Engineering Medicine Research Articles

Multisystem health comorbidity networks of metabolic dysfunction-associated steatotic liver disease

The global burden of metabolic dysfunction-associated steatotic liver disease (MASLD) is growing, but its subsequent health consequences have not been thoroughly examined. A phenome-wide association study was conducted to map the associations of MASLD with 948 unique clinical outcomes among 361,021 Europeans in the UK Biobank. Disease trajectory and comorbidity analyses were applied to visualize the sequential patterns of multiple comorbidities related to the occurrence of MASLD. The associations jointly verified by observational and polygenic phenome-wide analyses were further replicated by two-sample Mendelian randomization analysis using data from the FinnGen study and international consortia. The observational and polygenic phenome-wide association study revealed the associations of MASLD with 96 intrahepatic and extrahepatic diseases, including circulatory, metabolic, genitourinary, neurological, gastrointestinal, and hematologic diseases. Sequential patterns of MASLD-related extrahepatic comorbidities were primarily found in circulatory, metabolic, and inflammatory diseases. Mendelian randomization analyses supported the causal associations between MASLD and the risk of several intrahepatic disorders, metabolic diseases, cardio-cerebrovascular disease, and ascites but found no associations with neurological diseases. This study elucidated multisystem comorbidities and health consequences of MASLD, contributing to the development of combination interventions targeting distinct pathways for health promotion among patients with MASLD. X.L. was funded by the Natural Science Fund for Distinguished Young Scholars of Zhejiang Province (LR22H260001) and the National Nature Science Foundation of China (82204019) and Y.D. was funded by the Key Project of Traditional Chinese Medicine Science and Technology Plan of Zhejiang Province (GZY-ZJ-KJ-24077) and the National Natural Science Foundation of China (82001673 and 82272860).

Identification, Characterization, and Antibacterial Evaluation of Five Endophytic Fungi from Psychotria poeppigiana Müll. Arg., an Amazon Plant.

Endophytic fungi, residing within plants without causing disease, are known for their ability to produce bioactive metabolites with diverse properties such as antibacterial, antioxidant, and antifungal activities, while also influencing plant defense mechanisms. In this study, five novel endophytic fungi species were isolated from the leaves of Psychotria poeppigiana Müll. Arg., a plant from the Rubiaceae family, collected in the tropical Amazon region of Bolivia. The endophytic fungi were identified as a Neopestalotiopsis sp., three Penicillium sp., and an Aspergillus sp. through 18S ribosomal RNA sequencing and NCBI-BLAST analysis. Chemical profiling revealed that their extracts obtained by ethyl acetate contained terpenes, flavonoids, and phenolic compounds. In a bioautography study, the terpenes showed high antimicrobial activity against Escherichia coli. Notably, extracts from the three Penicillium species exhibited potent antibacterial activity, with minimum inhibitory concentration (MIC) values ranging from 62.5 to 2000 µg/mL against all three pathogens: Escherichia coli, Staphylococcus aureus, and Enterococcus faecalis (both Gram-positive and Gram-negative bacteria). These findings highlight the potential of these endophytic fungi, especially Penicillium species as valuable sources of secondary metabolites with significant antibacterial activities, suggesting promising applications in medicine, pharmaceuticals, agriculture, and environmental technologies.

Three-Dimensional Analysis of the Maxillary Sinuses in Ancient Crania Dated to the V-VI Centuries BCE from Opi (Italy): Volumetric Measurements in Ancient Skulls from the Necropolis of Opi, Abruzzi, Italy.

Cone beam computed tomography (CBCT) can provide precise information about complex anatomical structures as it is characterised by rapid volumetric image acquisition with high resolution. The aim of this study was to provide measurements for 20 ancient skulls of the Samnite people found in the necropolis of Opi, a small and isolated mountain village in Abruzzo, a region in central Italy. All the images (left and right) of the 20 ancient skulls from Opi were acquired. All the data are the property of the Department of Innovative Technologies in Medicine and Dentistry of G. D'Annunzio University, Chieti, and different parameters (area and volume) were measured and evaluated. The mean and standard deviation of the facial measurements were also calculated. All the data were subjected to statistical analysis. CBCT scan data of 20 fossil skulls did not show significant values regarding the MS area and the volume between the right and left sides. In the ancient skulls, no difference was found between the right and left sides.

Commercial Diagnostics and Emerging Precision Medicine Technologies in Psoriasis and Atopic Dermatitis.

