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Visual observation and dermoscopic analysis are the most common methods of diagnosing skin cancer. In advanced stages, melanomas spread faster and are less responsive to treatment. Because different lesions in the skin appear similar to one another and sometimes errors in identification occur, the accuracy of diagnosis will decrease significantly when the amount of received images is large. However, the proposed methods for estimating skin lesions and their separation from melanoma have uncertainties and are not generalizable. This paper proposes an optimal decision tree (DT)-based approach, including fuzzy-ID3-pValue and Bayes learning algorithms, which overcomes these challenges. When classifying images, DTs employ a multi-stage procedure to partition the feature space, which enhances their ease of use, precision, and speed. Inference engines are used in fuzzy logic to derive logical deductions about knowledge, which facilitates learning DT and Bayesian learning. Taking advantage of the DT dependency structure, we present a novel fuzzy DT for extracting precise and collaborative fuzzy rules. Furthermore, to emphasize the cohesive nature of the laws, a weighted method is employed. Besides, the inference engine system is constructed through deductive and inductive inference engines. The proposed method is verified using the ISIC-2019 dataset as well as the PH2 images, both of which contain dermoscopic images of multiple lesions. The proposed method provides efficient results with 96% and 88% accuracy for ISIC-2019 and PH2 data, respectively. [ABSTRACT FROM AUTHOR]

Purpose: The implementation of artificial intelligence (AI) in health care is gaining popularity. Many publications describe powerful AI-enabled algorithms. Yet there's only scarce evidence for measurable value in terms of patient outcomes, clinical decision-making or socio-economic impact. Our aim was to investigate the significance of AI in the emergency treatment of wrist trauma patients. Method: Two groups of physicians were confronted with twenty realistic cases of wrist trauma patients and had to find the correct diagnosis and provide a treatment recommendation. One group was assisted by an AI-enabled application which detects and localizes distal radius fractures (DRF) with near-to-perfect precision while the other group had no help. Primary outcome measurement was diagnostic accuracy. Secondary outcome measurements were required time, number of added CT scans and senior consultations, correctness of the treatment, subjective and objective stress levels. Results: The AI-supported group was able to make a diagnosis without support (no additional CT, no senior consultation) in significantly more of the cases than the control group (75% vs. 52%, p = 0.003). The AI-enhanced group detected DRF with superior sensitivity (1.00 vs. 0.96, p = 0.06) and specificity (0.99 vs. 0.93, p = 0.17), used significantly less additional CT scans to reach the correct diagnosis (14% vs. 28%, p = 0.02) and was subjectively significantly less stressed (p = 0.05). Conclusion: The results indicate that physicians can diagnose wrist trauma more accurately and faster when aided by an AI-tool that lessens the need for extra diagnostic procedures. The AI-tool also seems to lower physicians' stress levels while examining cases. We anticipate that these benefits will be amplified in larger studies as skepticism towards the new technology diminishes. [ABSTRACT FROM AUTHOR]

Breast carcinoma or breast cancer is the most common type of cancer in women. It is very dangerous, and early detection is important for proper diagnosis and reduction of mortality rate. Cancer starts in a benign state, and if there is no proper treatment, it becomes malignant. Computer-aided diagnosis (CAD) techniques can be used for early detection of cancers. These techniques can significantly and effectively help in the diagnosis of cancer. Deep learning is the most popular technique used for accurate data analysis. It is also a powerful tool for categorizing histopathological images of breast cancer and analyzing breast cell shapes and densities. This paper proposes a hybrid EfficientNet with an SVM to categorize breast mass density with consideration of tumor types. (i.e., malignant or benign). In this hybrid, EfficientNet, the mammography images are applied to EfficientNet, and then the classification is carried out using a Support Vector Machine (SVM). The proposed model achieved accuracy of 94.48%, precision of 94.79%, specificity of 99.21%, Precision of 94.79%, FPR of 0.79%, score of 94.45%, MCC of 93.78%, and Kappa of 74.76%. The model outperformed state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Microaneurysms (MAs) are small, circular, red lesions typically observed in the initial stage of Diabetic Retinopathy (DR). DR is a degenerative eye condition resulting from diabetes mellitus that can lead to vision loss if not detected and treated early. Since MAs are one of the first visible signs of DR, detecting them can lead to early diagnosis and treatment of DR and potentially prevent severe vision loss. In this survey, we conduct a thorough review and analyze the published literature on automated detection and segmentation of MAs, with the aim of bolstering ophthalmologists capabilities in the early screening and management of DR. Section 2 provides an overview of publicly available datasets that include image-level and pixel-level annotations of MAs. Benchmarking plays a vital role in the precise assessment of the efficacy of early DR detection systems. Section 3 presents common metrics used to benchmark the performance of MA recognition technologies. We categorize MA recognition methodologies into two principal groups: those based on image processing and those leveraging deep learning techniques. Sections 4 and 5 meticulously enumerate the cutting-edge techniques for MA detection and segmentation. Our discussion also delves into recent advancements and explores prospective future directions in this field. In conclusion, the evolution of machine learning and computer vision has enabled automated MA recognition methods to exhibit considerable potential, marking a significant stride forward in the fight against DR-related vision loss. [ABSTRACT FROM AUTHOR]

Background: Breast cancer is one of the leading diseases worldwide. According to estimates by the National Breast Cancer Foundation, over 42,000 women are expected to die from this disease in 2024. Objective: The prognosis of breast cancer depends on the early detection of breast micronodules and the ability to distinguish benign from malignant lesions. Ultrasonography is a crucial radiological imaging technique for diagnosing the illness because it allows for biopsy and lesion characterization. The user's level of experience and knowledge is vital since ultrasonographic diagnosis relies on the practitioner's expertise. Furthermore, computer-aided technologies significantly contribute by potentially reducing the workload of radiologists and enhancing their expertise, especially when combined with a large patient volume in a hospital setting. Method: This work describes the development of a hybrid CNN system for diagnosing benign and malignant breast cancer lesions. The models InceptionV3 and MobileNetV2 serve as the foundation for the hybrid framework. Features from these models are extracted and concatenated individually, resulting in a larger feature set. Finally, various classifiers are applied for the classification task. Results: The model achieved the best results using the softmax classifier, with an accuracy of over 95%. Conclusion: Computer-aided diagnosis greatly assists radiologists and reduces their workload. Therefore, this research can serve as a foundation for other researchers to build clinical solutions. [ABSTRACT FROM AUTHOR]

This article explores the use of convolutional neural networks (CNNs) in the detection and diagnosis of Attention Deficit Hyperactivity Disorder (ADHD). CNNs, which are adept at image recognition, can analyze brain imaging data to automatically identify relevant brain structures and abnormalities associated with ADHD. The integration of deep learning technology into ADHD diagnosis represents a significant advancement, allowing for more objective and data-driven approaches. The article also highlights specific studies that demonstrate the effectiveness of CNNs in diagnosing ADHD using EEG signals and MRI scans. The authors suggest that future research should focus on integrating different types of data and developing personalized treatment plans while considering ethical considerations and explainable AI models. Overall, CNNs are seen as a promising tool in improving ADHD care and patient outcomes. [Extracted from the article]

