Artificial Intelligence Deep Learning Research Articles

Scalable deep learning artificial intelligence histopathology slide analysis and validation.

Deep learning involves an artificial intelligence (AI) approach and has been shown to provide superior performance for automating image recognition tasks, as well as exceeding human capabilities in both time and accuracy. Histopathology diagnostics is one of the more popular challenges at the intersection of artificial intelligence, computer vision, and medicine. Developing methods to automatically detect and identify pathologies in digitized histology slides imposes unique challenges due to the large size of these images and the complexity of the features present in biological tissue. Most methods that are capable of human-level recognition in histopathology are tuned to a specific problem since the computational complexity exceeds that of traditional image classification problems. In the current study, a deep learning approach is developed and presented that can be trained to locate and accurately classify different types of pathologies in gigapixel digitized histology slides along with completing the binary disease classification for the entire image. The approach uses a novel pyramid tiling approach to take advantage of spatial awareness around the area to be classified, while maintaining efficiency and scalability for gigapixel images. The approach is trained and validated on a wide variety of tissue types (i.e., testis, ovary, prostate, kidney) and pathologies taken from an epigenetically altered histology study at Washington State University. The newly developed procedure was optimized and validated along with comparison and validation on public histology datasets. The current developed procedure was found to be optimal and more reproducible when compared to manual procedures, and optimal to previous protocols that used fragmented tissue or slide analysis. Observations demonstrate that the deep learning histopathology analysis is significantly more efficient and accurate than standard manual histopathology analysis.

Automatic detection of temporomandibular joint osteoarthritis radiographic features using deep learning artificial intelligence. A Diagnostic accuracy study

ObjectiveThe purpose of this study was to investigate the diagnostic performance of a neural network Artificial Intelligence model for the radiographic confirmation of Temporomandibular Joint Osteoarthritis in reference to an experienced radiologist. Materials and MethodsThe diagnostic performance of an AI model in identifying radiographic features in patients with TMJ-OA was evaluated in a diagnostic accuracy cohort study. Adult patients elected for radiographic examination by the Diagnostic Criteria for Temporomandibular Disorders decision tree were included. Cone-beam computed Tomography images were evaluated by object detection YOLO deep learning model. The diagnostic performance was verified against examiner radiographic evaluation. ResultsThe differences between the AI model and examiner were non-significant statistically, except in the subcortical cyst (P=0.049*). AI model showed substantial to near-perfect levels of agreement when compared to those of the examiner data. Regarding each radiographic phenotype, the AI model reported favorable sensitivity, specificity, accuracy, and highly statistically significant Receiver Operating Characteristic (ROC) analysis (p < 0.001). Area Under Curve ranged from 0.872, for surface erosion, to 0.911 for subcortical cyst. ConclusionAI object detection model could open the horizon for a valid, automated, and convenient modality for TMJ-OA radiographic confirmation and radiomic features identification with a significant diagnostic power.

Prediction of fellow eye neovascularization in type 3 macular neovascularization (Retinal angiomatous proliferation) using deep learning.

To establish a deep learning artificial intelligence model to predict the risk of long-term fellow eye neovascularization in unilateral type 3 macular neovascularization (MNV). This retrospective study included 217 patients (199 in the training/validation of the AI model and 18 in the testing set) with a diagnosis of unilateral type 3 MNV. The purpose of the AI model was to predict fellow eye neovascularization within 24 months after the initial diagnosis. The data used to train the AI model included a baseline fundus image and horizontal/vertical cross-hair scan optical coherence tomography images in the fellow eye. The neural network of this study for AI-learning was based on the visual geometry group with modification. The precision, recall, accuracy, and the area under the curve values of receiver operating characteristics (AUCROC) were calculated for the AI model. The accuracy of an experienced (examiner 1) and less experienced (examiner 2) human examiner was also evaluated. The incidence of fellow eye neovascularization over 24 months was 28.6% in the training/validation set and 38.9% in the testing set (P = 0.361). In the AI model, precision was 0.562, recall was 0.714, accuracy was 0.667, and the AUCROC was 0.675. The sensitivity, specificity, and accuracy were 0.429, 0.727, and 0.611, respectively, for examiner 1, and 0.143, 0.636, and 0.444, respectively, for examiner 2. This is the first AI study focusing on the clinical course of type 3 MNV. While our AI model exhibited accuracy comparable to that of human examiners, overall accuracy was not high. This may partly be a result of the relatively small number of patients used for AI training, suggesting the need for future multi-center studies to improve the accuracy of the model.

