Artificial Intelligence-based Diagnosis Research Articles

Artificial intelligence-based diagnosis in fetal pathology using external ear shapes.

Here we trained an automatic phenotype assessment tool to recognize syndromic ears in two syndromes in fetuses-=CHARGE and Mandibulo-Facial Dysostosis Guion Almeida type (MFDGA)-versus controls. We trained an automatic model on all profile pictures of children diagnosed with genetically confirmed MFDGA and CHARGE syndromes, and a cohort of control patients, collected from 1981 to 2023 in Necker Hospital (Paris) with a visible external ear. The model consisted in extracting landmarks from photographs of external ears, in applying geometric morphometry methods, and in a classification step using machine learning. The approach was then tested on photographs of two groups of fetuses: controls and fetuses with CHARGE and MFDGA syndromes. The training set contained a total of 1489 ear photographs from 526 children. The validation set contained a total of 51 ear photographs from 51 fetuses. The overall accuracy was 72.6% (58.3%-84.1%, p<0.001), and 76.4%, 74.9%, and 86.2% respectively for CHARGE, control and MFDGA fetuses. The area under the curves were 86.8%, 87.5%, and 90.3% respectively for CHARGE, controls, and MFDGA fetuses. We report the first automatic fetal ear phenotyping model, with satisfactory classification performances. Further validations are required before using this approach as a diagnostic tool.

Malaria and neurological complications: intersecting mechanisms, disease models, and artificial intelligence-based diagnosis

INTRODUCTION: In 2022, approximately 608,000 deaths worldwide were attributed to malaria. Beyond its high mortality rates, malaria is responsible for numerous long-lasting complications in survivors, including neurological deficits. Globally, over 1 billion individuals live with various neurological disorders, leading to seven million deaths annually. OBJECTIVE: Overcoming challenges associated with disease modeling and developing advanced techniques to investigate the neurological consequences of malaria are of great importance. Examining the influence of imbalanced gut microbiota and shared genetic factors on malaria progression and specific neurological conditions is advancing our understanding of neurodegenerative and neurocognitive impairments in malaria survivors. Some common molecular mechanisms shared by both malaria and neurological pathologies, including disruptions in the blood-brain barrier, neuroinflammation, and increased amyloid-β (Aβ) levels, have been studied. This review explores the pathogenesis of Plasmodium infection, highlighting molecular events in the intersecting mechanisms of malaria and Alzheimer’s disease (AD). The application of artificial intelligence and machine learning-based diagnostic tools is also of interest in this area, as they offer promising solutions for diagnosis and treatment. CONCLUSION: By elucidating the intersecting mechanisms of malaria and AD, this paper provides valuable insights into early detection methods and potential treatment strategies that may enable effective management of neurodegenerative progression in individuals affected by malaria.

Abstracts from the 25th IUSTI World Congress incorporating the Australasian Sexual and Reproductive Health Conference.

17-20 September 2024 ICC Sydney, Gadigal Country, Australia Theme: "Commitment to sexual and reproductive health and human rights" Taking place 17-20 September 2024 at the International Convention Centre in Sydney, Gadigal Country, the 25th IUSTI World Congress will this year incorporate the Australasian Sexual and Reproductive Health Conference. This congress will provide an opportunity to meet, discuss and learn about the latest research and innovation in sexual and reproductive health. The program will incorporate a range of world leading speakers and presentations, allowing delegates to expand their professional knowledge through local and international insight. All abstracts submitted for consideration in the 2024 program were independently reviewed by three experts on a range of criteria. These results were aggregated and presented to the Scientific Program Committee who used the scores and feedback from reviewers to select which presentations would be accepted into the program. These scores also determined the top abstracts within each theme. To cite the full set of abstracts: (2024) Abstracts from the 25th IUSTI World Congress incorporating the Australasian Sexual and Reproductive Health Conference. Sexual Health 21 , SHv21n4abs. doi:10.1071/SHv21n4abs To cite individual abstracts use the following format: Sun J et al . (2024) 2. Artificial intelligence-based diagnosis: distinguishing early syphilis from other sexually transmitted infections (STIs) [Conference abstract]. Sexual Health 21 , SHv21n4abs.

From bedside to portable and wearable: development of a conformable ultrasound patch for deep breast tissue imaging.