Background: Data-driven digital learning could improve the diagnostic performance of novice students for thyroid nodules. Objective: To evaluate the efficacy of digital self-learning and artificial intelligence-based computer-assisted diagnosis (AI-CAD) for inexperienced readers to diagnose thyroid nodules. Methods: Between February and August 2023, a total of 26 readers (less than 1 year of experience in thyroid US from various departments) from 6 hospitals participated in this study. Readers completed an online learning session comprising 3,000 thyroid nodules annotated as benign or malignant independently. They were asked to assess a test set consisting of 120 thyroid nodules with known surgical pathology before and after a learning session. Then, they referred to AI-CAD and made their final decisions on the thyroid nodules. Diagnostic performances before and after self-training and with AI-CAD assistance were evaluated and compared between radiology residents and readers from different specialties. Results: AUC (area under the receiver operating characteristic curve) improved after the self-learning session, and it improved further after radiologists referred to AI-CAD (0.679 vs 0.713 vs 0.758, p<0.05). Although the 18 radiology residents showed improved AUC (0.7 to 0.743, p=0.016) and accuracy (69.9% to 74.2%, p=0.013) after self-learning, the readers from other departments did not. With AI-CAD assistance, sensitivity (radiology 70.3% to 74.9%, others 67.9% to 82.3%, all p<0.05) and accuracy (radiology 74.2% to 77.1%, others 64.4% to 72.8%, all p <0.05) improved in all readers. Conclusion: While AI-CAD assistance helps improve the diagnostic performance of all inexperienced readers for thyroid nodules, self-learning was only effective for radiology residents with more background knowledge of ultrasonography. [ABSTRACT FROM AUTHOR]

This paper focuses on how regulatory bodies respond to artificial intelligence (AI)-enabled medical devices. To achieve this, we present a comparative overview of the United States (USA), European Union (EU), and China. Our search in the governmental database identified 59 AI medical devices approved in China as of July 2023. In comparison to the rules-based regulatory approach in China, the approaches in the USA and EU are more standards-oriented. [ABSTRACT FROM AUTHOR]

Even though accurate detection of dangerous malignancies from mammogram images is mostly dependent on radiologists' experience, specialists occasionally differ in their assessments. Computer-aided diagnosis provides a better solution for image diagnosis that can help experts make more reliable decisions. In medical applications for diagnosing cancerous growths from mammogram images, computerized and accurate classification of breast cancer mammogram images is critical. The deep learning approach has been widely applied in medical image processing and has had considerable success in biological image classification. The Convolutional Neural Network (CNN), Inception, and EfficientNet are proposed in this paper. The proposed models attain better performance compared to the conventional CNN. The models are used to automatically classify breast cancer mammogram images from Kaggle into benign and malignant. Simulation results demonstrated that EfficientNet, with an accuracy between 97.13 and 99.27%, and overall accuracy of 98.29%, perform better than the other models in this paper. [ABSTRACT FROM AUTHOR]

Recently, intelligent analysis of lung nodules with the assistant of computer aided diagnosis (CAD) techniques can improve the accuracy rate of lung cancer diagnosis. However, existing CAD systems and pulmonary datasets mainly focus on Computed Tomography (CT) images from one single period, while ignoring the cross spatio-temporal features associated with the progression of nodules contained in imaging data from various captured periods of lung cancer. If the evolution patterns of nodules across various periods in the patients' CT sequences can be explored, it will play a crucial role in guiding the precise screening identification of lung cancer. Therefore, a cross spatio-temporal lung nodule dataset with pathological information for nodule identification and diagnosis is constructed, which contains 317 CT sequences and 2,295 annotated nodules from 109 patients. This comprehensive database is intended to drive research in the field of CAD towards more practical and robust methods, and also contribute to the further exploration of precision medicine related field. To ensure patient confidentiality, we have removed sensitive information from the dataset. [ABSTRACT FROM AUTHOR]

Objectives: Optical coherence tomography (OCT) has recently been used in gynecology to detect cervical lesions in vivo and proven more effective than colposcopy in clinical trials. However, most gynecologists are unfamiliar with this new imaging technique, requiring intelligent computer-aided diagnosis approaches to help them interpret cervical OCT images efficiently. This study aims to (1) develop a clinically-usable deep learning (DL)-based classification model of 3D OCT volumes from cervical tissue and (2) validate the DL model's effectiveness in detecting high-risk cervical lesions, including high-grade squamous intraepithelial lesions and cervical cancer. Method: The proposed DL model, designed based on the convolutional neural network architecture, combines a feature pyramid network (FPN) with texture encoding and deep supervision. We extracted, represent, and fused four-scale texture features to improve classification performance on high-risk local lesions. We also designed an auxiliary classification mechanism based on deep supervision to adjust the weight of each scale in FPN adaptively, enabling low-cost training of the whole model. Results: In the binary classification task detecting positive subjects with high-risk cervical lesions, our DL model achieved an 81.55% (95% CI, 72.70–88.51%) F1-score with 82.35% (95% CI, 69.13–91.60%) sensitivity and 81.48% (95% CI, 68.57–90.75%) specificity on the Renmin dataset, outperforming five experienced medical experts. It also achieved an 84.34% (95% CI, 74.71–91.39%) F1-score with 87.50% (95% CI, 73.20–95.81%) sensitivity and 90.59% (95% CI, 82.29–95.85%) specificity on the Huaxi dataset, comparable to the overall level of the best investigator. Moreover, our DL model provides visual diagnostic evidence of histomorphological and texture features learned in OCT images to assist gynecologists in making clinical decisions quickly. Conclusions: Our DL model holds great promise to be used in cervical lesion screening with OCT efficiently and effectively. [ABSTRACT FROM AUTHOR]

Background: Accurate assessment of basal bone width is essential for distinguishing individuals with normal occlusion from patients with maxillary transverse deficiency who may require maxillary expansion. Herein, we evaluated the effectiveness of a deep learning (DL) model in measuring landmarks of basal bone width and assessed the consistency of automated measurements compared to manual measurements. Methods: Based on the U-Net algorithm, a coarse-to-fine DL model was developed and trained using 80 cone-beam computed tomography (CBCT) images. The model's prediction capabilities were validated on 10 CBCT scans and tested on an additional 34. To evaluate the performance of the DL model, its measurements were compared with those taken manually by one junior orthodontist using the concordance correlation coefficient (CCC). Results: It took approximately 1.5 s for the DL model to perform the measurement task in only CBCT images. This framework showed a mean radial error of 1.22 ± 1.93 mm and achieved successful detection rates of 71.34%, 81.37%, 86.77%, and 91.18% in the 2.0-, 2.5-, 3.0-, and 4.0-mm ranges, respectively. The CCCs (95% confidence interval) of the maxillary basal bone width and mandibular basal bone width distance between the DL model and manual measurement for the 34 cases were 0.96 (0.94–0.97) and 0.98 (0.97–0.99), respectively. Conclusion: The novel DL framework developed in this study improved the diagnostic accuracy of the individual assessment of maxillary width. These results emphasize the potential applicability of this framework as a computer-aided diagnostic tool in orthodontic practice. [ABSTRACT FROM AUTHOR]