Design of Interactive Artificial Intelligence for Early Cognitive Diagnosis

Due to the aging population in South Korea, the proportion of elderly people aged 65 and over is expected to increase from 14.9% in 2019 to 46.5% by 2067. The number of elderly people per 100 working-age population (15-64 years old) is also anticipated to rise to 102.4 by 2067. Population aging is recognized as a social issue, leading to problems such as increased chronic diseases, higher levels of elderly isolation, and insufficient medical infrastructure. To solve the problem of cognitive decline, such as dementia due to the aging of the population, research is actively being conducted in various fields, such as simulating cognitive ability and learning, inference, prediction, and problem-solving using artificial intelligence deep learning technology in the form of a fusion of artificial intelligence and realistic content technology. This study is on an interactive cognitive early diagnosis training system using artificial intelligence. The speech recognition technology for early cognitive diagnosis uses Selvas AI (Artificial Intelligence)'s speech recognition STT (Speech to Text)-TTS (Text to Speech). AI speech recognition interaction can increase psychological safety through conversation with users. It evaluates cognition (dementia) using MMSE (Mini-Mental State Examination)-K (DS), and it is a system that evaluates seven cognitive areas through the CERAD (Consortium to Establish a Registry for Alzheimer Disease)-K analysis system. The design of interactive artificial intelligence for early cognitive diagnosis aims to improve the cognitive function and daily living abilities of the elderly population.

Leveraging Deep Learning and Generative AI for Predicting Rheological Properties and Material Compositions of 3D Printed Polyacrylamide Hydrogels.

Artificial intelligence (AI) has the ability to predict rheological properties and constituent composition of 3D-printed materials with appropriately trained models. However, these models are not currently available for use. In this work, we trained deep learning (DL) models to (1) predict the rheological properties, such as the storage (G') and loss (G") moduli, of 3D-printed polyacrylamide (PAA) substrates, and (2) predict the composition of materials and associated 3D printing parameters for a desired pair of G' and G". We employed a multilayer perceptron (MLP) and successfully predicted G' and G" from seven gel constituent parameters in a multivariate regression process. We used a grid-search algorithm along with 10-fold cross validation to tune the hyperparameters of the MLP, and found the R2 value to be 0.89. Next, we adopted two generative DL models named variational autoencoder (VAE) and conditional variational autoencoder (CVAE) to learn data patterns and generate constituent compositions. With these generative models, we produced synthetic data with the same statistical distribution as the real data of actual hydrogel fabrication, which was then validated using Student's t-test and an autoencoder (AE) anomaly detector. We found that none of the seven generated gel constituents were significantly different from the real data. Our trained DL models were successful in mapping the input-output relationship for the 3D-printed hydrogel substrates, which can predict multiple variables from a handful of input variables and vice versa.

Deep learning detects subtle facial expressions in a multilevel society primate.

Facial expressions in nonhuman primates are complex processes involving psychological, emotional, and physiological factors, and may use subtle signals to communicate significant information. However, uncertainty surrounds the functional significance of subtle facial expressions in animals. Using artificial intelligence (AI), this study found that nonhuman primates exhibit subtle facial expressions that are undetectable by human observers. We focused on the golden snub-nosed monkeys (Rhinopithecus roxellana), a primate species with a multilevel society. We collected 3427 front-facing images of monkeys from 275 video clips captured in both wild and laboratory settings. Three deep learning models, EfficientNet, RepMLP, and Tokens-To-Token ViT, were utilized for AI recognition. To compare the accuracy of human performance, two groups were recruited: one with prior animal observation experience and one without any such experience. The results showed human observers to correctly detect facial expressions (32.1% for inexperienced humans and 45.0% for experienced humans on average with a chance level of 33%). In contrast, the AI deep learning models achieved significantly higher accuracy rates. The best-performing model achieved an accuracy of 94.5%. Our results provide evidence that golden snub-nosed monkeys exhibit subtle facial expressions. The results further our understanding of animal facial expressions and also how such modes of communication may contribute to the origin of complex primate social systems.