A breakthrough study from Du etal. has developed a wearable, ultrasound imaging patch for standardized and reproducible breast tissue imaging. The technology utilizes a honeycomb patch design to facilitate guided movement of the ultrasound array, enabling comprehensive, multiangle breast imaging. The system was validated in vitro and in vivo with a single human subject and has the potential for early-stage breast cancer detection. This study addressed the current limitations of wearable ultrasound technologies, including imaging over large, curvilinear organs and integration of superior piezoelectric materials for high-performance ultrasound arrays. The transition of ultrasound from the bedside to portable and wearable devices will pave the way for integration with big data collection, such as artificial intelligence (AI)-based diagnosis and personalized ultrasonographic profile generation, for rapid and objective measurements. This advancement is especially important in the context of breast cancer, where early diagnosis and assessment of medical therapy responses are paramount to patient care.

Application of Artificial Intelligence-Based Auxiliary Diagnosis in Congenital Heart Disease Screening.

To evaluate the application value of artificial intelligence-based auxiliary diagnosis for congenital heart disease. From May 2017 to December 2019, 1892 cases of congenital heart disease heart sounds were collected for learning- and memory-assisted diagnosis. The diagnosis rate and classification recognition were verified in 326 congenital heart disease cases. Auscultation and artificial intelligence-assisted diagnosis were used in 518 258 congenital heart disease screenings, and the detection accuracies of congenital heart disease and pulmonary hypertension were compared. Female sex and age > 14 years were predominant in atrial septal defect (P <.001) compared with ventricular septal defect/patent ductus arteriosus cases. Family history was more prominent in patent ductus arteriosus patients (P <.001). Compared with no pulmonary arterial hypertension, a male predominance was seen in cases of congenital heart disease-pulmonary arterial hypertension (P <.001), and age was significantly associated with pulmonary arterial hypertension (P =.008). A high prevalence of extracardiac anomalies was found in the pulmonary arterial hypertension group. A total of 326 patients were examined by artificial intelligence. The detection rate of atrial septal defect was 73.8%, which was different from that of auscultation (P =.008). The detection rate of ventricular septal defect was 78.8, and the detection rate of patent ductus arte-riosus was 88.9%. A total of 518 258 people from 82 towns and 1220 schools were screened including 15 453 suspected and 3930 (7.58%) confirmed cases. The detection accuracy of artificial intelligence in ventricular septal defect (P =.007) and patent ductus arteriosus (P =.021) classification was higher than that of auscultation. For normal cases, the recurrent neural network had a high accuracy of 97.77% in congenital heart disease-pulmonary arterial hypertension diagnosis (P =.032). Artificial intelligence-based diagnosis is an effective assistance method for congenital heart disease screening.

Review of eye diagnosis in traditional Chinese medicine: Modernization and future prospects

This article reviews the basic theories, methods, and clinical applications of eye diagnosis in traditional Chinese medicine (TCM). It introduces cutting-edge methods and applications and explains that the modernization of TCM eye diagnosis includes “equipment-assisted diagnosis” and “artificial intelligence-based diagnosis”. The article also notes that while there are many recent studies of the static attributes of eyes in modern TCM eye diagnosis, modern application research on the dynamic attributes of eyes in TCM diagnosis theory is relatively rare. We propose, therefore, that introducing advanced eye-movement detection technology into TCM clinical diagnosis could help to further modernize TCM eye diagnosis.

Static and Dynamic Load Carrying Capacity of Concrete Members Damaged by Internal Expansion-Induced Deterioration

Structures in Japan are stressed by harsh environmental and mechanical actions resulting from the sea and mountainous areas. This is why it is particularly important to find ways to maintain the integrity of structures in this country. This involves advanced experiments and structural analysis. Assistant Professor Hiroki Tamai, Structural Analysis Laboratory, Kyushu University, Japan, leads a team working to quantify the effects of salt-induced rebar corrosion and alkali-silica reaction (ASR) degradation on the residual load-bearing capacity of reinforced concrete (RC) members and structures. The goal is to use the acquired findings in the development of a method to accurately assess the condition of damaged structures and promptly respond with appropriate actions such as repair and reinforcement. The work involves extensive analytical research, including advanced experiment and numerical analysis, as well as quantitative evaluations and non-destructive inspection methods, some of which use Artificial Intelligence-based diagnosis systems. In their latest investigations, the researchers have been studying the quantitative understanding of the cracking behavior of concrete deteriorated by internal expansion using X-ray CT and other techniques, and the evaluation of the static and dynamic load-bearing capacity of RC beams with deteriorated damage. This will enable them to accurately identify damage to structural members and structures due to deterioration caused by internal expansion, to better understand their residual capacity, and to propose more appropriate repair and reinforcement methods.

