A review of artificial intelligence applications in anterior segment ocular diseases

Background: Artificial intelligence (AI) has great potential for interpreting and analyzing images and processing large amounts of data. There is a growing interest in investigating the applications of AI in anterior segment ocular diseases. This narrative review aims to assess the use of different AI-based algorithms for diagnosing and managing anterior segment entities. Methods: We reviewed the applications of different AI-based algorithms in the diagnosis and management of anterior segment entities, including keratoconus, corneal dystrophy, corneal grafts, corneal transplantation, refractive surgery, pterygium, infectious keratitis, cataracts, and disorders of the corneal nerves, conjunctiva, tear film, anterior chamber angle, and iris. The English-language databases PubMed/MEDLINE, Scopus, and Google Scholar were searched using the following keywords: artificial intelligence, deep learning, machine learning, neural network, anterior eye segment diseases, corneal disease, keratoconus, dry eye, refractive surgery, pterygium, infectious keratitis, anterior chamber, and cataract. Relevant articles were compared based on the use of AI models in the diagnosis and treatment of anterior segment diseases. Furthermore, we prepared a summary of the diagnostic performance of the AI-based methods for anterior segment ocular entities. Results: Various AI methods based on deep and machine learning can analyze data obtained from corneal imaging modalities with acceptable diagnostic performance. Currently, complicated and time-consuming manual methods are available for diagnosing and treating eye diseases. However, AI methods could save time and prevent vision impairment in eyes with anterior segment diseases. Because many anterior segment diseases can cause irreversible complications and even vision loss, sufficient confidence in the results obtained from the designed model is crucial for decision-making by experts. Conclusions: AI-based models could be used as surrogates for analyzing manual data with improveddiagnostic performance. These methods could be reliable tools for diagnosing and managing anterior segmentocular diseases in the near future in remote areas. It is expected that future studies can design algorithms thatuse less data in a multitasking manner for the detection and management of anterior segment diseases

Two-stage deep neural network for diagnosing fungal keratitis via in vivo confocal microscopy images

Timely and effective diagnosis of fungal keratitis (FK) is necessary for suitable treatment and avoiding irreversible vision loss for patients. In vivo confocal microscopy (IVCM) has been widely adopted to guide the FK diagnosis. We present a deep learning framework for diagnosing fungal keratitis using IVCM images to assist ophthalmologists. Inspired by the real diagnostic process, our method employs a two-stage deep architecture for diagnostic predictions based on both image-level and sequence-level information. To the best of our knowledge, we collected the largest dataset with 96,632 IVCM images in total with expert labeling to train and evaluate our method. The specificity and sensitivity of our method in diagnosing FK on the unseen test set achieved 96.65% and 97.57%, comparable or better than experienced ophthalmologists. The network can provide image-level, sequence-level and patient-level diagnostic suggestions to physicians. The results show great promise for assisting ophthalmologists in FK diagnosis

Applicazione di tecniche di deep learning per la segmentazione di cellule dendritiche corneali