Prediction of flashover time in a compartment fire by CNN-LSTM based deep neural network considering wearable data collection equipment

Real-time prediction of flashover in a compartment fire is of great significance for rescue. This paper investigated a CNN-LSTM neural network to predict the flashover based on the temperature and radiative heat flux in 24 compartment fire cases simulated with FDS. Multiple conditions affecting compartment fires have been taken into account, like room size, room height, fire source location, opening size, number of vents, and fire load. Radiative heat flux meters and thermocouples were positioned at specific heights, to simulate sensors worn by firefighters entering a fire scene. The proposed model consisted of a convolutional layer and three LSTM layers. After training, the deep learning model effectively identified the flashover in compartment fires, with an average relative error of 4.1 % for temperature prediction and 11.2 % for radiative heat flux prediction. In addition, the model was validated on other simulated fire cases and full-scale experimental data, and the results showed good performance. In this paper, the sensitivity of the model to lead time was analyzed, and the application range of the model was determined. The model performed well within the lead time of 25s. In view of the strong performance of the proposed deep learning model in predicting flashover based on wearable sensors (temperature and radiative heat flux), it is believed that this model holds potential for application in a broader range of fire scenarios. This demonstrates the feasibility of using deep learning artificial intelligence models to predict compartment fire development, providing scientific guidance for smart firefighting technology.

Ironmaking process under artificial intelligence technology: A review

The ironmaking process is a complex and continuous operation, which makes it difficult to collect and predict the production parameters. To address this challenge, artificial intelligence (AI) deep learning has emerged as a promising solution. The paper discusses the evolution and utilisation of AI in the metallurgical industry. The paper emphasises the implementation of AI in various ironmaking processes, including raw material selection and charging for iron production, molten iron composition prediction in blast furnace (BF), and internal operational state and fault detection in BF. Additionally, the paper predicts BF gas and explores the utilisation of automated equipment such as cooling systems. Drawing upon existing literature, this paper emphasises that deep learning has numerous advantages in the ironmaking industry, including its ability to process data quickly, strong adaptability, and high accuracy. The paper also highlights several challenges that the future development of AI in the ironmaking field may encounter. Looking ahead, the future of deep learning in ironmaking appears promising.

Advanced classification of drill core rock type and weathering grade using detection transformer-based artificial intelligence techniques

Rock classification plays a crucial role in various fields such as geology, engineering, and environmental studies. Employing deep learning AI (artificial intelligence) methods has a high potential to significantly improve the accuracy and efficiency of this task. The paper delves into the exploration of two cutting-edge AI techniques, namely Mask DINO and Mask R-CNN (convolutional neural network), as means to identify rock weathering grades and rock types. The results demonstrate that Mask DINO, which is a Detection Transformer (DETR), outperforms Mask R-CNN for the aforementioned purposes. Mask DINO achieved f-1 scores of 91% and 86% in weathering grade detection and rock type detection, as opposed to the Mask R-CNN’s f-1 scores of 84% and 75%, respectively. These findings underscore the substantial potential of employing DETR algorithms like Mask DINO for automatic classification of both rock type and weathering states. Although the study examines only two AI models, the data processing and other techniques developed in this study may serve as a foundation for future advancements in the field. By incorporating these advanced AI techniques, logging personnel can obtain valuable references to aid their work, ultimately contributing to the advancement of geological and related fields.

Improved detection of small pulmonary embolism on unenhanced computed tomography using an artificial intelligence-based algorithm - a single centre retrospective study.