Pneumonia is a serious inflammatory disease that causes lung ulcers, and it is one of the leading reasons for pediatric death in the world. Chest X-rays are perhaps the most commonly utilized modalities to recognize pneumonia. Generally, the illness could be analyzed by a specialist radiologist. But for some reason, the diagnosis may be subjective. Thus, the physicians must be guided by computer-aided diagnosis frameworks in this challenging task. In this study, we propose a combined deep learning architecture to identify pneumonia in chest radiography images. We first, use Adaptive Median Filter for images enhancement, then we employ a regularized Convolutional Neural Network for features extraction, and then we use Long Short Term Memory as a classifier. Finally, the attention mechanism is used to direct the network attention to relevant features. The suggested approach was tested on two publicly available pneumonia X-ray datasets provided by Kermany and the Radiological Society of North America. On the Kermany and RSNA datasets, the suggested technique attained accuracy rates of 99.91% and 88.86%, respectively. In the last stage of our experiments, we employed a Grad-CAM-based color visualization technique to precisely interpret the detection of pneumonia in radiological images. The results outperformed those of state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Automated diagnostic systems can enhance the accuracy and efficiency of pathological diagnoses, nuclear segmentation plays a crucial role in computer-aided diagnosis systems for histopathology. However, achieving accurate nuclear segmentation is challenging due to the complex background tissue structures and significant variations in cell morphology and size in pathological images. In this study, we have proposed a U-Net based deep learning model, called MA-Net(Multifunctional Aggregation Network), to accurately segmenting nuclei from H&E stained images. In contrast to previous studies that focused on improving a single module of the network, we applied feature fusion modules, attention gate units, and atrous spatial pyramid pooling to the encoder and decoder, skip connections, and bottleneck of U-Net, respectively, to enhance the network's performance in nuclear segmentation. The dice coefficient loss was used during model training to enhance the network's ability to segment small objects. We applied the proposed MA-Net to multiple public datasets, and comprehensive results showed that this method outperforms the original U-Net method and other state-of-the-art methods in nuclei segmentation tasks. The source code of our work can be found in https://github.com/LinaZhaoAIGroup/MA-Net. [ABSTRACT FROM AUTHOR]

Problem: Leishmaniasis is a disease caused by protozoan parasites of the genus Leishmania and has a high prevalence and impact on global health. Currently, the available drugs for its treatment have drawbacks, such as high toxicity, resistance of the parasite, and high cost. Therefore, the search for new, more effective, and safe drugs is a priority. The effectiveness of an anti-leishmanial drug is analyzed through in vitro studies in which a technician manually counts the intracellular form of the parasite (amastigote) within macrophages, which is slow, laborious, and prone to errors. Objective(s): To develop a computational system that facilitates the detection and counting of amastigotes in microscopy images obtained from in vitro studies using image processing techniques. Methodology: Segmentation of objects in the microscope image that might be Leishmania amastigotes was performed using the multilevel Otsu method on the saturation component of the hue, saturation, and intensity color model. In addition, morphological operations and the watershed transform combined with the weighted external distance transform were used to separate clustered objects. Then positive (amastigote) objects were detected (and consequently counted) using a classifier algorithm, the selection of which as well as the definition of the features to be used were also part of this research. MATLAB was used for the development of the system. Results and discussion: The results were evaluated in terms of sensitivity, precision, and the F-measure and suggested a favorable effectiveness of the proposed method. Conclusions: This system can help researchers by allowing large volumes of images of amastigotes to be counted using an automatic image analysis technique. [ABSTRACT FROM AUTHOR]

The first article discusses a rare oesophageal lesion called oesophageal submucosal gland duct adenoma (ESGDA). It is typically diagnosed in males aged 50-80 and can cause abdominal pain. Less than 20 cases have been reported, with some cases also involving other types of cancer. The article emphasizes the importance of considering ESGDA in the differential diagnosis of oesophageal submucosal lesions and highlights the need for complete endoscopic mucosal resection.The second article focuses on colonoscopy in octogenarians and older patients for colorectal cancer (CRC) detection. The study evaluates the effectiveness and safety of a contemporary bowel preparation method called 1L polyethylene glycol plus ascorbic acid (1L PEG+ASC) in this population. The results show that 1L PEG+ASC was highly effective and had a good safety profile, with over half of the patients achieving high-quality colon cleansing. [Extracted from the article]

Previous research has provided strong evidence that speech patterns can help to distinguish between people with early stage neurodegenerative disorders (ND) and healthy controls. This study examined speech patterns in responses to questions asked by an intelligent virtual agent (IVA): a talking head on a computer which asks pre-recorded questions. The study investigated whether measures of response length, speech rate and pausing in responses to questions asked by an IVA help to distinguish between healthy control participants and people diagnosed with Mild Cognitive Impairment (MCI) or Alzheimer's disease (AD). The study also considered whether those measures can further help to distinguish between people with MCI, people with AD, and healthy control participants (HC). There were 38 people with ND (31 people with MCI, 7 people with AD) and 26 HC. All interactions took place in English. People with MCI spoke fewer words compared to HC, and people with AD and people with MCI spoke for less time than HC. People with AD spoke at a slower rate than people with MCI and HC. There were significant differences across all three groups for the proportion of time spent pausing and the average pause duration: silent pauses make up the greatest proportion of responses from people with AD, who also have the longest average silent pause duration, followed by people with MCI then HC. Therefore, the study demonstrates the potential of an IVA as a method for collecting data showing patterns which can help to distinguish between diagnostic groups. [ABSTRACT FROM AUTHOR]

As artificial intelligence (AI) assisted diagnosing systems become accessible and user-friendly, evaluating how first-year medical students perceive such systems holds substantial importance in medical education. This study aimed to assess medical students' perceptions of an AI-assisted diagnostic tool known as 'Glass AI.' Data was collected from first year medical students enrolled in a 1.5-week Cell Physiology pre-clerkship unit. Students voluntarily participated in an activity that involved implementation of Glass AI to solve a clinical case. A questionnaire was designed using 3 domains: 1) immediate experience with Glass AI, 2) potential for Glass AI utilization in medical education, and 3) student deliberations of AI-assisted diagnostic systems for future healthcare environments. 73/202 (36.10%) of students completed the survey. 96% of the participants noted that Glass AI increased confidence in the diagnosis, 43% thought Glass AI lacked sufficient explanation, and 68% expressed risk concerns for the physician workforce. Students expressed future positive outlooks involving AI-assisted diagnosing systems in healthcare, provided strict regulations, are set to protect patient privacy and safety, address legal liability, remove system biases, and improve quality of patient care. In conclusion, first year medical students are aware that AI will play a role in their careers as students and future physicians. [ABSTRACT FROM AUTHOR]

Purpose: The objective of this study was to determine the normative vestibulo- ocular reflex gain output values of the computerized rotational head impulse test (crHIT) with stationary visual targets (earth bound) in healthy participants in each decade age band of life: 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, and 70+ years. Method: Seventy-seven community-dwelling participants (10-85 years of age) with normal lateral semicircular canal (SCC) functioning and no symptoms or history of vestibular dysfunction were recruited through convenience sampling and assessed with the crHIT using stationary targets. These participants were assessed using two standard protocols in a randomized order. Results: Results from 77 participants (Mage = 46 years; 43 women, 34 men) were analyzed. Pearson's correlation coefficient and simple linear regression indicated a statistically significant relationship between crHIT gain output and age (p >.05) for right gain, 1030°/s², and left gain, 1005°/s². Although a statistically significant relationship was found, the slope was minor, demonstrating that the clinical effect of age on crHIT gain output was insignificant. Furthermore, no statistically significant relationship exists between crHIT gain output and gender (p > .05). Age-dependent normative data were calculated using the 2.5th and 97.5th confidence interval (CI) percentile method. The responses of angular vestibulo-ocular reflex (aVOR) gain values for crHIT are expected to occur within the range for lower limit reference interval (RI) of 0.85-0.9 and upper limit RI of 1.11-1.18 for 1030°/s² and lower limit RI of 0.86-0.92 and upper limit RI of 1.13-1.16 for 1005°/s² .It can be expected that 90% CI of the population with normal lateral SCC function- ing will have aVOR gain values that fall within this range. Conclusion: Despite a statistically significant relationship that exists with aVOR gain output and age, the changes are minor, declining by 0.0088 units per 10 years, justifying the same normative data for all decade age bands. [ABSTRACT FROM AUTHOR]

