus. We showcase its utility with case studies, where Oncointerpreter.ai extracted key clinical and molecular attributes from deidentified pathology and clinical genomics reports, summarized their contextual significance and answered queries on pertinent treatment options. Oncointerpreter also provided personalized summary of currently active clinical trials that match the patients' disease status, their selection criteria, and geographic locations. Benchmarking and comparative assessment indicated that the model responses were generally consistent, and hallucination, ie, factually incorrect or nonsensical response was rare; treatment- and outcome related queries led to context-aware responses, and response time correlated with verbosity. The choice of model and domain-specific training also affected the response quality. Oncointerpreter.ai can aid the existing clinical care with interactive, individualized summarization of diagnostics data to promote informed dialogs with the patients with new cancer diagnoses. https://github.com/Siris2314/Oncointerpreter.

Online public response to emergency department diagnostic error report: A qualitative study.

The 2022 study on diagnostic error in the emergency department (ED) published by the Agency for Healthcare Research and Quality (AHRQ) reported that one in every 18 ED patients is misdiagnosed. The report was methodologically critiqued by emergency physicians and researchers. However, little is known about public perception of error in the ED. We sought to characterize public response to AHRQ's publication. A search was conducted for online news articles published December 2022 reporting the AHRQ study and containing "public comment" sections. Verbatim comments and relevant characteristics were collected. Three coders completed content analysis and resolved any differences. Descriptive statistics and themes are reported. Fifteen online articles were reviewed; three had public comment sections (New York Times, DailyMail, and Boston Globe). There were 553 unique user comments; 293 were original comments (53%) and 260 were replies to comments (47%). The 260 replies were in response to 113 original comments, with the remaining original comments having 0 replies (n = 180). Of the 202 commenters who identified a personal role in a health care encounter, 70 (35%) identified as patients and 68 (34%) identified as physicians. Comments centered on seven major themes: (1) negative personal experiences, (2) reframing study conclusions, (3) sense of decline in training standards, (4) internal stressors impeding ED diagnostic accuracy, (5) external stressors impeding ED diagnostic accuracy, (6) suggested solutions, and (7) role of the ED in diagnosis. The news coverage of AHRQ's report provided individuals a platform to share their perspectives. Many comments reflected a nuanced understanding of the role of emergency care and the stressors of the ED environment. Despite questions about the report's accuracy, there were many individuals who shared personal negative experiences suggesting that the public may feel directly impacted by error in the ED.

Artificial Intelligence in Audiology: A Scoping Review of Current Applications and Future Directions.

The integration of artificial intelligence (AI) into medical disciplines is rapidly transforming healthcare delivery, with audiology being no exception. By synthesizing the existing literature, this review seeks to inform clinicians, researchers, and policymakers about the potential and challenges of integrating AI into audiological practice. The PubMed, Cochrane, and Google Scholar databases were searched for articles published in English from 1990 to 2024 with the following query: "(audiology) AND ("artificial intelligence" OR "machine learning" OR "deep learning")". The PRISMA extension for scoping reviews (PRISMA-ScR) was followed. The database research yielded 1359 results, and the selection process led to the inclusion of 104 manuscripts. The integration of AI in audiology has evolved significantly over the succeeding decades, with 87.5% of manuscripts published in the last 4 years. Most types of AI were consistently used for specific purposes, such as logistic regression and other statistical machine learning tools (e.g., support vector machine, multilayer perceptron, random forest, deep belief network, decision tree, k-nearest neighbor, or LASSO) for automated audiometry and clinical predictions; convolutional neural networks for radiological image analysis; and large language models for automatic generation of diagnostic reports. Despite the advances in AI technologies, different ethical and professional challenges are still present, underscoring the need for larger, more diverse data collection and bioethics studies in the field of audiology.

Bearing Fault Detection in Conveyor Belt Drums Using Machine Learning

In recent years, the application of machine learning techniques in condition monitoring has significantly advanced the precision and efficiency of fault detection processes. In particular, detecting bearing faults in conveyor belt drums is critical in the mining industry for maintaining operational reliability and productivity. This paper presents a case study using vibration signals and diagnostic reports provided by the company Dynamox. After meticulous data cleaning, preprocessing, and feature extraction employing advanced signal processing techniques and statistical features, several machine learning models were trained, optimized and evaluated, with the best models providing very promising results.

CerviFusionNet: A multi-modal, hybrid CNN-transformer-GRU model for enhanced cervical lesion multi-classification

Cervical lesions pose a significant threat to women's health worldwide. Colposcopy is essential for screening and treating cervical lesions, but its effectiveness depends on the doctor's experience. Artificial intelligence-based solutions via colposcopy images have shown great potential in cervical lesions screening. However, some challenges still need to be addressed, such as low algorithm performance and lack of high-quality multi-modal datasets. Here, we established a multi-modal colposcopy dataset of 2,273 HPV+ patients, comprising original colposcopy images, acetic acid reactions at 60s and 120s, iodine staining, diagnostic reports, and pathological results. Utilizing this dataset, we developed CerviFusionNet, a hybrid architecture that merges convolutional neural networks and vision transformers to learn robust representations. We designed a temporal module to capture dynamic changes in acetic acid sequences, which can boost the model performance without sacrificing inference speed. Compared with several existing methods, CerviFusionNet demonstrated excellent accuracy and efficiency.