To preliminarily verify the feasibility of a deep-learning (DL) artificial intelligence (AI) model to localize pulmonary embolism (PE) on unenhanced chest-CT by comparison with pulmonary artery (PA) CT angiography (CTA). In a monocentric study, we retrospectively reviewed 99 oncological patients (median age in years: 64 (range: 28-92 years); percentage of female: 39.4%) who received unenhanced and contrast-enhanced chest CT examinations in one session between January 2020 and October 2022 and who were diagnosed incidentally with PE. Findings in the unenhanced images were correlated with the contrast-enhanced images, which were considered the gold standard for central, segmental and subsegmental PE. The new algorithm was trained and tested based on the 99 unenhanced chest-CT image data sets. Based on them, candidate boxes, which were output by the model, were post-processed by evaluating whether the predicted box intersects with the patient's lung segmentation at any position. The AI-based algorithm proved to have an overall sensitivity of 54.5% for central, of 81.9% for segmental and 80.0% for subsegmental PE if taking n = 20 candidate boxes into account. Depending on the localization of the pulmonary embolism, the detection rate for only one box was: 18.1% central, 34.7% segmental and 0.0% subsegmental. The median volume of the clots differed significantly between the three subgroups and was 846.5 mm3 (IQR:591.1-964.8) in central, 201.3 mm3 (IQR:98.3-390.9) in segmental and 110.6 mm3 (IQR:94.3-128.0) in subsegmental PA (p < 0.05). The new algorithm proved to have high sensitivity in detecting PE in particular in segmental/subsegmental localization and may guide to decide whether a second contrast enhanced CT is necessary.

Enhancing personalized gene expression prediction from DNA sequences using genomic foundation models

Artificial intelligence/deep learning (AI/DL) models that predict molecular phenotypes like gene expression directly from DNA sequences have recently emerged. While these models have proven effective at capturing the variation across genes, their ability to explain inter-individual differences has been limited. We hypothesize that the performance gap can be narrowed through the use of pre-trained embeddings from the Nucleotide Transformer, a large foundation model trained on 3,000+ genomes. We train a transformer model using the pre-trained embeddings and compare its predictive performance to Enformer, the current state-of-the-art model, using genotype and expression data from 290 individuals. Our model significantly outperforms Enformer in terms of correlation across individuals and narrows the performance gap with an elastic net regression approach using just the genetic variants as predictors. Although simple regression models have their advantages in personalized prediction tasks, DL approaches based on foundation models pre-trained on diverse genomes have unique strengths in flexibility and interpretability. With further methodological and computational improvements with more training data, these models may someday predict molecular phenotypes from DNA sequences with accuracy surpassing that of regression-based approaches. Our work demonstrates the potential for large pre-trained AI/DL models to advance functional genomics.

Research status of the tumor stroma ratio in prognosis and treatment of pancreatic cancer

An increasing number of studies suggested that the tumor microenvironment exerts a substantial influence on the pathophysiology of pancreatic cancer. As a crucial component of the tumor microenvironment,the tumor stroma plays a pivotal role in the occurrence,development,and chemotherapy resistance of pancreatic cancer. By serving as a proxy for the interaction between tumor cells and the microenvironment,the tumor stroma ratio(TSR) has emerged as a focal point of investigation in recent years. At present,numerous studies show that a low TSR is a protective factor for the prognosis of resectable pancreatic cancer. Additionally, patients with a low TSR are more suitable for the gemcitabine and albumin-bound paclitaxel chemotherapy regimen. But these researches are not conclusive, and there is still a gap between guiding precision treatment. Further research and exploration are required. Integration of artificial intelligence deep learning models into traditional pathological and imaging assessments facilitates precise evaluation of the TSR. It can also enable stratification and precision treatment of pancreatic cancer patients based on this index.