While psoriasis and atopic dermatitis (AD) are two common dermatological conditions, their diagnosis and therapeutic decision-making pathways are often complex. As a result, there has been increased focus on the development of precision medicine approaches for psoriasis and AD. Two companies at the forefront of dermatology precision medicine research are Mindera Health and Castle Biosciences. Here, we review the technologies developed by these two companies using a dermal diagnostic patch and superficial skin scrapings, respectively, their research published to date, and their future research goals. Research from both companies shows promise in predicting the response of inflammatory skin disease to biologics using minimally invasive techniques. However, challenges to adoption include insurance coverage and patient trust in the technologies. While there are several differences between Mindera Health and Castle Biosciences, they have a shared goal of utilizing minimally invasive technologies to sample skin and predict response to biologic treatments using a panel of optimized biomarkers.

Family Planning in Genetic Optic Atrophies in Israel, a Case Series and a Discussion of Ethical Considerations.

Patients with genetic optic atrophies must navigate all stages of life with their visual impairment, including the important milestone of family planning. Advances in genetic testing now allows physicians and affected families to consider medical help with the aim of preventing blindness through preconception, preimplantation, and perinatal methods. This case series presents 4 patients with different genetic optic atrophies (Leber hereditary optic neuropathy [LHON], autosomal dominant optic atrophy, Wolfram syndrome, and papillorenal syndrome) who were followed by the Neuro-Ophthalmology Unit at a tertiary medical center between 2010 and 2023 and were of child-bearing age. The aim of this study was to increase understanding in family planning options for patients with optic atrophies, raise awareness of the solutions available, and provide guidance for clinicians to support their patients. Advances in medicine, genetics, and medical technology allow multidisciplinary teams to assist patients in fulfilling their desire for a genetically healthy offspring. Customized solutions can be designed to meet the specific challenges posed by each type of genetic optic atrophy. The solutions proposed in this series are based on genetic testing done in the parents, which then allows to plan medical and genetic intervention individually. The solutions opted for in this series range from the decision to not have another child until PGD (Preimplantation genetic diagnosis). We describe how genetic advancements have made it possible for patients with the 4 most common hereditary optic atrophies to fulfill their wish to have children without visually threatening genetic mutations. We also review the recent literature on the penetrance of optic atrophy in OA-mutation carriers and raise 2 significant ethical considerations: the reduction of a future life to a non-life-threatening impairment and that of public expenditure for non-life-threatening conditions.

Adjunctive Testing Using Biospectral Emission Sequencing: Bioregulatory Intelligence Technology in Parallel With the Goals of Artificial Intelligence in Medicine.

The many advancements in medical technology of the last century have continually sought to improve the sensitivity of testing and the specificity of treatment of human maladies. Conventional physical and pharmaceutical treatment is largely an imprecise process, stimulating the impetus for the advancement of machine learning-enhanced artificial intelligence (AI) medical technologies. Biospectral Emission Sequencing (BES) is a bioregulatory intelligence (BI) technology already in use as an adjunct to conventional testing. Biospectral Emission Sequencing provides a functional system of dynamic real-time adjunctive testing and treatment selection. This paper discusses the parallel technologies of present and future AI and BI technologies in medicine.

Ultrathin Boron Growth onto Nanodiamond Surfaces via Electrophilic Boron Precursors.

Diamond as a templating substrate is largely unexplored, and the unique properties of diamond, including its large bandgap, thermal conductance, and lack of cytotoxicity, makes it versatile in emergent technologies in medicine and quantum sensing. Surface termination of an inert diamond substrate and its chemical reactivity are key in generating new bonds for nucleation and growth of an overlayer material. Oxidized high-pressure high temperature (HPHT) nanodiamonds (NDs) are largely terminated by alcohols that act as nucleophiles to initiate covalent bond formation when an electrophilic reactant is available. In this work, we demonstrate a templated synthesis of ultrathin boron on ND surfaces using trigonal boron compounds. Boron trichloride (BCl3), boron tribromide (BBr3), and borane (BH3) were found to react with ND substrates at room temperature in inert conditions. BBr3 and BCl3 were highly reactive with the diamond surface, and sheet-like structures were produced and verified with electron microscopy. Surface-sensitive spectroscopies were used to probe the molecular and atomic structure of the ND constructs' surface, and quantification showed the boron shell was less than 1 nm thick after 1-24 h reactions. Observation of the reaction supports a self-terminating mechanism, similar to atomic layer deposition growth, and is likely due to the quenching of alcohols on the diamond surface. X-ray absorption spectroscopy revealed that boron-termination generated midgap electronic states that were originally predicted by density functional theory (DFT) several years ago. DFT also predicted a negative electron surface, which has yet to be confirmed experimentally here. The boron-diamond nanostructures were found to aggregate in dichloromethane and were dispersed in various solvents and characterized with dynamic light scattering for future cell imaging or cancer therapy applications using boron neutron capture therapy (BNCT). The unique templating mechanism based on nucleophilic alcohols and electrophilic trigonal precursors allows for covalent bond formation and will be of interest to researchers using diamond for quantum sensing, additive manufacturing, BNCT, and potentially as an electron emitter.