Development of a prognostic prediction support system for cervical intraepithelial neoplasia using artificial intelligence-based diagnosis.

ObjectiveHuman papillomavirus subtypes are predictive indicators of cervical intraepithelial neoplasia (CIN) progression. While colposcopy is also an essential part of cervical cancer prevention, its accuracy and reproducibility are limited because of subjective evaluation. This study aimed to develop an artificial intelligence (AI) algorithm that can accurately detect the optimal lesion associated with prognosis using colposcopic images of CIN2 patients by utilizing objective AI diagnosis.MethodsWe identified colposcopic findings associated with the prognosis of patients with CIN2. We developed a convolutional neural network that can automatically detect the rate of high-grade lesions in the uterovaginal area in 12 segments. We finally evaluated the detection accuracy of our AI algorithm compared with the scores by multiple gynecologic oncologists.ResultsHigh-grade lesion occupancy in the uterovaginal area detected by senior colposcopists was significantly correlated with the prognosis of patients with CIN2. The detection rate for high-grade lesions in 12 segments of the uterovaginal area by the AI system was 62.1% for recall, and the overall correct response rate was 89.7%. Moreover, the percentage of high-grade lesions detected by the AI system was significantly correlated with the rate detected by multiple gynecologic senior oncologists (r=0.61).ConclusionOur novel AI algorithm can accurately determine high-grade lesions associated with prognosis on colposcopic images, and these results provide an insight into the additional utility of colposcopy for the management of patients with CIN2.

Artificial Intelligence-Based Diagnosis of Breast Cancer by Mammography Microcalcification

Artificial Intelligence-Based Diagnosis of Breast Cancer by Mammography Microcalcification

An Early Prediction of Lung Cancer using CT Scan Images

Lung cancer is a common occurrence type in a population and one amonglethal cancers. Recently, out of several research presented by diverse health agencies; it is obvious that the fatality ratio is rising due todelayeddiagnosis of lung cancer. Hence, an artificial intelligence-based diagnosis is required to find out the onset of lung nodule micro-calcification, which may support the doctors and radiologists to accurately predict it through image processing methods. In this paper, a novel technique is proposed to identify the nodule micro-calcification pattern by using its physical features. The physical features that considered are the reflection coefficients and mass densities of the binned CT image of lung. The physical features measurements reiteratesonce again the existence of malignant nodule. Then, by applying the methods of thresholding and in interpolation of physical features, a three-dimensional (3D) projected image of the region of interest (ROI) is achieved in respect of physical dimensions. Thus, the nodule size is calculated from 3D projection. This concept is used to verify how best in classification with 100 malignant images (the nodule presence) and 10 normal images (the nodule absence). Apart size measurement, the proposed method supports SVM classifier to act for excellentclassification from normal and malignantinput imagesby just using two physical features. The classifier exhibited an accuracy of 98%.

Optical Diagnosis of Colorectal Polyps: Recent Developments.

Optical diagnosis of diminutive colorectal polyps has been recently proposed as an alternative to histopathologic diagnosis. Recent developments in imaging techniques, new classification systems, and the use of artificial intelligence have allowed for increased viability of optical diagnosis. This review provides an up-to-date overview of optical diagnosis recommendations, classifications, outcomes, and recent developments. There are currently seven major classification systems and three major society recommendations for quality benchmarks for optical diagnosis of diminutive polyps. The NICE classification has been extensively studied and meets quality benchmarks for most imaging techniques but does not allow for the diagnosis of sessile serrated polyps (SSPs). The SIMPLE classification has met quality benchmarks for NBI and i-Scan and allows for the diagnosis of SSPs. Other classification systems need to be further studied to validate effectiveness. Computer-assisted diagnosis of colorectal polyps is a very promising recent development with first studies showing that society-recommended quality benchmarks for real-time colonoscopies on patients are being met. Limitations include a non-negligible percentage of failure to diagnose, low specificity, and low number of real-time diagnostic studies. More research needs to be performed to further understand the value of artificial intelligence for optical polyp diagnosis. Optical diagnosis of diminutive colorectal polyps is currently a viable strategy for experienced endoscopists using validated classifications and imaging-enhanced endoscopy. Artificial intelligence-based diagnosis could make optical diagnosis widely applicable but is currently in its early developmental stage.