AI meets physics: a comprehensive survey

Uncovering the mechanisms of physics is driving a new paradigm in artificial intelligence (AI) discovery. Today, physics has enabled us to understand the AI paradigm in a wide range of matter, energy, and space-time scales through data, knowledge, priors, and laws. At the same time, the AI paradigm also draws on and introduces the knowledge and laws of physics to promote its own development. Then this new paradigm of using physical science to inspire AI is the physical science of artificial intelligence (PhysicsScience4AI, PS4AI). Although AI has become the driving force for development in various fields, there is still a “black box” phenomenon that is difficult to explain in the field of AI deep learning. This article will briefly review the connection between relevant physics disciplines (classical mechanics, electromagnetism, statistical physics, quantum mechanics) and AI. It will focus on discussing the mechanisms of physics disciplines and how they inspire the AI deep learning paradigm, and briefly introduce some related work on how AI solves physics problems. PS4AI is a new research field. At the end of the article, we summarize the challenges facing the new physics-inspired AI paradigm and look forward to the next generation of artificial intelligence technology. This article aims to provide a brief review of research related to physics-inspired AI deep algorithms and to stimulate future research and exploration by elucidating recent advances in physics.

A Review of the Development and Future Challenges of Case-Based Reasoning

Case-based reasoning (CBR), which is based on the cognitive assumption that similar problems have similar solutions, is an important problem-solving and learning method in the field of artificial intelligence (AI). In this article, the development of CBR is reviewed, and the major challenges of CBR are summarized. The paper is organized into four parts. First, the basic framework and concepts of CBR are introduced. Then, the developed technology and innovative work that were designed to solve problems by CBR are summarized. Then, the application fields of CBR are summarized. Finally, according to the idea of deep learning and interpretable AI, the main challenges for the future development of CBR are proposed.

The impact of introducing deep learning based [18F]FDG PET denoising on EORTC and PERCIST therapeutic response assessments in digital PET/CT

Background[18F]FDG PET denoising by SubtlePET™ using deep learning artificial intelligence (AI) was previously found to induce slight modifications in lesion and reference organs’ quantification and in lesion detection. As a next step, we aimed to evaluate its clinical impact on [18F]FDG PET solid tumour treatment response assessments, while comparing “standard PET” to “AI denoised half-duration PET” (“AI PET”) during follow-up.Results110 patients referred for baseline and follow-up standard digital [18F]FDG PET/CT were prospectively included. “Standard” EORTC and, if applicable, PERCIST response classifications by 2 readers between baseline standard PET1 and follow-up standard PET2 as a “gold standard” were compared to “mixed” classifications between standard PET1 and AI PET2 (group 1; n = 64), or between AI PET1 and standard PET2 (group 2; n = 46). Separate classifications were established using either standardized uptake values from ultra-high definition PET with or without AI denoising (simplified to “UHD”) or EANM research limited v2 (EARL2)-compliant values (by Gaussian filtering in standard PET and using the same filter in AI PET). Overall, pooling both study groups, in 11/110 (10%) patients at least one EORTCUHD or EARL2 or PERCISTUHD or EARL2 mixed vs. standard classification was discordant, with 369/397 (93%) concordant classifications, unweighted Cohen’s kappa = 0.86 (95% CI: 0.78–0.94). These modified mixed vs. standard classifications could have impacted management in 2% of patients.ConclusionsAlthough comparing similar PET images is preferable for therapy response assessment, the comparison between a standard [18F]FDG PET and an AI denoised half-duration PET is feasible and seems clinically satisfactory.

Deep learning assisted quantitative analysis of Aβ and microglia in patients with idiopathic normal pressure hydrocephalus in relation to cognitive outcome.

Neuropathologic changes of Alzheimer disease (AD) including Aβ accumulation and neuroinflammation are frequently observed in the cerebral cortex of patients with idiopathic normal pressure hydrocephalus (iNPH). We created an automated analysis platform to quantify Aβ load and reactive microglia in the vicinity of Aβ plaques and to evaluate their association with cognitive outcome in cortical biopsies of patients with iNPH obtained at the time of shunting. Aiforia Create deep learning software was used on whole slide images of Iba1/4G8 double immunostained frontal cortical biopsies of 120 shunted iNPH patients to identify Iba1-positive microglia somas and Aβ areas, respectively. Dementia, AD clinical syndrome (ACS), and Clinical Dementia Rating Global score (CDR-GS) were evaluated retrospectively after a median follow-up of 4.4 years. Deep learning artificial intelligence yielded excellent (>95%) precision for tissue, Aβ, and microglia somas. Using an age-adjusted model, higher Aβ coverage predicted the development of dementia, the diagnosis of ACS, and more severe memory impairment by CDR-GS whereas measured microglial densities and Aβ-related microglia did not correlate with cognitive outcome in these patients. Therefore, cognitive outcome seems to be hampered by higher Aβ coverage in cortical biopsies in shunted iNPH patients but is not correlated with densities of surrounding microglia.