Objective: To develop and evaluate a technique using convolutional neural networks (CNNs) for the computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. By leveraging deep learning techniques, the study might potentially lead to improved patient outcomes and clinical decision-making. Methods: This study obtained 500 lateral radiographic cervical spine x-ray images from standard open-source dataset repositories to develop a classification model using CNNs. All the images contained diagnostic information, including normal cervical radiographic images (n=250) and fracture images of the cervical spine fracture (n=250). The model would classify whether the patient had a cervical spine fracture or not. Seventy percent of the images were training data sets used for model training, and 30% were for testing. Konstanz Information Miner (KNIME)'s graphic user interface-based programming enabled class label annotation, data preprocessing, CNNs model training, and performance evaluation. Results: The performance evaluation of a model for detecting cervical spine fractures presents compelling results across various metrics. This model exhibits high sensitivity (recall) values of 0.886 for fractures and 0.957 for normal cases, indicating its proficiency in identifying true positives. Precision values of 0.954 for fractures and 0.893 for normal cases highlight the model's ability to minimize false positives. With specificity values of 0.957 for fractures and 0.886 for normal cases, the model effectively identifies true negatives. The overall accuracy of 92.14% highlights its reliability in correctly classifying cases by the area under the receiver operating characteristic curve. Conclusion: We successfully used deep learning models for computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. This approach can assist the radiologist in screening, detecting, and diagnosing cervical spine fractures. [ABSTRACT FROM AUTHOR]

Ultrasound image examination has become the preferred choice for diagnosing metabolic dysfunction-associated steatotic liver disease (MASLD) due to its non-invasive nature. Computer-aided diagnosis (CAD) technology can assist doctors in avoiding deviations in the detection and classification of MASLD. We propose a hybrid model that integrates the pre-trained VGG16 network with an attention mechanism and a stacking ensemble learning model, which is capable of multi-scale feature aggregation based on the self-attention mechanism and multi-classification model fusion (Logistic regression, random forest, support vector machine) based on stacking ensemble learning. The proposed hybrid method achieves four classifications of normal, mild, moderate, and severe fatty liver based on ultrasound images. Our proposed hybrid model reaches an accuracy of 91.34% and exhibits superior robustness against interference, which is better than traditional neural network algorithms. Experimental results show that, compared with the pre-trained VGG16 model, adding the self-attention mechanism improves the accuracy by 3.02%. Using the stacking ensemble learning model as a classifier further increases the accuracy to 91.34%, exceeding any single classifier such as LR (89.86%) and SVM (90.34%) and RF (90.73%). The proposed hybrid method can effectively improve the efficiency and accuracy of MASLD ultrasound image detection. [ABSTRACT FROM AUTHOR]

Automatic pancreas detection and cropping with high precision from medical images is an important yet challenging problem for medical image analysis and Computer-Aided Diagnosis (CAD). Factors relating to the limited availability of image data and segmentation methodology hinder this task. High variability in the location of the pancreas,which occupies a very small area of the pancreatic Computed Tomography (CT) scans,and the anatomy of organs also add to the list of issues. These challenges necessitate an urgent need for the development of localization and auto-cropping methods of the region of interest (ROI). This paper presents the results obtained by the implementation of Region-based Convolutional Neural Network (RCNN)-Crop inspired by the Region Proposal Network (RPN) and Feature Pyramid Network (FPN) to localize the pancreas by building bounding boxes and auto-crop the ROI obtained from various other organs in the pancreatic CT scans and has a Mean Average Precision (mAP) of 28.10% for the dataset provided. [ABSTRACT FROM AUTHOR]

The article discusses the importance of implementing transparency and auditing in machine learning (ML) systems used for clinical decision-making to ensure ethical and reliable use. Topics discussed include the necessity of transparency in ML system design, the role of mandatory auditing to assess system performance, and the need for enforceable regulations to complement ethical guidelines in healthcare AI tools.

Medical image datasets are essential for training models used in computer-aided diagnosis, treatment planning, and medical research. However, some challenges are associated with these datasets, including variability in data distribution, data scarcity, and transfer learning issues when using models pre-trained from generic images. This work studies the effect of these challenges at the intra- and inter-domain level in few-shot learning scenarios with severe data imbalance. For this, we propose a methodology based on Siamese neural networks in which a series of techniques are integrated to mitigate the effects of data scarcity and distribution imbalance. Specifically, different initialization and data augmentation methods are analyzed, and four adaptations to Siamese networks of solutions to deal with imbalanced data are introduced, including data balancing and weighted loss, both separately and combined, and with a different balance of pairing ratios. Moreover, we also assess the inference process considering four classifiers, namely Histogram, kNN, SVM, and Random Forest. Evaluation is performed on three chest X-ray datasets with annotated cases of both positive and negative COVID-19 diagnoses. The accuracy of each technique proposed for the Siamese architecture is analyzed separately. The results are compared to those obtained using equivalent methods on a state-of-the-art CNN, achieving an average F1 improvement of up to 3.6%, and up to 5.6% of F1 for intra-domain cases. We conclude that the introduced techniques offer promising improvements over the baseline in almost all cases and that the technique selection may vary depending on the amount of data available and the level of imbalance. [ABSTRACT FROM AUTHOR]

This document contains summaries of several articles related to colorectal disease. The first article discusses the adoption of a hybrid alternative to single-use laparoscopic ports in order to reduce the carbon footprint of laparoscopic surgery. The second article explores surgeons' experiences and practices in the primary repair of obstetric anal sphincter injuries. The third article introduces an AI-powered endoscopic analysis tool that improves the detection of precancerous lesions in colorectal cancer screening. The fourth article examines the impact of colonoscopy key performance indicators on the risk of colorectal cancer and advanced polyps. The fifth article investigates the association between psoas sarcopenia and mortality in patients with per rectal bleeding. The sixth article outlines a Phase III study evaluating the efficacy and safety of perioperative dostarlimab monotherapy in patients with resectable colon adenocarcinoma. The seventh article assesses the incidence, healthcare resource use, and costs associated with incisional hernia repair. The final article discusses the use of innovative techniques in restorative prophylactic surgery for familial adenomatous polyposis. [Extracted from the article]

Background/Aims: We investigated how interactions between humans and computer-aided detection (CADe) systems are influenced by the user’s experience and polyp characteristics. Methods: We developed a CADe system using YOLOv4, trained on 16,996 polyp images from 1,914 patients and 1,800 synthesized sessile serrated lesion (SSL) images. The performance of polyp detection with CADe assistance was evaluated using a computerized test module. Eighteen participants were grouped by colonoscopy experience (nurses, fellows, and experts). The value added by CADe based on the histopathology and detection difficulty of polyps were analyzed. Results: The area under the curve for CADe was 0.87 (95% confidence interval [CI], 0.83 to 0.91). CADe assistance increased overall polyp detection accuracy from 69.7% to 77.7% (odds ratio [OR], 1.88; 95% CI, 1.69 to 2.09). However, accuracy decreased when CADe inaccurately detected a polyp (OR, 0.72; 95% CI, 0.58 to 0.87). The impact of CADe assistance was most and least prominent in the nurses (OR, 1.97; 95% CI, 1.71 to 2.27) and the experts (OR, 1.42; 95% CI, 1.15 to 1.74), respectively. Participants demonstrated better sensitivity with CADe assistance, achieving 81.7% for adenomas and 92.4% for easy-to-detect polyps, surpassing the standalone CADe performance of 79.7% and 89.8%, respectively. For SSLs and difficult-to-detect polyps, participants' sensitivities with CADe assistance (66.5% and 71.5%, respectively) were below those of standalone CADe (81.1% and 74.4%). Compared to the other two groups (56.1% and 61.7%), the expert group showed sensitivity closest to that of standalone CADe in detecting SSLs (79.7% vs 81.1%, respectively). Conclusions: CADe assistance boosts polyp detection significantly, but its effectiveness depends on the user’s experience, particularly for challenging lesions [ABSTRACT FROM AUTHOR]