La microscopia confocale corneale (CCM) è una modalità di imaging non invasiva in grado di fornire una visualizzazione diretta, in vivo ed in tempo reale dello stato fisiologico presente, se sano o malato. In particolare, è possibile osservare, a livello del plesso nervoso sub-basale, la presenza di cellule dendritiche (DCs): le loro differenti condizioni di numerosità e morfologia consentono di diagnosticare patologie quali occhio secco, neuropatia diabetica, cheratiti fungine, ecc. L’applicazione dell’intelligenza artificiale per effettuare diagnosi si è nel tempo proposta come una metodologia per superare il dispendio temporale e la soggettività dovuti all’analisi condotta dal medico. In questa tesi è stato implementato un algoritmo di Deep Learning per la segmentazione automatica delle cellule dendritiche corneali, facendo uso di un network neurale convoluzionale (CNN) di tipo U-Net costituito da un percorso di codifica e da uno di decodifica. L'algoritmo sviluppato consente di individuare le cellule dendritiche con un True Positive Rate dell'80.7 % ed un False Discovery Rate del 21 %. Questi risultati dimostrano la possibilità di individuare in automatico le cellule dendritiche ed incoraggiano l'ulteriore sviluppo dell'algoritmo proposto.The corneal confocal microscopy (CCM) is a non invasive imaging modality that gives a direct, in vivo and a real-time visualization of the physiological state presents, if healty or diseased. In particular, is possibile to observe, at sub-basal nerve plexus level, the presence of dendritic cells (DCs): their different conditions of numerosity and morfology allow to diagnose pathologies like dry-eye, diabetic neuropathy, fungal keratitis, etc. As time goes by, the application of artificial intelligence for doing diagnosis has been proposed as a methodology for overcome the subjective and time-consuming analysis conducted by the physician. In this thesis has been implemented a Deep Learning algorithm for the automatic segmentation of corneal dendritic cells, using an U-net convolutional neural network (CNN) made by a coding and a decoding pathway. The developed algorithm allows to individualize dendritic cells with a True Positive Rate of 80.7 % and a False Discovery Rate of 21 %. This results show the possibility of automatically individualize dendritic cells and encourage a further development of the proposed algorithm

Attention Mechanisms in the Classification of Histological Images

Recently, there has been an increase in the number of medical exams prescribed by medical doctors, not only to diagnose but also to keep track of the evolution of pathologies. In this sense, one of the medical specialties where the mentioned increase in the prescription rate has been observed is oncology. In this regard, not only to efficiently diagnose but also to monitor the evolution of the mentioned diseases, CT (Computed Tomography) scans, MRIs (Magnetic Resonance Imaging), and Biopsies are imaging techniques commonly used. After the exams are performed and the results retrieved by the respective health professionals, their analysis and interpretation are mandatory. This process, carried out by medical experts, is usually a time-consuming and tiring task. In this sense and to reduce the workload of these experts and support decision making, the research community start proposing several computer-aided systems, whose primary goal is to efficiently distinguish between healthy images and tumoral ones. Despite the success achieved by these methodologies, it become evident that the distinction of the two mentioned image categories (healthy and not-healthy) was associated with small regions of the images, and therefore not all image regions were equally important for diagnostic purposes. In this line of thinking, attention mechanisms start being considered to highlight important regions and neglect unimportant ones, leading to more correct predictions. In this thesis, we aim to study the impact of such mechanisms in the extraction of features from histopathological images of the epithelium from the oral cavity. In order to access the quality of the generated features for diagnostic purposes, those features were used to distinguish healthy from cancerous histopathological images.Recentemente, tem-se observado uma tendência crescente no número de exames médicos prescritos por médicos, no sentido de diagnosticar e acompanhar a evolução de patologias. Deste modo, uma das especialidades médicas onde a referida taxa de prescrição se assinala bastante elevada é a oncologia. No sentido de não só diagnosticar com eficácia, mas também para que a evolução das patologias seja devidamente seguida, é comum recorrer-se a técnicas de imagiologia como TACs (Tomografia Axial Computorizadas), RMs (Ressonâncias Magnéticas) ou Biópsias. Após a recepção dos respectivos exames médicos é necessário a sua análise e interpretação pelos profissionais competentes. Este processo é frequentemente moroso e cansativo para estes profissionais. No sentido de reduzir o labor destes profissionais e apoiar a tomada de decisão, começaram a surgir na literatura diversos sistemas computacionais cujo objectivo é distinguir imagens saudáveis de imagens não-saudáveis. Apesar do sucesso alcançado por estes sistemas, rapidamente se verificou que a distinção das duas classes de imagens é dependente de pequenas regiões, neste sentido nem todas as regiões constituintes da imagem são igualmente importantes para a distinção acima indicada. Posto isto, foram considerados mecanismos de atenção no sentido de maior importância dar a porções relevantes da imagem e negligenciar menos importantes, conduzindo a previsões mais correctas. Nesta dissertação pretende-se fazer um estudo do impacto destes mecanismos na extracção de features de imagens histopatológicas da mucosa oral. No sentido de avaliar a qualidade das features extraídas para o diagnóstico, estas são usadas por classificadores para a distinção de imagens saudáveis e cancerígenas