Epigenome editing technologies for discovery and medicine.

Epigenome editing has rapidly evolved in recent years, with diverse applications that include elucidating gene regulation mechanisms, annotating coding and noncoding genome functions and programming cell state and lineage specification. Importantly, given the ubiquitous role of epigenetics in complex phenotypes, epigenome editing has unique potential to impact a broad spectrum of diseases. By leveraging powerful DNA-targeting technologies, such as CRISPR, epigenome editing exploits the heritable and reversible mechanisms of epigenetics to alter gene expression without introducing DNA breaks, inducing DNA damage or relying on DNA repair pathways.

AT THE CROSSROADS OF TECHNOLOGY AND MEDICINE: PROSPECTS OF AUTOMATION IN MEDICAL PRACTICE WITH THE USE OF NEURAL NETWORKS

This article delves into the exciting possibilities that arise when technology is applied to the field of medicine. It explores how automation can streamline various processes, leading to more efficient and accurate healthcare. One focus area is the potential for replacing direct interaction between doc-tors and patients with automated systems. By utilizing advanced neural networks, diagnostic and treatment processes can becomautomated, leading to faster and more precise outcomes. For exam-ple, AI-powered systems can analyze medical images and detect abnormalities with incredible accu-racy, aiding doctors in making diagnoses. Additionally, virtual assistants and chat bots can provide personalized healthcare advice, reducing the need for in-person consultations for minor concerns. These advancements not only enhance efficiency but also have potential to improve access to healthcare, especially in remote areas. Embracing technology in medicine opens up a lot of possibilities for better patient care and overall healthcare outcomes.

Considerations for applying emerging technologies in paediatric laboratory medicine.

Emerging technology in laboratory medicine can be defined as an analytical method (including biomarkers) or device (software, applications, and algorithms) that by its stage of development, translation into broad routine clinical practice, or geographical adoption and implementation has the potential to add value to clinical diagnostics. Paediatric laboratory medicine itself may be considered an emerging area of specialisation that is established relatively recently following increased appreciation and understanding of the unique physiology and healthcare needs of the children. Through four clinical (neonatal hypoglycaemia, neonatal hyperbilirubinaemia, sickle cell disorder, congenital adrenal hyperplasia) and six technological (microassays, noninvasive testing, alternative matrices, next generation sequencing, exosome analysis, machine learning) illustrations, key takeaways of application of emerging technology for each area are summarised. Additionally, nine key considerations when applying emerging technology in paediatric laboratory medicine setting are discussed.

HTA model for laboratory medicine technologies: overview of approaches adopted in some international agencies.

The Health Technology Assessment (HTA) Working Group of the Emerging Technology Division of International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) aims to develop a methodological approach for producing structured HTA information for laboratory medicine technologies. This approach seeks to support decision-making processes at the country, regional, and/or hospital levels regarding the introduction of specific technologies. The focus of this model will primarily be ondefining assessment elements within the domains of 'organizational aspects' and 'costs and economic evaluations', potentially differentiated by the type of diagnostic technology (e.g., genetic tests, molecular tests). To achieve this project's goal, a literature review and examination ofwebsites of international HTA agencies have been conducted. The research aims to identify multidisciplinary methodological approaches used to assess laboratory diagnostic technologies and to pinpoint the domains and assessment elements utilized. Wefound 7 methodological articles describing methodological approaches adopted toassess laboratory diagnostic technologies. Among the HTA organizations considered, 23 reports were found, of which 7 were produced by the European Network of HTA (EUnetHTA), 4 by the National Institute for Health and Care Excellence Diagnostic Assessment Program (NICE DAP), and 12 by other HTA agencies. The EUnetHTA reports wererapid collaborative assessments covering various domains, while the NICE DAP reports focused on diagnostic guidances, including descriptions of technologies, clinical need and practice, diagnostic tests, accuracy, effectiveness, and cost-effectiveness. Finally, a survey targeting laboratory professionals will be conducted to introduce assessment elements, differentiated by the type of diagnostic technology, primarily for organizational and economic domains.