Objectives: This study aims to develop computer-aided detection (CAD) for colorectal cancer (CRC) using abdominal CT based on a deep convolutional neural network. Methods: This retrospective study included consecutive patients with colorectal adenocarcinoma who underwent abdominal CT before CRC resection surgery (training set = 379, test set = 103). We customized the 3D U-Net of nnU-Net (CUNET) for CRC detection, which was trained with fivefold cross-validation using annotated CT images. CUNET was validated using datasets covering various clinical situations and institutions: an internal test set (n = 103), internal patients with CRC first determined by CT (n = 54) and asymptomatic CRC (n = 51), and an external validation set from two institutions (n = 60). During each validation, data from the healthy population were added (internal = 60; external = 130). CUNET was compared with other deep CNNs: residual U-Net and EfficientDet. The CAD performances were evaluated using per-CRC sensitivity (true positive/all CRCs), free-response receiver operating characteristic (FROC), and jackknife alternative FROC (JAFROC) curves. Results: CUNET showed a higher maximum per-CRC sensitivity than residual U-Net and EfficientDet (internal test set 91.3% vs. 61.2%, and 64.1%). The per-CRC sensitivity of CUNET at false-positive rates of 3.0 was as follows: internal CRC determined by CT, 89.3%; internal asymptomatic CRC, 87.3%; and external validation, 89.6%. CUNET detected 69.2% (9/13) of CRCs missed by radiologists and 89.7% (252/281) of CRCs from all validation sets. Conclusions: CUNET can detect CRC on abdominal CT in patients with various clinical situations and from external institutions. Key Points: • Customized 3D U-Net of nnU-Net (CUNET) can be applied to the opportunistic detection of colorectal cancer (CRC) in abdominal CT, helping radiologists detect unexpected CRC. • CUNET showed the best performance at false-positive rates ≥ 3.0, and 30.1% of false-positives were in the colorectum. CUNET detected 69.2% (9/13) of CRCs missed by radiologists and 87.3% (48/55) of asymptomatic CRCs. • CUNET detected CRCs in multiple validation sets composed of varying clinical situations and from different institutions, and CUNET detected 89.7% (252/281) of CRCs from all validation sets. [ABSTRACT FROM AUTHOR]

Objective: Accurately predicting knee osteoarthritis (KOA) is essential for early detection and personalized treatment. We aimed to develop and test a magnetic resonance imaging (MRI)‐based joint space (JS) radiomic model (RM) to predict radiographic KOA incidence through neural networks by integrating meniscus and femorotibial cartilage radiomic features. Methods: In the Osteoarthritis Initiative cohort, participants with knees without radiographic KOA at baseline but at high risk for radiographic KOA were included. Patients' knees developed radiographic KOA, whereas control knees did not over four years. We randomly split the participants into development and test cohorts (8:2) and extracted features from baseline three‐dimensional double‐echo steady‐state sequence MRI. Model performance was evaluated using an area under the receiver operating characteristic curve (AUC), sensitivity, and specificity in both cohorts. Nine resident surgeons performed the reader experiment without/with the JS‐RM aid. Results: Our study included 549 knees in the development cohort (275 knees of patients with KOA vs 274 knees of controls) and 137 knees in the test cohort (68 knees of patients with KOA vs 69 knees of controls). In the test cohort, JS‐RM had a favorable accuracy for predicting the radiographic KOA incidence with an AUC of 0.931 (95% confidence interval [CI] 0.876–0.963), a sensitivity of 84.4% (95% CI 83.9%–84.9%), and a specificity of 85.6% (95% CI 85.2%–86.0%). The mean specificity and sensitivity of resident surgeons through MRI reading in predicting radiographic KOA incidence were increased from 0.474 (95% CI 0.333–0.614) and 0.586 (95% CI 0.429–0.743) without the assistance of JS‐RM to 0.874 (95% CI 0.847–0.901) and 0.812 (95% CI 0.742–0.881) with JS‐RM assistance, respectively (P < 0.001). Conclusion: JS‐RM integrating the features of the meniscus and cartilage showed improved predictive values in radiographic KOA incidence. [ABSTRACT FROM AUTHOR]

To avoid irreversible vision loss, early detection and diagnosis of Diabetic Retinopathy (DR) severity is critical. The percentage of people undertaking eye examinations has risen in recent years, increasing the burden on Ophthalmologists. Various Artificial Intelligence (AI) screening systems have recently been deployed to improve the accuracy of DR diagnosis. However, owing to their black-box nature, most successful AI screening systems are still held back in reality for medical decision aid. The need for an Explainable Artificial Intelligence (XAI) screening system to help Ophthalmologists in DR diagnosis is inevitable. The proposed work is divided into three phases: (i) pre-processing, (ii) optic disk localization, and (iii) DR severity classification. On top of the deep learning model, the proposed work implements a Local Interpretable Model-agnostic Explanations (LIME) explainer to describe what features of the retinal image took part in justifying the predictions. The proposed framework outputs a pixel-value tensor, explaining the possible pixel values contributing to the model's prediction. MESSIDOR data collection is used for experimental analysis. When compared with other deep learning models, the proposed framework achieved a better accuracy of 98.04%, sensitivity of 99.69%, specificity of 96.37%, f1-score of 96.99% and error rate of 3.60%. Incorporating explainable deep learning models for diabetic retinopathy severity grading improves diagnostic accuracy and provides clinicians with clear insights, enabling trust and informed decision-making in DR diagnosis. This proposed technique enormously advances more effective and responsible healthcare procedures. [ABSTRACT FROM AUTHOR]

Brain tumor classification is a significant issue in computer aided diagnosis to make a convenient treatment. Deep learning techniques are surpassing traditional brain tumor classification methods in the medical image field with advanced hardware technology. In this article, a novel classification framework has been proposed that includes pre-trained fine-tuned EfficientNet B7. The framework focuses on a multi-class classification problem to distinguish among three common types of brain tumors, i.e., gliomas, meningiomas, and pituitary tumors. The proposed classification framework is trained and tested using six optimization algorithms SGD, RMSProp, Adagrad, Adam, Adadelta, and Nadam using two classifiers softmax and support vector machine (SVM). The best performance achieved by Nesterov-accelerated adaptive moment estimation (Nadam) optimizer using softmax classifier with test accuracy 99 % , average precision 99.3 % , average recall 97.3 % and average F1-score 98 % which outperformed other state-of-the-art architectures on Figshare MRI image data set. The proposed framework has also been evaluated, and performance has been compared using two more publicly available standard four-class brain tumor MRI data sets. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Assessing the cumulative degree of bowel injury in ileal Crohn's disease (CD) is difficult. We aimed to develop machine learning (ML) methodologies for automated estimation of cumulative ileal injury on computed tomography-enterography (CTE) to help predict future bowel surgery. METHODS: Adults with ileal CD using biologic therapy at a tertiary care center underwent ML analysis of CTE scans. Two fellowship-trained radiologists graded bowel injury severity at granular spatial increments along the ileum (1 cm), called mini-segments. ML segmentation methods were trained on radiologist grading with predicted severity and then spatially mapped to the ileum. Cumulative injury was calculated as the sum (S-CIDSS) and mean of severity grades along the ileum. Multivariate models of future small bowel resection were compared with cumulative ileum injury metrics and traditional bowel measures, adjusting for laboratory values, medications, and prior surgery at the time of CTE. RESULTS: In 229 CTE scans, 8,424 mini-segments underwent analysis. Agreement between ML and radiologists injury grading was strong (κ = 0.80, 95% confidence interval 0.79-0.81) and similar to inter-radiologist agreement (κ = 0.87, 95% confidence interval 0.85-0.88). S-CIDSS (46.6 vs 30.4, P = 0.0007) and mean cumulative injury grade scores (1.80 vs 1.42, P < 0.0001) were greater in CD biologic users that went to future surgery. Models using cumulative spatial metrics (area under the curve = 0.76) outperformed models using conventional bowel measures, laboratory values, and medical history (area under the curve = 0.62) for predicting future surgery in biologic users. DISCUSSION: Automated cumulative ileal injury scores show promise for improving prediction of outcomes in small bowel CD. Beyond replicating expert judgment, spatial enterography analysis can augment the personalization of bowel assessment in CD. [ABSTRACT FROM AUTHOR]

