Algorithm For Early Detection Research Articles

Machine Learning Algorithms for Early Detection of Legume Crop Disease

Background: Legume crops are an essential component of global agriculture and are widely supplied for human consumption, livestock feed and soil improvement due to their vital nutritional nature. The economic and nutritional significance of legumes is threatened by a multitude of diseases that can cause substantial yield losses. Traditional methods for disease detection, relying on visual inspection, are often subjective and inefficient, leading to delayed intervention. Methods: This study investigates the utilization of machine learning algorithms for the early identification of diseases affecting legume crops. A comprehensive evaluation is conducted on machine learning algorithms, namely Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) with respect to the domain of disease detection. Through a comparative analysis of their performance across different environmental conditions and phases of crop development, this study also explores their strengths and weaknesses. Result: The findings and comparative examination offered significant perspectives on the potential of machine learning algorithms in the realm of early legume crop disease detection. In addition to enhancing crop health and disease management, the research provides support for sustainable agricultural practices and possesses the capacity to augment environmental sustainability and food security through the application of machine learning techniques.

Algorithms for Early Detection of Silent Liver Fibrosis in the Primary Care Setting.

More than one-third of the adult world population has steatotic liver disease (SLD), with a few percent of individuals developing cirrhosis after decades of silent liver fibrosis accumulation. Lack of systematic early detection causes most patients to be diagnosed late, after decompensation, when treatment has limited effect and survival is poor. Unfortunately, no isolated screening test in primary care can sufficiently predict advanced fibrosis from SLD. Recent efforts, therefore, combine several parameters into screening algorithms, to increase diagnostic accuracy. Besides patient selection, for example, by specific characteristics, algorithms include nonpatented or patented blood tests and liver stiffness measurements using elastography-based techniques. Algorithms can be composed as a set of sequential tests, as recommended by most guidelines on primary care pathways. Future use of algorithms that are easy to interpret, cheap, and semiautomatic will improve the management of patients with SLD, to the benefit of global health care systems.

Predictive Maintenance Approach in Ventricular Assist Devices: Safeguarding Against Thrombus Formation

Affecting millions in the world, cardiovascular diseases are a public health problem. Some patients are not eligible for heart transplantation. Thus, a possibility is to receive a circulatory device known as a ventricular assist device (VAD). This kind of device shows some problems, like thrombogenesis. The thrombus formation in a VAD can cause patient death, and a previous, non-invasive diagnostic is quite complex. The objective of this work is to develop an algorithm to reproduce time signals that indicate the presence and absence of a thrombus, use these signals to train an artificial neural network to classify them, and use these algorithms in a predictive algorithm for early thrombus detection. The results show that it was possible to detect the thrombus formation in its early stages, but the noise level interferes with the accuracy of the ANN, especially when signals in the time domain are used.

The role of medical and organizational measures in improving the quality of early detection and clinical examination of autoimmune thyroiditis among the population of the Republic of Belarus

Objective. To evaluate the role of medical and organizational measures in improving the quality of early detection and medical examination of autoimmune thyroiditis in the population of the Republic of Belarus.Materials and methods. The data of population statistics and the State register of persons affected by the Chernobyl catastrophe and other accidents on cases of thyroid pathology, as well as outpatient records of patients were analyzed. Epidemiological, statistical and sociological methods were used.Results. It was noted that different clinical and laboratory variants of autoimmune thyroiditis (AIT) are encountered in clinical practice. Subclinical AIT with sonographic signs of the disease but without diagnostic concentrations of antibodies to thyroid peroxidase (AB/TPO) and disturbances of its function is registered in 14.1 (6.0-24.4) %. AIT with diagnostic concentrations of autoantibodies to AB/TPO, with preserved thyroid function or hypothyroidism occurs in 22.5 (13.534.0) % of the cohort subjects. The frequency of combining AIT with nodular neoplasms of the thyroid increased by the 4th stage of screening and reached 20.7 (11.2-33.4) %.The reasons for different approaches to diagnosis were analyzed and measures to improve organizational and medical measures for early detection and dispensary treatment of patients with AIT were proposed: groups of persons at high risk of AIT and primary hypothyroidism (PHT) were identified, an algorithm for early detection and dispensary treatment was developed, etc.Conclusion. When making a decision to improve the quality of medical care to the population of the Republic of Belarus on early detection and medical examination of patients with AIT, it is necessary to take into account the timeliness of thyroid status studies in the population, compliance with the terms of dynamic observation for persons at high risk of AIT and PHT development, peculiarities of clinical and laboratory variants of AIT course.