The Role of Artificial Intelligence in Predicting Optic Neuritis Subtypes From Ocular Fundus Photographs.

Optic neuritis (ON) is a complex clinical syndrome that has diverse etiologies and treatments based on its subtypes. Notably, ON associated with multiple sclerosis (MS ON) has a good prognosis for recovery irrespective of treatment, whereas ON associated with other conditions including neuromyelitis optica spectrum disorders or myelin oligodendrocyte glycoprotein antibody-associated disease is often associated with less favorable outcomes. Delay in treatment of these non-MS ON subtypes can lead to irreversible vision loss. It is important to distinguish MS ON from other ON subtypes early, to guide appropriate management. Yet, identifying ON and differentiating subtypes can be challenging as MRI and serological antibody test results are not always readily available in the acute setting. The purpose of this study is to develop a deep learning artificial intelligence (AI) algorithm to predict subtype based on fundus photographs, to aid the diagnostic evaluation of patients with suspected ON. This was a retrospective study of patients with ON seen at our institution between 2007 and 2022. Fundus photographs (1,599) were retrospectively collected from a total of 321 patients classified into 2 groups: MS ON (262 patients; 1,114 photographs) and non-MS ON (59 patients; 485 photographs). The dataset was divided into training and holdout test sets with an 80%/20% ratio, using stratified sampling to ensure equal representation of MS ON and non-MS ON patients in both sets. Model hyperparameters were tuned using 5-fold cross-validation on the training dataset. The overall performance and generalizability of the model was subsequently evaluated on the holdout test set. The receiver operating characteristic (ROC) curve for the developed model, evaluated on the holdout test dataset, yielded an area under the ROC curve of 0.83 (95% confidence interval [CI], 0.72-0.92). The model attained an accuracy of 76.2% (95% CI, 68.4-83.1), a sensitivity of 74.2% (95% CI, 55.9-87.4) and a specificity of 76.9% (95% CI, 67.6-85.0) in classifying images as non-MS-related ON. This study provides preliminary evidence supporting a role for AI in differentiating non-MS ON subtypes from MS ON. Future work will aim to increase the size of the dataset and explore the role of combining clinical and paraclinical measures to refine deep learning models over time.

Diagnostic Accuracy of an Integrated AI Tool to Estimate Gestational Age From Blind Ultrasound Sweeps

Accurate assessment of gestational age (GA) is essential to good pregnancy care but often requires ultrasonography, which may not be available in low-resource settings. This study developed a deep learning artificial intelligence (AI) model to estimate GA from blind ultrasonography sweeps and incorporated it into the software of a low-cost, battery-powered device. To evaluate GA estimation accuracy of an AI-enabled ultrasonography tool when used by novice users with no prior training in sonography. This prospective diagnostic accuracy study enrolled 400 individuals with viable, single, nonanomalous, first-trimester pregnancies in Lusaka, Zambia, and Chapel Hill, North Carolina. Credentialed sonographers established the "ground truth" GA via transvaginal crown-rump length measurement. At random follow-up visits throughout gestation, including a primary evaluation window from 14 0/7 weeks' to 27 6/7 weeks' gestation, novice users obtained blind sweeps of the maternal abdomen using the AI-enabled device (index test) and credentialed sonographers performed fetal biometry with a high-specification machine (study standard). The primary outcome was the mean absolute error (MAE) of the index test and study standard, which was calculated by comparing each method's estimate to the previously established GA and considered equivalent if the difference fell within a prespecified margin of ±2 days. In the primary evaluation window, the AI-enabled device met criteria for equivalence to the study standard, with an MAE (SE) of 3.2 (0.1) days vs 3.0 (0.1) days (difference, 0.2 days [95% CI, -0.1 to 0.5]). Additionally, the percentage of assessments within 7 days of the ground truth GA was comparable (90.7% for the index test vs 92.5% for the study standard). Performance was consistent in prespecified subgroups, including the Zambia and North Carolina cohorts and those with high body mass index. Between 14 and 27 weeks' gestation, novice users with no prior training in ultrasonography estimated GA as accurately with the low-cost, point-of-care AI tool as credentialed sonographers performing standard biometry on high-specification machines. These findings have immediate implications for obstetrical care in low-resource settings, advancing the World Health Organization goal of ultrasonography estimation of GA for all pregnant people. ClinicalTrials.gov Identifier: NCT05433519.