This document is a compilation of various studies and articles related to colonoscopy procedures and their outcomes. The references cover topics such as quality indicators for colonoscopy, the detection rates of adenomas and neoplasia, and the management and surveillance of colorectal polyps. The studies also discuss factors that can affect the quality of colonoscopy procedures, such as withdrawal time and technique. The document provides a comprehensive list of resources for library patrons conducting research on colonoscopy and related topics. [Extracted from the article]

Dermatological conditions are primarily prevalent in humans and are primarily caused by environmental and climatic fluctuations, as well as various other reasons. Timely identification is the most effective remedy to avert minor ailments from escalating into severe conditions. Diagnosing skin illnesses is consistently challenging for health practitioners. Presently, they rely on conventional methods, such as examining the condition of the skin. State-of-the-art technologies can enhance the accuracy of skin disease diagnosis by utilizing data-driven approaches. This paper presents a Computer Assisted Diagnosis (CAD) framework that has been developed to detect skin illnesses at an early stage. We suggest a computationally efficient and lightweight deep learning model that utilizes a CNN architecture. We then do thorough experiments to compare the performance of shallow and deep learning models. The CNN model under consideration consists of seven convolutional layers and has obtained an accuracy of 87.64% when applied to three distinct disease categories. The studies were conducted using the International Skin Imaging Collaboration (ISIC) dataset, which exclusively consists of dermoscopic images. This study enhances the field of skin disease diagnostics by utilizing state-of-the-art technology, attaining exceptional levels of accuracy, and striving for efficiency improvements. The unique features and future considerations of this technology create opportunities for additional advancements in the automated diagnosis of skin diseases and tailored treatment. [ABSTRACT FROM AUTHOR]

Pain diagnosis remains a challenging task due to its subjective nature, the variability in pain expression among individuals, and the difficult assessment of the underlying biopsychosocial factors. In this complex scenario, artificial intelligence (AI) can offer the potential to enhance diagnostic accuracy, predict treatment outcomes, and personalize pain management strategies. This review aims to dissect the current literature on computer-aided diagnosis methods. It also discusses how AI-driven diagnostic strategies can be integrated into multimodal models that combine various data sources, such as facial expression analysis, neuroimaging, and physiological signals, with advanced AI techniques. Despite the significant advancements in AI technology, its widespread adoption in clinical settings faces crucial challenges. The main issues are ethical considerations related to patient privacy, biases, and the lack of reliability and generalizability. Furthermore, there is a need for high-quality real-world validation and the development of standardized protocols and policies to guide the implementation of these technologies in diverse clinical settings. [ABSTRACT FROM AUTHOR]

Radiographic measurements play a crucial role in evaluating the alignment of distal radius fractures (DRFs). Various manual methods have been used to perform the measurements, but they are susceptible to inaccuracies. Recently, computer-aided methods have become available. This review explores the methods commonly used to assess DRFs. The review introduces the different measurement techniques, discusses the sources of measurement errors and measurement reliability, and provides a recommendation for their use. Radiographic measurements used in the evaluation of DRFs are not reliable. Standardizing the measurement techniques is crucial to address this and automated image analysis could help improve accuracy and reliability. [ABSTRACT FROM AUTHOR]

Purpose: This study evaluates the efficacy of two advanced Large Language Models (LLMs), OpenAI's ChatGPT 4 and Google's Gemini Advanced, in providing treatment recommendations for head and neck oncology cases. The aim is to assess their utility in supporting multidisciplinary oncological evaluations and decision-making processes. Methods: This comparative analysis examined the responses of ChatGPT 4 and Gemini Advanced to five hypothetical cases of head and neck cancer, each representing a different anatomical subsite. The responses were evaluated against the latest National Comprehensive Cancer Network (NCCN) guidelines by two blinded panels using the total disagreement score (TDS) and the artificial intelligence performance instrument (AIPI). Statistical assessments were performed using the Wilcoxon signed-rank test and the Friedman test. Results: Both LLMs produced relevant treatment recommendations with ChatGPT 4 generally outperforming Gemini Advanced regarding adherence to guidelines and comprehensive treatment planning. ChatGPT 4 showed higher AIPI scores (median 3 [2–4]) compared to Gemini Advanced (median 2 [2–3]), indicating better overall performance. Notably, inconsistencies were observed in the management of induction chemotherapy and surgical decisions, such as neck dissection. Conclusions: While both LLMs demonstrated the potential to aid in the multidisciplinary management of head and neck oncology, discrepancies in certain critical areas highlight the need for further refinement. The study supports the growing role of AI in enhancing clinical decision-making but also emphasizes the necessity for continuous updates and validation against current clinical standards to integrate AI into healthcare practices fully. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) refers to a micro-vascular complication causing vision problems in diabetic patients. It is a serious global public health issue that damages the retinal blood vessels and leaves a large number of patients with loss of vision. Micro-aneurysms, hemorrhages, macular edema, and exudates are some of the lesions used to diagnose it. When detected early, Diabetic Retinopathy can be treated well with precise identification of lesions. Computer-aided diagnosis refers to the approaches used to extract specific features associated with the disease by using image processing filters. Some Machine Learning tools, especially the ones well-suited to data analysis applications are particularly useful. This paper primarily focuses on a comparative performance evaluation of many machine learning and deep learning-based techniques applied for the diagnosis of this vision ailment in diabetic patients. [ABSTRACT FROM AUTHOR]

Skin cancer is one of the critical diseases that occurs in different age groups of the population. Early detection and prevention can save many lives. Melanoma is one of the fundamental skin cancer types. The traditional approaches for skin lesion detection and classification (SLDC) failed to distinguish between melanoma and non-melanoma skin malignancies, resulting in low classification accuracy. Thus, this article is focused on the implementation of computer aided detection method for SLDC using iterative random forest (IRF) algorithm, hereafter named as IRFNet. The proposed IRFNet acts as a decision-making tool for the dermatologists. Initially, the first level consists of five intuitive classifiers (ICs) named as perceptron coupled with color local binary patterns (PC-CLBP), histograms of oriented gradients (HOGs), the generative adversarial network coupled with ABCD rule (GAN-ABCD), ResNet, and AlexNet are used to extract the texture, shape, color, size, and convolutional pixel connections-based features from given skin lesion images. Then, IRF algorithm is used to select the optimal features and finally the object classifier called deep learning convolutional neural network (DLCNN) is employed to classify the type of lesion as either melanoma or non-melanoma based on back-propagation perceptron that provides the final decision. The extensive simulation outcome performed on ISIC-2019 and PH2 datasets discloses the superiority of the proposed IRFNet in terms of precision, recall, sensitivity, specificity, and f1-score. In addition, it also achieved the trade-off between classification accuracy and execution time as compared to the existing SLDC approaches. [ABSTRACT FROM AUTHOR]