German Porphyria Registry (PoReGer)-Background and Setup.

Porphyrias, as most rare diseases, are characterized by complexity and scarcity of knowledge. A national registry in one of the largest European populations that prospectively collects longitudinal clinical and laboratory data are an important and effective tool to close this gap. The German Porphyria Registry (PoReGer) was founded by four centers with longstanding expertise in the field of porphyrias and rare diseases (Charité-Universitätsmedizin Berlin, Porphyria Center Saxony Chemnitz, University Medical Center Hamburg-Eppendorf, University Medical Center Göttingen) and the German reference laboratory for porphyria, and is supported by the largest German porphyria patient organization. A specified data matrix for three subgroups (acute, chronic blistering cutaneous, acute non-blistering cutaneous) includes data on demographics, specific porphyria-related symptoms, clinical course, general medical history, necessary follow-up assessments (including laboratory and imaging results), symptomatic and disease-modifying therapies, and side-effects. Additionally, the registry includes patient-reported outcome measures on quality of life, depression, and fatigue. PoReGer aims to broaden and deepen the understanding on all porphyria-related subjects. We expect these data to significantly improve the management and care of porphyria patients. Additionally, the data can be used for educational purposes to increase awareness, for the planning of healthcare services, and for machine learning algorithms for early detection of porphyrias.

Exploring Binary Classification Models for Parkinson’s Disease Detection

Parkinson’s disease (PD) is a degenerative condition characterised by a combination of motor symptoms, such as tremors and rigidity, and non-motor symptoms, including cognitive impairment and anxiety. Although there is currently no known cure, there are available treatments that can effectively mitigate symptoms and enhance life expectancy. Our research primarily centred around vocal disorders, given that an estimated 90% of individuals diagnosed with Parkinson’s disease experience the onset of such disorders in the early stages of the disease. In this study, a dataset is collected from UCI repository consisting of 252 cases was utilised, with 188 cases classified as Parkinson’s disease and 64 cases classified as non-Parkinson’s disease. The results of our study indicate that the K-nearest neighbours (KNN) algorithm demonstrated a peak accuracy of 98.52% and an average accuracy of 97.33%, surpassing the performance of previously established models. Additionally, we introduced the implementation of Parzen Windows Estimation (a KDE technique) and computed classifier accuracies based on mean test set scores after multiple rounds of data reshuffling and model training. Our results enhance credibility compared to previous works in this field. Given the high misdiagnosis rate (26%) and the expense of imaging tests, deploying a precise and cost-effective algorithm for early PD detection is critical.

Impact of Sodium-Glucose Cotransporter-2 Inhibitors in the Management of Chronic Kidney Disease: A Middle East and Africa Perspective.

Chronic kidney disease (CKD) is a major public health concern in the Middle East and Africa (MEA) region and a leading cause of death in patients with type 2 diabetes mellitus (T2DM) and hypertension. Early initiation of sodium-glucose cotransporter - 2 inhibitors (SGLT-2i) and proper sequencing with renin-angiotensin-aldosterone system inhibitors (RAASi) in these patients may result in better clinical outcomes due to their cardioprotective properties and complementary mechanisms of action. In this review, we present guideline-based consensus recommendations by experts from the MEA region, as practical algorithms for screening, early detection, nephrology referral, and treatment pathways for CKD management in patients with hypertension and diabetes mellitus. This study will help physicians take timely and appropriate actions to provide better care to patients with CKD or those at high risk of CKD.

A-295 - Use of 3-dimensional total body photography and digital dermoscopy with artificial intelligence algorithms for early melanoma detection in high-risk patients

A-295 - Use of 3-dimensional total body photography and digital dermoscopy with artificial intelligence algorithms for early melanoma detection in high-risk patients

Cross-attention-based saliency inference for predicting cancer metastasis on whole slide images.