Влияние современных симуляционных тренажеров на развитие навыков командной работы: согласованных действий и коммуникации

<p style="text-align: justify;">The article presents a review of a number of modern foreign studies of simulators and their role in the development of teamwork skills. The analysis of some trending learning theories and methodologies, which are used by both national and foreign researchers in the implementation of training programs on simulators of professional activity is carried out. The results of studies demonstrating the influence of computer simulators on the development of teamwork skills, including aspects of coordinated actions and effective communication are presented. Various methods and means used by foreign researchers to improve the effectiveness of simulation-based training are described. The importance of using simulation-based training in various professional fields, such as medicine, aviation, space programs and information technology, to ensure a high level of training of specialists is substantiated. It is shown that modern simulation-based simulators offer training methods that are maximally close to real professional activity, which contributes to the formation of highly qualified specialists in professional fields that require high development of teamwork skills.</p>

Cognitive Brain of Homo sapiens: Stress, Emotions, Health, Hormones, Longevity

Replication of new discoveries in the field of genetics and epigenetics, the scientific union of neurobiology and neurophysiology of the quantum brain HOMO SAPIENS of the 21st century, the achievements of biophysics, hybrid neuroimaging and nuclear medicine, the introduction of nutritionology and bioelementology technologies, the 5P of MEDICINE and 5G TECHNOLOGY, compliance of art medicine and modern psychoanalysis, - will allow control the cognitive brain of a 21st century HUMAN. Scientific progress and 33 years of experimental experience of biophysics in the field of neurogeriatrics and neurocardiology, neuroendocrinology and psychoneuroimmunology - made it possible to argue that "nerve cells are restored," the quantum human brain is CONTROLLED, high-quality and healthy LONGEVITY HOMO SAPIENS of the 21st century is achievable. Children who experience chronic stressors are vulnerable to lifelong emotional and physical health problems. Cognitive and mental health affects a person's ability to cope with stress, maintain a relationship paradigm, make healthy choices and lead a fulfilling life. An increase in the expected and average life expectancy of a person is realizable/achievable/feasible when creating conditions/tools/algorithms/technologies for active and high-quality longevity.

Heterogenous biological network multi-task learning model for ncRNA-disease-drug association prediction

Traditional medical research is characterised by lengthy duration, significant financial investment, and substantial risk of failure. In response to these challenges, network medicine, combined with medicine and computer technology, has become an important development direction, and computational methods have been proposed to predict potential associations. However, most of the current computational methods focus on single-potential association prediction tasks, which face issues of association sparsity and weak generalisation ability. To address these challenges, we developed a heterogeneous biological network multi-task learning model (HBNMM). Unlike previous methods based on bipartite graphs, HBNMM constructs a complex heterogeneous biological network, including ncRNA-disease-drug association networks and diverse similarity networks. HBNMM applies graph attention networks to aggregate node neighbourhood information and acquire node feature embeddings, and is then trained with a multi-task learning strategy to simultaneously predict potential ncRNA-disease, ncRNA-drug, and drug-disease associations. As a result, the HBNMM achieves an excellent performance that is higher than that of the state-of-the-art models. Furthermore, five case studies supported by experiments showed powerful predictive ability for drug discovery and disease treatment.

The Influence of Three-Dimensionally Printed Polymer Materials as Trusses and Shell Structures on the Mechanical Properties and Load-Bearing Capacity of Reinforced Concrete.