Objective: The frequent use of intense X-rays in the diagnosis and follow-up of adolescent scoliosis patients results in radiation exposure. This study aims to explore the viability of a patient-friendly digital anthropometry method as an auxiliary tool. It also evaluates the correlation between this digital method and the Cobb method in assessing lumbar scoliosis values. Material and Method: 100 scoliosis patients were examined using the digital anthropometric method with 17 anatomical parameters defined. Lumbar scoliosis degrees were also measured using the Cobb method based on radiological images. Result: The patients had a female to male ratio of 3:1, and 49% were diagnosed with lumbar scoliosis. The patient with the highest lumbar scoliosis displayed a 13.8 degree cervical lordosis angle, a 50.1 degree thoracic kyphosis angle, and a 26.4 degree lumbar lordosis angle. This lumbar lordosis angle was above the mean value. Conversely, the patient with the lowest lumbar scoliosis had a 12.9 degree cervical lordosis angle, a 53.81 degree thoracic kyphosis angle, and a 21.8 degree lumbar lordosis angle, with the latter being below the mean value. A significant positive correlation was observed between the lumbar lordosis angle obtained from the photo- anthropometric analysis and the lumbar scoliosis degree measured using the Cobb method (r=0.902, p<0.001). Conclusion: Patient follow-up can be enhanced through the photo-anthropometric method, and due to its patient- friendly nature, it can be more integrally included in treatment programs. [ABSTRACT FROM AUTHOR]

Accurate segmentation of brain tumors from magnetic resonance imaging (MRI) is crucial for diagnosis, treatment planning, and monitoring of patients with neurological disorders. This paper proposes an approach for brain tumor segmentation employing a cascaded architecture integrating L‐Net and W‐Net deep learning models. The proposed cascaded model leverages the strengths of U‐Net as a baseline model to enhance the precision and robustness of the segmentation process. In the proposed framework, the L‐Net excels in capturing the mask, while the W‐Net focuses on fine‐grained features and spatial information to discern complex tumor boundaries. The cascaded configuration allows for a seamless integration of these complementary models, enhancing the overall segmentation performance. To evaluate the proposed approach, extensive experiments were conducted on the datasets of BraTs and SMS Medical College comprising multi‐modal MRI images. The experimental results demonstrate that the cascaded L‐Net and W‐Net model consistently outperforms individual models and other state‐of‐the‐art segmentation methods. The performance metrics such as the Dice Similarity Coefficient value achieved indicate high segmentation accuracy, while Sensitivity and Specificity metrics showcase the model's ability to correctly identify tumor regions and exclude healthy tissues. Moreover, the low Hausdorff Distance values confirm the model's capability to accurately delineate tumor boundaries. In comparison with the existing methods, the proposed cascaded scheme leverages the strengths of each network, leading to superior performance compared to existing works of literature. [ABSTRACT FROM AUTHOR]

Atrial fibrillation (AF) is a life-threatening cardiac condition caused by inadequate blood flow, resulting in abnormal ECG records, blood clotting, and cardioembolic strokes. In recent years, physicians have been particularly concerned with early detection and diagnosis to overcome cardiogenic stroke. AF can be easily identified at the initial stages due to the development in computer-aided diagnosis. The performance of this method is affected by noise and the variations in pattern of the ECG, which leads to false diagnosis. Current signal processing and shallow machine learning (ML) approaches are severely limited in their ability to detect this condition accurately. Deep neural networks have been shown to be extremely effective at learning nonlinear patterns in a wide variety of problems, which include computer vision tasks. Deep learning models are computationally costly, non-explainable, and require a large quantity of data to discover characteristics. In contrast, ML approaches are explainable and require good feature extraction. In this manuscript, ML based supervised classification method is developed based on feature ensembling. ECG signals are preprocessed (mean subtraction followed by Butterworth filtering and computation of RR intervals) and subjected to feature extraction (by entropy-, wavelets-, & statistical-features). The variations due to AF are effectively captured and selective features are ensembled to perform classification by SVM and KNN. This method is experimented on five different databases (such as: PAF prediction Challenge, Long-Term AF, Intracardiac, AF termination Challenge, and MIT-BIH atrial fibrillation) and the classification performance is found to be the highest compared to the state of art. To evaluate the effectiveness of the proposed technique, AF-specific characteristics are retrieved from the ECG signal in the presence of artificially added noise and the features are fed to classifiers for classification. Performance of the proposed method is compared with the deep learning based approaches. The graphical abstract of the proposed atrial fibrillation detection method is presented. The overall accuracy of the proposed method was found to be 91.88 % and 91.99 % for wavelets-SVM and ensemble wavelet-SVM, respectively. This model attained 100 % accuracy for entropy and statistical features with SVM and KNN, respectively. [ABSTRACT FROM AUTHOR]

Brain cancer is a substantial factor in the mortality associated with cancer, presenting difficulties in the timely identification of the disease. The precision of diagnoses is significantly dependent on the proficiency of radiologists and neurologists. Although there is potential for early detection with computer-aided diagnosis (CAD) algorithms, the majority of current research is hindered by its modest sample sizes. This meta-analysis aims to comprehensively assess the diagnostic test accuracy (DTA) of computer-aided design (CAD) models specifically designed for the detection of brain cancer utilizing hyperspectral (HSI) technology. We employ Quadas-2 criteria to choose seven papers and classify the proposed methodologies according to the artificial intelligence method, cancer type, and publication year. In order to evaluate heterogeneity and diagnostic performance, we utilize Deeks' funnel plot, the forest plot, and accuracy charts. The results of our research suggest that there is no notable variation among the investigations. The CAD techniques that have been examined exhibit a notable level of precision in the automated detection of brain cancer. However, the absence of external validation hinders their potential implementation in real-time clinical settings. This highlights the necessity for additional studies in order to authenticate the CAD models for wider clinical applicability. [ABSTRACT FROM AUTHOR]

Lung cancer (LC) is a life-threatening and dangerous disease all over the world. However, earlier diagnoses and treatment can save lives. Earlier diagnoses of malevolent cells in the lungs responsible for oxygenating the human body and expelling carbon dioxide due to significant procedures are critical. Even though a computed tomography (CT) scan is the best imaging approach in the healthcare sector, it is challenging for physicians to identify and interpret the tumour from CT scans. LC diagnosis in CT scan using artificial intelligence (AI) can help radiologists in earlier diagnoses, enhance performance, and decrease false negatives. Deep learning (DL) for detecting lymph node contribution on histopathological slides has become popular due to its great significance in patient diagnoses and treatment. This study introduces a computer-aided diagnosis for LC by utilizing the Waterwheel Plant Algorithm with DL (CADLC-WWPADL) approach. The primary aim of the CADLC-WWPADL approach is to classify and identify the existence of LC on CT scans. The CADLC-WWPADL method uses a lightweight MobileNet model for feature extraction. Besides, the CADLC-WWPADL method employs WWPA for the hyperparameter tuning process. Furthermore, the symmetrical autoencoder (SAE) model is utilized for classification. An investigational evaluation is performed to demonstrate the significant detection outputs of the CADLC-WWPADL technique. An extensive comparative study reported that the CADLC-WWPADL technique effectively performs with other models with a maximum accuracy of 99.05% under the benchmark CT image dataset. [ABSTRACT FROM AUTHOR]