Artificial intelligence-assisted grading for tear trough deformity

BackgroundVarious classification systems for tear trough deformity (TTD) have been published, but their complexity can pose challenges in clinical use, especially for less experienced surgeons. It is believed that artificial intelligence (AI) technology can address some of these challenges by reducing inadvertent errors and improving the accuracy of medical practice. In this study, we aimed to establish a reliable and precise digital image grading model for TTD using smartphone-based photography, enhanced by AI deep learning technology. This model is designed to aid and guide surgeons, particularly those who are less experienced or from younger generations, during clinical examinations and in making decisions about further surgical interventions. Materials and MethodsA total of 504 patients and 983 photos were included in the study. We adopted the Barton’s grading system for TTD. All photos were taken with the same smartphone and processed and analyzed using the medical AI assistant (MAIATM) software. The photos were then randomly divided into two groups to establish training and testing models. ResultsThe confusion matrix for the training model demonstrated a sensitivity of 56%, specificity of 87.3%, an F1 score of 0.57, and an area under the curve (AUROC) of 0.85. For the testing group, the sensitivity was 49.3%, specificity was 85%, with an F1 score of 0.49, and an AUROC of 0.83. Representative heatmaps were also generated. ConclusionOur study is the first to demonstrate that tear trough deformities can be easily categorized using a built-in smartphone camera in conjunction with an AI deep learning program. This approach can reduce errors during clinical patient evaluations, particularly for less experienced practitioners.

Redefining a new frontier in alkaptonuria therapy with AI-driven drug candidate design via in- silico innovation.

A rare metabolic condition called alkaptonuria (AKU) is caused by a decrease in homogentisate 1,2 dioxygenase (HGO) activity due to a mutation in homogentisate dioxygenase (HGD) gene. Homogentisic acid is a byproduct of the catabolism of tyrosine and phenylalanine that darkens the urine and accumulates in connective tissues which causes an agonizing arthritis. Employing the use of deep learning artificial intelligence (AI) drug design, this study aims to alleviate the current toxicity of the AKU drugs currently inuse, particularly nitisinone, by utilizing the natural flavanolkaempferol molecule as a 4-hydroxyphenylpyruvate dioxygenase inhibitor. Kaempferol was employed to generate three effective de novo drug candidates targeting the enzyme4-hydroxyphenylpyruvate dioxygenase using an AI drug design tool. We present novel AIK formulations in the present study. The AIK's (Artificial Intelligence Kaempferol) examination of drug-likeliness among the three led to its choice as a possible target. The toxicity assessment research ofAIK demonstrates that it is not only safer to use than othertreatments, but also more efficient. The docking of theAIGT with 4-hydroxyphenylpyruvate dioxygenase, whichrevealed a binding affinity of around-9.099 kcal/mol, highlights the AIK's potential as a therapeutic candidate. Aninnovative approach to deal with challenging circumstances is thus presented in this study by new formulations kaempferol that have been meticulously designed by AI. The results of the invitro tests must be confirmed invivo, even though AI-designed AIK is effective and sufficiently safe as computed.