Although multiple instance learning (MIL) methods are widely used for automatic tumor detection on whole slide images (WSI), they suffer from the extreme class imbalance WSIs containing small tumors where the tumor may include only a few isolated cells. For early detection, it is important that MIL algorithms can identify small tumors. Existing studies have attempted to address this issue using attention-based architectures and instance selection-based methodologies but have not produced significant improvements. This paper proposes crossattention-based salient instance inference MIL (CASiiMIL), which involves a novel saliency-informed attention mechanism to identify small tumors (e.g., breast cancer lymph node micro-metastasis) on WSIs without needing any annotations. In addition to this new attention mechanism, we introduce a negative representation learning algorithm to facilitate the learning of saliencyinformed attention weights for improved sensitivity on tumor WSIs. The proposed model outperforms the state-ofthe-art MIL methods on two popular tumor metastasis detection datasets. The proposed approach demonstrates great cross-center generalizability, high accuracy in classifying WSIs with small tumor lesions, and excellent interpretability attributed to the saliency-informed attention weights. We expect that the proposed method will pave the way for training algorithms for early tumor detection on large datasets where acquiring fine-grained annotations is is not practical.

Exploring the effectiveness of machine learning algorithms for early detection of Type-2 Diabetes Mellitus

Type 2 Diabetes Mellitus (T2DM) poses a significant global health challenge, greatly impacting the well-being and longevity of individuals. Detecting T2DM early on is of utmost importance as it prevents or delays associated complications. This research study seeks to assess the effectiveness of employing ma-chine learning algorithms for the early identification of T2DM. A classification model is constructed utilizing a dataset comprising T2DM-diagnosed patients and healthy controls, incorporating advanced feature selection techniques. The model is subjected to rigorous training and testing, utilizing machine learning algorithms such as Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forest, and Support Vector Machines. The results demonstrate that the Random Forest algorithm achieves an impressive accuracy of 98 % in detecting T2DM. This remarkable accuracy underscores the immense potential of machine learning algorithms in facilitating the early detection of T2DM. It emphasizes the significance of integrating these methods into the clinical decision-making process. The findings from this study will significantly contribute to developing an enhanced precision medicine screening process for T2DM, empowering healthcare providers to identify the disease at its nascent stages, thereby leading to improved patient outcomes.

Detection of early bruises on apples using near‐infrared camera imaging technology combined with adaptive threshold segmentation algorithm

AbstractApples are highly susceptible to bruising during picking, packing, and transportation, and early detection of apple bruises allows timely screening and treatment to reduce food safety risks. However, early bruises on apples, especially bruises of 30 min or less, with similar appearance to sound tissue, making manual sorting difficult. To address this problem, a near‐infrared (NIR) camera imaging technology combined with adaptive threshold segmentation algorithm for early apple bruise detection is proposed in this article. First, a NIR camera imaging system is built to acquire full‐band images of fruit face, fruit stem face and calyx face of sound apples and apples with single bruise and multiple bruises at 30 min of occurrence. Then, an adaptive threshold segmentation algorithm is proposed and an image processing algorithm, which includes high‐pass filtering, feature extraction, adaptive threshold segmentation and Hough circle detection, is developed and applied to full‐band images in response to the interference of apple stem and calyx on early bruising detection in this article. The results show that the proposed algorithm has an accuracy of 95.56% for the detection of sound apple samples and an F1‐score of 94.70% for the detection of early bruised samples, which is better than the commonly used image segmentation algorithms such as Otsu and fixed thresholding. This study shows that the combination of NIR camera imaging and the adaptive threshold segmentation algorithm proposed in this article can effectively detect early bruises on apples, providing a new idea for rapid and nondestructive detection of agricultural product quality.Practical applicationsDetecting early bruises on apples can achieve effective monitoring and control of apple supply chain, and guarantee the quality and safety of agricultural products. Most studies are based on conventional machine vision with RGB camera or hyperspectral imaging technology, while conventional machine vision based on RGB camera is difficult to detect apple early bruises that are not visible to the naked eye, and hyperspectral imaging has a large amount of data and redundant image data among wavelengths. Compared with previous studies, apple early bruise detection method proposed in this article combining NIR camera imaging technology and adaptive threshold segmentation algorithm can avoid the interference of bright spots, fruit stem and calyx on bruise segmentation while capturing a single channel image, providing a new method for accurate and rapid detection of early bruises on apples.