Diagnostic Armamentarium of Infectious Keratitis: A Comprehensive Review

Infectious keratitis (IK) represents the leading cause of corneal blindness worldwide, particularly in developing countries. A good outcome of IK is contingent upon timely and accurate diagnosis followed by appropriate interventions. Currently, IK is primarily diagnosed on clinical grounds supplemented by microbiological investigations such as microscopic examination with stains, and culture and sensitivity testing. Although this is the most widely accepted practice adopted in most regions, such an approach is challenged by several factors, including indistinguishable clinical features shared among different causative organisms, polymicrobial infection, long diagnostic turnaround time, and variably low culture positivity rate. In this review, we aim to provide a comprehensive overview of the current diagnostic armamentarium of IK, encompassing conventional microbiological investigations, molecular diagnostics (including polymerase chain reaction and mass spectrometry), and imaging modalities (including anterior segment optical coherence tomography and in vivo confocal microscopy). We also highlight the potential roles of emerging technologies such as next-generation sequencing, artificial intelligence-assisted platforms. and tele-medicine in shaping the future diagnostic landscape of IK

Anwendungen maschinellen Lernens für datengetriebene Prävention auf Populationsebene

Healthcare costs are systematically rising, and current therapy-focused healthcare systems are not sustainable in the long run. While disease prevention is a viable instrument for reducing costs and suffering, it requires risk modeling to stratify populations, identify high- risk individuals and enable personalized interventions. In current clinical practice, however, systematic risk stratification is limited: on the one hand, for the vast majority of endpoints, no risk models exist. On the other hand, available models focus on predicting a single disease at a time, rendering predictor collection burdensome. At the same time, the den- sity of individual patient data is constantly increasing. Especially complex data modalities, such as -omics measurements or images, may contain systemic information on future health trajectories relevant for multiple endpoints simultaneously. However, to date, this data is inaccessible for risk modeling as no dedicated methods exist to extract clinically relevant information. This study built on recent advances in machine learning to investigate the ap- plicability of four distinct data modalities not yet leveraged for risk modeling in primary prevention. For each data modality, a neural network-based survival model was developed to extract predictive information, scrutinize performance gains over commonly collected covariates, and pinpoint potential clinical utility. Notably, the developed methodology was able to integrate polygenic risk scores for cardiovascular prevention, outperforming existing approaches and identifying benefiting subpopulations. Investigating NMR metabolomics, the developed methodology allowed the prediction of future disease onset for many common diseases at once, indicating potential applicability as a drop-in replacement for commonly collected covariates. Extending the methodology to phenome-wide risk modeling, elec- tronic health records were found to be a general source of predictive information with high systemic relevance for thousands of endpoints. Assessing retinal fundus photographs, the developed methodology identified diseases where retinal information most impacted health trajectories. In summary, the results demonstrate the capability of neural survival models to integrate complex data modalities for multi-disease risk modeling in primary prevention and illustrate the tremendous potential of machine learning models to disrupt medical practice toward data-driven prevention at population scale.Die Kosten im Gesundheitswesen steigen systematisch und derzeitige therapieorientierte Gesundheitssysteme sind nicht nachhaltig. Angesichts vieler verhinderbarer Krankheiten stellt die Prävention ein veritables Instrument zur Verringerung von Kosten und Leiden dar. Risikostratifizierung ist die grundlegende Voraussetzung für ein präventionszentri- ertes Gesundheitswesen um Personen mit hohem Risiko zu identifizieren und Maßnah- men einzuleiten. Heute ist eine systematische Risikostratifizierung jedoch nur begrenzt möglich, da für die meisten Krankheiten keine Risikomodelle existieren und sich verfüg- bare Modelle auf einzelne Krankheiten beschränken. Weil für deren Berechnung jeweils spezielle Sets an Prädiktoren zu erheben sind werden in Praxis oft nur wenige Modelle angewandt. Gleichzeitig versprechen komplexe Datenmodalitäten, wie Bilder oder -omics- Messungen, systemische Informationen über zukünftige Gesundheitsverläufe, mit poten- tieller Relevanz für viele Endpunkte gleichzeitig. Da es an dedizierten Methoden zur Ex- traktion klinisch relevanter Informationen fehlt, sind diese Daten jedoch für die Risikomod- ellierung unzugänglich, und ihr Potenzial blieb bislang unbewertet. Diese Studie nutzt ma- chinelles Lernen, um die Anwendbarkeit von vier Datenmodalitäten in der Primärpräven- tion zu untersuchen: polygene Risikoscores für die kardiovaskuläre Prävention, NMR Meta- bolomicsdaten, elektronische Gesundheitsakten und Netzhautfundusfotos. Pro Datenmodal- ität wurde ein neuronales Risikomodell entwickelt, um relevante Informationen zu extra- hieren, additive Information gegenüber üblicherweise erfassten Kovariaten zu quantifizieren und den potenziellen klinischen Nutzen der Datenmodalität zu ermitteln. Die entwickelte Me-thodik konnte polygene Risikoscores für die kardiovaskuläre Prävention integrieren. Im Falle der NMR-Metabolomik erschloss die entwickelte Methodik wertvolle Informa- tionen über den zukünftigen Ausbruch von Krankheiten. Unter Einsatz einer phänomen- weiten Risikomodellierung erwiesen sich elektronische Gesundheitsakten als Quelle prädik- tiver Information mit hoher systemischer Relevanz. Bei der Analyse von Fundusfotografien der Netzhaut wurden Krankheiten identifiziert für deren Vorhersage Netzhautinformationen genutzt werden könnten. Zusammengefasst zeigten die Ergebnisse das Potential neuronaler Risikomodelle die medizinische Praxis in Richtung einer datengesteuerten, präventionsori- entierten Medizin zu verändern