Three-dimensional printing technology (3D printing) is becoming a more and more popular technology for aerospace, biology, medicine, mechanics, civil and other engineering fields. In civil engineering, researchers and engineers attempt to print construction materials such as concrete using 3D-printing technology. This study aims to investigate the reinforcement of concrete beams with 3D printing. To achieve this, fused deposition modeling (FDM) technology as a printing method and polylactic acid (PLA) as a material were selected. Two types of geometries were chosen to find the optimal mechanical behavior of concrete: high-performance concrete (HPC) reinforced with four types of trusses (Pratt, Howe, Warren, and Warren with vertical) and ultra-high-performance concrete (UHPC) reinforced with a hyperboloid shell structure. The compressive and tensile strengths of reinforced UHPC were examined by a three-point bending test, and reinforced HPC was examined by a four-point bending test. The results of the experiments show that hyperboloid shell structures can absorb energy, although the strength of reinforced UHPC is reduced. For example, there was a decrease of over 20% in the compressive strength and 41% in the flexural strength, but the ductility was raised. Adding the hyperboloid shell structure improved the deformability of the UHPC. When Warren and Howe trusses were added to the HPC as reinforcements, the flexural strength improved by over 26% and 4.3%, respectively. The overall results of this study show that the concrete reinforced with 3D-printed trusses was better than that with a hyperboloid shell structure.

Technologies of regenerative medicine as a method of treatment of male infertility: literature review

The problem of infertility has recently become quite widespread all over the world, having a negative impact on couples themselves and society as a whole. The male factor of infertility accounts for about 50% of cases, in this regard, a large number of studies conducted are aimed at finding new methods of treating this disease. However, the accepted modern methods of treatment, including assisted reproductive technologies, are currently not effective enough. Recently, much attention has been devoted to the use of regenerative medicine technologies in the treatment of diseases of the reproductive system of men and women due to its significant potential. This review presents existing experimental and clinical studies that studied the effect, safety and efficacy of stem cells of various kinds of origin, induced pluripotent stem cells, exosomes, spermatogonial stem cells, platelet rich plasma in the treatment of various types of male infertility.

P-290 Isolation and characterization of extracellular vesicles from human endometrial organoids

Abstract Study question Do human endometrial organoids produce extracellular vesicles (EVs)? Summary answer Endometrial organoids release EVs (ORG-EVs) that we successfully and comprehensively characterize according to guidelines. What is known already EVs play a crucial role in cellular communication, garnering increasing attention in physiological and pathological processes. Although the enormous potential of EVs has been extensively demonstrated across a large variety of research fields, the promise held by EVs derived from human endometrial organoids is still largely unexplored. Indeed, nowadays there is limited research available on the presence, role, and function of EVs derived from organoids of female reproductive tissue samples. This underscores the importance of adopting standardized methods for isolating, characterizing, and reporting findings, which is crucial for advancing in this promising and innovative, yet challenging, research field. Study design, size, duration This study involved establishing organoid cultures from endometrial biopsies of healthy women (n = 10) of reproductive age, who underwent ovarian surgery between January and October 2023. ORG-EVs were isolated using differential ultracentrifugation. Characterization of ORG-EVs, including assessment of their biophysical and biochemical properties, adhered to guidelines established by the International Society for Extracellular Vesicles (ISEV). Participants/materials, setting, methods Endometrial specimens from 10 women were employed to establish organoid cultures. ORG-EVs were isolated from spent medium through differential ultracentrifugation. Morphology, size distribution and concentration analysis were performed using Transmission Electron Microscopy (TEM) and Nanoparticle Tracking Analysis (NTA). Western blotting was used to confirm the presence of EVs markers. Main results and the role of chance TEM analysis confirmed the presence of isolated ORG-EVs, elucidating their morphology and structural integrity. NTA revealed that ORG-EVs had an average size of 160.4 ± 6.2 nm (mean ± SD) and a mean concentration of 5.2E+11 ± 2.1E+10 particles/ml (mean ± SD). Western blot analysis further confirmed the vesicular nature of the assessed particles, demonstrating positivity for standard EV markers such as tetraspanins (CD63 and CD9), and soluble proteins (ALIX and TSG101), while excluding the endoplasmic reticulum-resident protein calnexin (negative control). Additionally, ORG-EVs exhibited a positive signal for mucin1 (MUC1), a glycoprotein present on the surface of endometrial epithelial cells, thereby confirming their cellular origin. Limitations, reasons for caution This is a pilot study, characterized by a small sample size requiring larger cohorts to validate results and, at the same time, to undertake deeper exploration of ORG-EVs’ molecular cargo. Emphasizing the critical need for standardized isolation and characterization methods is paramount. Wider implications of the findings Endometrial organoids enhance EVs production, providing a powerful tool for reproductive aspects studies, and improving regenerative medicine and artificial reproductive technology. These initial findings propel further exploration, unveiling of ORG-EVs’ molecular cargo aiming to elucidate their potential role in human female reproductive tissues and mechanism related to embryo implantation process. Trial registration number not applicable

Laboratory Preparation for Digital Medicine in Healthcare 4.0: An Investigation Into the Awareness and Applications of Big Data and Artificial Intelligence.