Purpose: Susceptibility map weighted imaging (SMWI), based on quantitative susceptibility mapping (QSM), allows accurate nigrosome-1 (N1) evaluation and has been used to develop Parkinson’s disease (PD) deep learning (DL) classification algorithms. Neuromelanin-sensitive (NMS) MRI could improve automated quantitative N1 analysis by revealing neuromelanin content. This study aimed to compare classification performance of four approaches to PD diagnosis: (1) N1 quantitative “QSM-NMS” composite marker, (2) DL model for N1 morphological abnormality using SMWI (“Heuron IPD”), (3) DL model for N1 volume using SMWI (“Heuron NI”), and (4) N1 SMWI neuroradiological evaluation. Method: PD patients (n = 82; aged 65 ± 9 years; 68% male) and healthy-controls (n = 107; 66 ± 7 years; 48% male) underwent 3 T midbrain MRI with T2*-SWI multi-echo-GRE (for QSM and SMWI), and NMS-MRI. AUC was used to compare diagnostic performance. We tested for correlation of each imaging measure with clinical parameters (severity, duration and levodopa dosing) by Spearman-Rho or Kendall-Tao-Beta correlation. Results: Classification performance was excellent for the QSM-NMS composite marker (AUC = 0.94), N1 SMWI abnormality (AUC = 0.92), N1 SMWI volume (AUC = 0.90), and neuroradiologist (AUC = 0.98). Reasons for misclassification were right–left asymmetry, through-plane re-slicing, pulsation artefacts, and thin N1. In the two DL models, all 18/189 (9.5%) cases misclassified by Heuron IPD were controls with normal N1 volumes. We found significant correlation of the SN QSM-NMS composite measure with levodopa dosing (rho = −0.303, p = 0.006). Conclusion: Our data demonstrate excellent performance of a quantitative QSM-NMS marker and automated DL PD classification algorithms based on midbrain MRI, while suggesting potential further improvements. Clinical utility is supported but requires validation in earlier stage PD cohorts. [ABSTRACT FROM AUTHOR]

The most frequent type of cancer among women is breast cancer. Artificial intelligence (AI) researchers are developing automated systems to assist in the detection and classification of breast cancer. This study explores machine learning (ML) and deep learning (DL) as two AI methods for identifying benign and malignant breast tumors in ultrasound images. The investigation assesses the performance of various computer-aided detection and diagnosis (CAD) systems, which utilize either handcrafted features or deep features extracted from DL models. Furthermore, three models for CAD deep learning-based systems were implemented using convolutional neural networks (CNN), convolutional autoencoders (CAE), and deep features with CNN models, and compared with three traditional ML models based on handcrafted (texture) features. The results indicate that the deep features of the CNN model are promising, achieving a mean accuracy of 95% with a standard deviation of 1.1%. [ABSTRACT FROM AUTHOR]

Powered by machine learning, computer-aided diagnostics support clinicians by streamlining their work. In cancer screening, for instance, this technique often involves an automated detection of potential cancerous lesions from X-ray Computed Tomography (CT) images reconstructed a priori and unaware of the detection task. Since different reconstruction algorithms enhance different image attributes, a natural question is whether adapting the algorithms to the downstream task is relevant to improving the performance of computer-aided diagnosis tools. This paper provides empirical evidence about the performance of the taskadapted framework for joint reconstruction-segmentation of lung nodules from CT scans. We describe the procedures that allow the joint training of reconstruction and segmentation operators. We also report that relevant segmentation metrics improve by up to twenty percentage points in the four angular-sparsity and X-ray dose settings studied. Finally, we give practical recommendations for the procedure's hyperparameter tuning. Our results suggest that the imaging pipeline could benefit from computing different images adapted to the clinician's eyes and the automatic segmentation tools, outlining future improvements in patient care and clinical tasks. [ABSTRACT FROM AUTHOR]

Pneumonia is a medical condition affecting 100 million people globally, and rates are predicted to reach epidemic levels within the next several decades. As a result of the air sacs in both or even one lung becoming inflamed, the patient may experience fever, chills, and trouble breathing. Coughs with pus may also occur. Various organisms can cause pneumonia, including bacteria, viruses, and fungi. Early detection of pneumonia can allow the severity of the purulent material to be reduced. The ability of computer-aided detection techniques to reliably diagnose pneumonia has made them popular among scientists. We used a pre-trained Inception V3Net, Squeeze Excitation-based deep Convolutional Neural Network (SE-CNN) that was trained on the Kermany dataset and the RSNA Pneumonia Detection Challenge dataset in this study. In early-stage detection, the suggested technique beat previous state-of-the-art networks, achieving 91% precision in severity rating. Furthermore, our network's accuracy, recall, f1-score, as well as quadratic weighted kappa were reported to be 91.56%, 91%, and 90%, respectively. In terms of processing time and space, our suggested framework is simple, precise, and effective. [ABSTRACT FROM AUTHOR]

Integrating machine learning into medical diagnostics has revolutionized the field, particularly enhancing Computeraided Diagnosis (CAD) systems. These systems assist healthcare professionals by leveraging medical data and machine learning algorithms for more accurate diagnosis and treatment plans. Mammography, an X-ray-based imaging technique, is pivotal in early breast cancer detection, enabling the differentiation between benign and malignant lesions. Recent studies have focused on developing deep learning-enabled mammography CAD systems, which have shown promising results in detecting, segmenting, and classifying anomalies in mammogram images. This comprehensive review presents an innovative system architecture for breast cancer detection, segmentation, and classification using deep learning within mammography CAD systems. It also explores publicly available mammogram datasets and the critical parameters for assessing deep learning system performance. The literature review is meticulously conducted using the PRISMA methodology to evaluate and synthesise novel research findings in this domain. This survey highlights the technological advancements and underlines the potential of deep learning in transforming mammographic analysis for breast cancer detection. [ABSTRACT FROM AUTHOR]

Rapid changes in artificial intelligence (AI) have already impacted the medical field. While the use of AI to assist medical diagnosis has been documented, AI is continually expanding within medical applications. AI applications in sonography and their effect on ultrasound examinations and sonographers are still indeterminate. Six papers were reviewed to investigate AI applications and effects within the sonography field. These papers provided results on a range of ultrasound applications including breast, obstetric, skin lesions, carotid, blood flow and cardiac ultrasound imaging when combined with AI. In this narrative review, the application of AI demonstrated that accuracy and speed of clinical diagnosis can be improved. These six aspects of ultrasound imaging combined with AI demonstrated the potential to assist the operator and clinicians with a diagnosis in various applications and settings. Additionally, AI can be beneficial to telehealth applications for rural and remote areas where healthcare access can be limited. These changes are opportunities to assist with medical care to provide benefits to patients, sonographers and clinicians as AI transitions to a positive integration within many aspects of clinical care. [ABSTRACT FROM AUTHOR]

Background and Aims: The utilization of artificial intelligence (AI) with computer‐aided detection (CADe) has the potential to increase the adenoma detection rate (ADR) by up to 30% in expert settings and specialized centers. The impact of CADe on serrated polyp detection rates (SDR) and academic trainees ADR & SDR remains underexplored. We aim to investigate the effect of CADe on ADR and SDR at an academic center with various levels of providers' experience. Methods: A single‐center retrospective analysis was conducted on asymptomatic patients between the ages of 45 and 75 who underwent screening colonoscopy. Colonoscopy reports were reviewed for 3 months prior to the introduction of GI Genius™ (Medtronic, USA) and 3 months after its implementation. The primary outcome was ADR and SDR with and without CADe. Results: Totally 658 colonoscopies were eligible for analysis. CADe resulted in statistically significant improvement in SDR from 8.92% to 14.1% (P = 0.037). The (ADR + SDR) with CADe and without CADe was 58% and 55.1%, respectively (P = 0.46). Average colonoscopy (CSC) withdrawal time was 17.33 min (SD 10) with the device compared with 17.35 min (SD 9) without the device (P = 0.98). Conclusion: In this study, GI Genius™ was associated with a statistically significant increase in SDR alone, but not in ADR or (ADR + SDR), likely secondary to the more elusive nature of serrated polyps compared to adenomatous polyps. The use of CADe did not affect withdrawal time. [ABSTRACT FROM AUTHOR]