Utilizing random forest algorithm for early detection of academic underperformance in open learning environments

Background One of the primary benefits of Open Learning Environments (OLEs) is their scalability. OLEs provide flexible and accessible learning opportunities to a large number of students, often on a global scale. This scalability has led to the development of OLEs that cover a wide range of subjects and disciplines, from computer science and engineering to humanities and social sciences. However, the scalability of OLEs also presents some challenges i.e., it can be too difficult to provide personalized support and feedback to individuals. Early prediction of student performance can improve the learning experience of students by providing early interventions and support. Method The specific objective of this study was to build a model that identifies at-risk students and allows for timely interventions to promote their academic achievement. The random forest classifier model has been used for analyzing anonymized large datasets available from Open University Learning Analytics (OULAD) to identify patterns and relationships among various factors that contribute to student success or failure. Results The findings of this study suggest that this algorithm achieved 90% accuracy in identifying students who may be at risk and providing them with the necessary support to succeed.

Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions

Edge AI, an interdisciplinary technology that enables distributed intelligence with edge devices, is quickly becoming a critical component in early health prediction. Edge AI encompasses data analytics and artificial intelligence (AI) using machine learning, deep learning, and federated learning models deployed and executed at the edge of the network, far from centralized data centers. AI enables the careful analysis of large datasets derived from multiple sources, including electronic health records, wearable devices, and demographic information, making it possible to identify intricate patterns and predict a person’s future health. Federated learning, a novel approach in AI, further enhances this prediction by enabling collaborative training of AI models on distributed edge devices while maintaining privacy. Using edge computing, data can be processed and analyzed locally, reducing latency and enabling instant decision making. This article reviews the role of Edge AI in early health prediction and highlights its potential to improve public health. Topics covered include the use of AI algorithms for early detection of chronic diseases such as diabetes and cancer and the use of edge computing in wearable devices to detect the spread of infectious diseases. In addition to discussing the challenges and limitations of Edge AI in early health prediction, this article emphasizes future research directions to address these concerns and the integration with existing healthcare systems and explore the full potential of these technologies in improving public health.

PSIV-22 Detection of Parturition of Rangeland Beef Cattle Using Gps and Accelerometer Data

Abstract Detection of parturition of rangeland cows remotely may be possible using low cost LoRa WAN monitoring systems that are capable of logging and transmitting cow activity and position data in real time. This study evaluated candidate algorithms for early detection of parturition using longitudinal data of cow activity and position collected by GPS and triaxial accelerometers. Trials were conducted at the USDA Jornada Experimental Range from November to December 2022. Five Raramuri Criollo and five Angus x Hereford mature cows were equipped with LoRa WAN tracking collars instrumented with GPS and triaxial accelerometers and monitored through late gestation (> 7 months) while grazing rangeland pastures of 1,230 and 2,200 ha, respectively. Animal location (latitude and longitude) and activity count (Ac) obtained from GPS and accelerometers data, respectively, were collected by receiving stations that transmitted data in real time through a LoRa WAN network. Collars transmitted GPS positions at one-hour intervals and Ac data at two-minute intervals. An operator routinely inspected focal cows in herds to register parturition within approximately 12 h accuracy. Sensor data for 21 days prior to calving were processed to calculate distance traveled (m/h) and activity rate (Ac/h). For each hour interval, the adjusted activity Index IN = activity/distance (Ac/m) was computed to disentangle motion changes not associated with walking activity. Two algorithms were tested. The first considered the temporal deviation (D) of IN for a given hour (X0), compared with the average IN of the same hour in the previous seven days: D = INX0 /(INX-1+ INX-2 + …+ INX-7)/7). The second considered the normalized probability (N) of D for a given hour (X0) compared with the same hour over the previous seven days: N = (INX0-(INX-1+ INX-2 + …+ INX-7)/7)/sd.(INX-0, INX-1, …, INX-7). A threshold for high probability of calving was set when at least three consecutive hours with D >3 or N >0.95 were detected. Both algorithms correctly triggered alerts on actual calving days. Thus, lack of detection or false detections of calving indicated that the sensitivity and specificity for calving detection were both 100%. The normalized method (N) triggered delayed calving alerts in two cases. Furthermore, greater (P < 0.05) number of consecutive hours with D > 3 (5.6 ± 2.1) around actual calving time were detected vs. the number of consecutive hours with N > 0.95 (3.9 ± 1.2), suggesting that the former algorithm was also able to detect longer duration of behaviors associated with calving. Results indicate possibilities for remote detection of the onset and duration of calving behavior (parturition + first nursing hours) of beef cows managed on large rangeland pastures that impose operational challenges for visual inspection of cows during calving. Further tests with a greater number of cows and management systems would be needed to confirm this hypothesis.