Healthcare 4.0. refers to the integration of advanced technologies, such as artificial intelligence (AI) and big data analysis, into the healthcare sector. Recognizing the impact of Healthcare 4.0 technologies in laboratory medicine (LM), we seek to assess the overall awareness and implementation of Healthcare 4.0 among members of the Korean Society for Laboratory Medicine (KSLM). A web-based survey was conducted using an anonymous questionnaire. The survey comprised 36 questions covering demographic information (seven questions), big data (10 questions), and AI (19 questions). In total, 182 (17.9%) of 1,017 KSLM members participated in the survey. Thirty-two percent of respondents considered AI to be the most important technology in LM in the era of Healthcare 4.0, closely followed by 31% who favored big data. Approximately 80% of respondents were familiar with big data but had not conducted research using it, and 71% were willing to participate in future big data research conducted by the KSLM. Respondents viewed AI as the most valuable tool in molecular genetics within various divisions. More than half of the respondents were open to the notion of using AI as assistance rather than a complete replacement for their roles. This survey highlighted KSLM members' awareness of the potential applications and implications of big data and AI. We emphasize the complexity of AI integration in healthcare, citing technical and ethical challenges leading to diverse opinions on its impact on employment and training. This highlights the need for a holistic approach to adopting new technologies.

Explainable coronary artery disease prediction model based on AutoGluon from AutoML framework.

This study focuses on the innovative application of Automated Machine Learning (AutoML) technology in cardiovascular medicine to construct an explainable Coronary Artery Disease (CAD) prediction model to support the clinical diagnosis of CAD. This study utilizes a combined data set of five public data sets related to CAD. An ensemble model is constructed using the AutoML open-source framework AutoGluon to evaluate the feasibility of AutoML in constructing a disease prediction model in cardiovascular medicine. The performance of the ensemble model is compared against individual baseline models. Finally, the disease prediction ensemble model is explained using SHapley Additive exPlanations (SHAP). The experimental results show that the AutoGluon-based ensemble model performs better than the individual baseline models in predicting CAD. It achieved an accuracy of 0.9167 and an AUC of 0.9562 in 4-fold cross-bagging. SHAP measures the importance of each feature to the prediction of the model and explains the prediction results of the model. This study demonstrates the feasibility and efficacy of AutoML technology in cardiovascular medicine and highlights its potential in disease prediction. AutoML reduces the barriers to model building and significantly improves prediction accuracy. Additionally, the integration of SHAP enhances model transparency and explainability, which is critical to ensuring model credibility and widespread adoption in cardiovascular medicine.

Enhancing Clinical Diagnosis With Convolutional Neural Networks: Developing High-Accuracy Deep Learning Models for Differentiating Thoracic Pathologies.

Background The use of computational technology in medicine has allowed for an increase in the accuracy of clinical diagnosis, reducing errors through additional layers of oversight. Artificial intelligence technologies present the potential to further augment and expedite the accuracy, quality, and efficiency at which diagnosis can be made when used as an adjunctive tool. Such techniques, if found to be accurate and reliable in their diagnostic acuity, can be implemented to foster better clinical decision-making, improving patient quality of care while reducing healthcare costs. Methodology This study implemented convolution neural networks to develop a deep learning model capable of differentiating normal chest X-rays from those indicating pneumonia, tuberculosis, cardiomegaly, and COVID-19. There were 3,063 normal chest X-rays, 3,098 pneumonia chest X-rays, 2,920 COVID-19 chest X-rays, 2,214 chest X-rays, and 554 tuberculosis chest X-rays from Kaggle that were used for training and validation.The model was trained to recognize patterns within the chest X-rays to efficiently recognize these diseases within patients to be treated on time. Results The results indicated a success rate of 98.34% incorrect detections, exemplifying a high degree of accuracy. There are limitations to this study. Training models require hundreds to thousands of samples, and due to potential variability in image scanning equipment and techniques from which the images are sourced, the model could have learned to interpret external noise and unintended details which can adversely impact accuracy. Conclusions Further studies that implement more universal database-sourced images with similar image scanning techniques, assess diverse but related medical conditions, and the utilization of repeat trials can help assess the reliability of the model.These results highlight the potential of machine learning algorithms for disease detection with chest X-rays.