PRELIMINARY FINDINGS OF A POTENZIATED PIEZOSURGERGICAL DEVICE AT THE RABBIT SKULL

The number of available ultrasonic osteotomes has remarkably increased. In vitro and in vivo studies have revealed differences between conventional osteotomes, such as rotating or sawing devices, and ultrasound-supported osteotomes (Piezosurgery®) regarding the micromorphology and roughness values of osteotomized bone surfaces. Objective: the present study compares the micro-morphologies and roughness values of osteotomized bone surfaces after the application of rotating and sawing devices, Piezosurgery Medical® and Piezosurgery Medical New Generation Powerful Handpiece. Methods: Fresh, standard-sized bony samples were taken from a rabbit skull using the following osteotomes: rotating and sawing devices, Piezosurgery Medical® and a Piezosurgery Medical New Generation Powerful Handpiece. The required duration of time for each osteotomy was recorded. Micromorphologies and roughness values to characterize the bone surfaces following the different osteotomy methods were described. The prepared surfaces were examined via light microscopy, environmental surface electron microscopy (ESEM), transmission electron microscopy (TEM), confocal laser scanning microscopy (CLSM) and atomic force microscopy. The selective cutting of mineralized tissues while preserving adjacent soft tissue (dura mater and nervous tissue) was studied. Bone necrosis of the osteotomy sites and the vitality of the osteocytes near the sectional plane were investigated, as well as the proportion of apoptosis or cell degeneration. Results and Conclusions: The potential positive effects on bone healing and reossification associated with different devices were evaluated and the comparative analysis among the different devices used was performed, in order to determine the best osteotomes to be employed during cranio-facial surgery

Handbook on clinical neurology and neurosurgery

HANDBOOKNEUROLOGYNEUROSURGERYКЛИНИЧЕСКАЯ НЕВРОЛОГИЯНЕВРОЛОГИЯНЕЙРОХИРУРГИЯThis handbook includes main parts of clinical neurology and neurosurgery