Lunch Symposium

Pulmonary arterial hypertension (PAH), defined as the presence of artery pulmonary artery pressure > 20 mmHg, pulmonary artery wedge pressure [Formula: see text]15 mmHg, and pulmonary vascular resistance (PVR) > 2Wood units based on expert consensus, is characterized by a progressive and sustained increase in PVR, which may lead to right heart failure and death. PAH is a well-known complication of connective tissue diseases (CTDs), such as systemic sclerosis, systemic lupus erythematosus, Sjögren’s syndrome, and other autoimmune conditions. In the past few years, tremendous progress in the understanding of PAH pathogenesis has been made, with various novel diagnostic and screening methods for the early detection of CTD-PAH proposed worldwide. This symposium would highlight the most updated 2022 ESC/ERS guideline in the disease management of PAH, including latest haemodynmaic definitions of PAH, new risk stratification approach at reassessment and updated PAH treatment recommendations. In addition to the guideline update, important discussion would cover the important diagnostic, and screening algorithm for early detection of CTD-PAH and clinical experiences in managing difficult CTD-PAH cases.

Ethical and legal implications of implementing risk algorithms for early detection and screening for oesophageal cancer, now and in the future.

Oesophageal cancer has significant morbidity and mortality but late diagnosis is common since early signs of disease are frequently misinterpreted. Project DELTA aims to enable earlier detection and treatment through targeted screening using a novel risk prediction algorithm for oesophageal cancer (incorporating risk factors of Barrett's oesophagus including prescriptions for acid-reducing medications (CanPredict)), together with a non-invasive, low-cost sampling device (CytospongeTM). However, there are many barriers to implementation, and this paper identifies key ethical and legal challenges to implementing these personalised prevention strategies for Barrett's oesophagus/oesophageal cancer. To identify ethical and legal issues relevant to the deployment of a risk prediction tool for oesophageal cancer into primary care, we adopted an interdisciplinary approach, incorporating targeted informal literature reviews, interviews with expert collaborators, a multidisciplinary workshop and ethical and legal analysis. Successful implementation raises many issues including ensuring transparency and effective risk communication; addressing bias and inequity; managing resources appropriately and avoiding exceptionalism. Clinicians will need support and training to use cancer risk prediction algorithms, ensuring that they understand how risk algorithms supplement rather than replace medical decision-making. Workshop participants had concerns about liability for harms arising from risk algorithms, including from potential bias and inequitable implementation. Determining strategies for risk communication enabling transparency but avoiding exceptionalist approaches are a significant challenge. Future challenges include using artificial intelligence to bolster risk assessment, incorporating genomics into risk tools, and deployment by non-health professional users. However, these strategies could improve detection and outcomes. Novel pathways incorporating risk prediction algorithms hold considerable promise, especially when combined with low-cost sampling. However immediate priorities should be to develop risk communication strategies that take account of using validated risk algorithms, and to ensure equitable implementation. Resolving questions about liability for harms arising should be a longer-term objective.

Damage Identification for Railway Tracks Using Onboard Monitoring Systems in In-Service Vehicles and Data Science

Nowadays, railway track monitoring strategies are based on the use of railway inspection vehicles and wayside dynamic monitoring systems. The latter sometimes requires traffic disruption, as well as higher time and cost-consumption activities, and the use of dedicated inspection vehicles is less economical and efficient as the use of in-service vehicles. Furthermore, the use of non-automated algorithms faces challenges when it comes to early damage detection in railway infrastructure, considering operational, environmental, and big data aspects, and may lead to false alarms. To overcome these challenges, the application of artificial intelligence (AI) algorithms for early detection of track defects using accelerations, measured by dynamic monitoring systems in in-service railway vehicles is attracting the attention of railway managers. In this paper, an AI-based methodology based on axle box acceleration signals is applied for the early detection of distributed damage to track in terms of the longitudinal level and lateral alignment. The methodology relies on feature extraction using an autoregressive model, data normalization using principal component analysis, data fusion and feature discrimination using Mahalanobis distance and outlier analysis, considering eight onboard accelerometers. For the numerical simulations, 75 undamaged and 45 damaged track scenarios are considered. The alert limit state defined in the European Standard for assessing track geometry quality is also assumed as a threshold. It was found that the detection accuracy of the AI-based methodology for different sensor layouts and types of damage is greater than 94%, which is acceptable.

Early lameness detection in dairy cattle based on wearable gait analysis using semi-supervised LSTM-Autoencoder

Early lameness detection is crucial to ensure the welfare and productivity of dairy cows. However, current research on early lameness identification using wearable analysis relies on the limited robustness of indirect behavioral measures, which are susceptible to individual variations and imbalances in lameness samples. In this study, we propose a semi-supervised Long short-term memory (LSTM)-Autoencoder algorithm for early lameness detection in dairy cows through time series data reconstruction. We collected gait data from all four limbs of 30 dairy cows using four IMUs. A LSTM-Autoencoder with three LSTM hidden layers was trained to learn the time series features of healthy gaits. Each gait was reconstructed, and anomaly gaits exceeding a threshold were identified by comparing reconstructed gaits with actual gaits. The gait symmetry was measured by comparing the percentage of anomaly gait between opposite limbs as an indicator of lameness severity. With a high accuracy of 97.78% and a true negative rate of 98.33%, our integrated approach outperforms traditional methods in early lameness detection and lame limb identification, enabling real-time monitoring and timely identification of lameness. The study is the first attempt at using a time series anomaly detection framework with deep learning-based gait reconstruction for lameness detection. Wearable gait analysis offers portability and real-time capabilities, providing continuous, accurate, and comprehensive gait information unaffected by lighting and field-of-view limitations. This approach holds promise for enhancing animal welfare and optimizing management practices in the dairy industry through timely identification and continuous monitoring of lameness.

Enhanced Model-Based Algorithm for Early Internal Short Circuit Detection in Lithium-Ion Batteries

Electric vehicles (EVs) are becoming more popular due to concerns about fuel shortages and environmental pollution. Lithium-ion batteries are the preferred power source for EVs because they have high energy and power densities. Ensuring the efficient, safe, and reliable operation of these batteries has been a significant focus of research in recent decades. One major concern that can affect Li-ion battery performance is thermal runaway, which can cause dangerous battery fires. Internal short circuits (ISCs) are believed to be the root cause of thermal runaway incidents in batteries, making early detection of spontaneous ISCs a critical diagnostic task. This study presents a new and simple early ISC detection method for a Li-ion cell based on the augmentation of the state space of an Extended Kalman Filter (EKF) that includes voltage and surface temperature observations. The framework allows for an estimation of the cell's internal ISC state while remaining computationally efficient. The proposed approach is demonstrated in a simulated environment using dynamic stress tests that reflect a practical battery working cycle. The results demonstrate that the method can promptly detect ISC occurrences.

An Assessment of Contemporary Methods and Data-Enabled Approaches for Early Cataract Detection

Background: Cataracts are common causes of visual impairment. Preventing blindness requires an early and accurate diagnosis. This review examines current cataract diagnosis strategies, explores data-driven machine learning algorithms for early detection, investigates the use of artificial intelligence (AI) approaches, assesses improvements in cataract detection accuracy, identifies research gaps, and provides recommendations for future studies. Methods: We gathered labelled cataract and non-cataract fundus data from the Kaggle. Scholarly publications were sourced from reliable databases such as ProQuest, IEEE, ELSEVIER, Google Scholar, and PubMed. A detailed literature search with specific terms expanded the scope of this review. We included studies that used cataract and non-cataract fundus eye images from cross-sectional, retrospective, and prospective studies. The quality assessment used the AMSTAR tool, considering factors such as literature search comprehensiveness, study selection criteria, data extraction methodologies, and study validity (Table 1). Results: This study encompassed 130 research publications, focusing on machine learning models and clinical-based diagnostic approaches for early-stage cataract identification. The performance of machine-learning models is influenced by factors such as dataset noise and limited reliable data. Barriers to the successful implementation of AI for cataract diagnosis were identified. Conclusions: This review emphasises the obstacles hindering the broad application of AI in cataract diagnosis. Addressing these findings is vital for developing strategies to overcome these challenges and enhance cataract detection systems. To achieve improved accuracy and efficiency in cataract diagnosis, future research should prioritise efforts to enhance dataset availability and quality, reduce data noise, and refine machine-learning algorithms. Unlocking the full potential of AI and/or machine learning can lead to significant breakthroughs in cataract diagnosis, ultimately resulting in better patient outcomes and reduced visual impairments.