Specific Algorithm Research Articles

Pre‐Flight and In‐Flight Calibration and Performance of the Terminal Tracking Cameras (TTCams) on the NASA Lucy Mission

AbstractThe Terminal Tracking Camera (TTCam) imaging system on the NASA Lucy Discovery mission consists of a pair of cameras that are being used mainly as a navigation and target acquisition system for the mission's asteroid encounters. However, a secondary science‐focused function of the TTCam system is to provide wide‐angle broadband images over a large range of phase angles around close approach during each asteroid flyby. The scientific data acquired by TTCam can be used for shape modeling and topographic and geologic analyses. This paper describes the pre‐flight and initial in‐flight calibration and characterization of the TTCams, including the development of a radiometric calibration pipeline to convert raw TTCam images into radiance and radiance factor (I/F) images, along with their uncertainties. Details are also provided here on the specific calibration algorithms, the origin and archived location of the required ancillary calibration files, and the archived sources of the raw calibration and flight data used in this analysis.

Study of Soft Errors in Spiking Neural Network Hardware

The problem of soft errors in the hardware implementation of Spiking Neural Networks (SNN) has always been a challenge. SNNs, unlike traditional deep learning networks, simulate the temporal dynamic behavior of biological neurons and emit spikes when a specific threshold is reached. In recent years, the hardware implementation of SNNs has shown great potential in performing efficient and low-power tasks, but as technology nodes shrink and integrated circuit complexity increases, soft errors become a key challenge. To this end, we propose a novel approach based on input and weight analysis, through specific algorithms at the hardware level, to significantly reduce the probability of soft errors affecting the results. In terms of training accuracy, our method maintains a high accuracy under various voltage fluctuation conditions, demonstrating its superior robustness.

Assessment of Breeding Potential of Foxtail Millet Varieties Using a TOPSIS Model Constructed Based on Distinctness, Uniformity, and Stability Test Characteristics.

Foxtail millet (Setaria italica) is an important cereal crop with rich nutritional value. Distinctness, Uniformity, and Stability (DUS) are the prerequisites for the application of new variety rights for foxtail millet. In this study, we investigated 32 DUS test characteristics of 183 foxtail millet resources, studied their artificial selection trends, and identified the varieties that conform to breeding trends. The results indicated significant differences in terms of the means, ranges, and coefficients of variation for each characteristic. A correlation analysis was performed to determine the correlations between various DUS characteristics. A principal component analysis was conducted on 31 test characteristics to determine their primary characteristics. By plotting PC1 and PC2, all the germplasm resources could be clearly distinguished. The trends in foxtail millet breeding were identified through a differential analysis of the DUS test characteristics between the landrace and cultivated varieties. Based on these breeding trends, the optimal solution types for multiple evaluation indicators were determined; the weight allocation was calculated; and a specific TOPSIS algorithm was designed to establish a comprehensive multi-criteria decision-making model. Using this model, the breeding potential of foxtail millet germplasm resources were ranked. These findings provided important reference for foxtail millet breeding in the future.

Neural image re-exposure

Images and videos often suffer from issues such as motion blur, video discontinuity, or rolling shutter artifacts. Prior studies typically focus on designing specific algorithms to address individual issues. In this paper, we highlight that these issues, albeit differently manifested, fundamentally stem from sub-optimal exposure processes. With this insight, we propose a paradigm termed re-exposure, which resolves the aforementioned issues by performing exposure simulation. Following this paradigm, we design a new architecture, which constructs visual content representation from images and event camera data, and performs exposure simulation in a controllable manner. Experiments demonstrate that, using only a single model, the proposed architecture can effectively address multiple visual issues, including motion blur, video discontinuity, and rolling shutter artifacts, even when these issues co-occur.

Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions.

In recent years, there has been explosive development in artificial intelligence (AI), which has been widely applied in the health care field. As a typical AI technology, machine learning models have emerged with great potential in predicting cardiovascular diseases by leveraging large amounts of medical data for training and optimization, which are expected to play a crucial role in reducing the incidence and mortality rates of cardiovascular diseases. Although the field has become a research hot spot, there are still many pitfalls that researchers need to pay close attention to. These pitfalls may affect the predictive performance, credibility, reliability, and reproducibility of the studied models, ultimately reducing the value of the research and affecting the prospects for clinical application. Therefore, identifying and avoiding these pitfalls is a crucial task before implementing the research. However, there is currently a lack of a comprehensive summary on this topic. This viewpoint aims to analyze the existing problems in terms of data quality, data set characteristics, model design, and statistical methods, as well as clinical implications, and provide possible solutions to these problems, such as gathering objective data, improving training, repeating measurements, increasing sample size, preventing overfitting using statistical methods, using specific AI algorithms to address targeted issues, standardizing outcomes and evaluation criteria, and enhancing fairness and replicability, with the goal of offering reference and assistance to researchers, algorithm developers, policy makers, and clinical practitioners.

Advances in bioinformatics-based protein function prediction

With the increasing of computer power and rapid expansion of biological data, the application of bioinformatics tools has become the mainstream approach to address biological problems. The accurate identification of protein function by bioinformatics tools is crucial for both biomedical research and drug discovery, making it a hot topic of research. In this paper, we categorize bioinformatics-based protein function prediction methods into three categories: protein sequence-based methods, protein structure-based methods, and protein interaction networks-based methods. We further analyze these specific algorithms, highlighting the latest research advancements and providing valuable references for the application of bioinformatics-based protein function prediction in biomedical research and drug discovery.

Convergence of hybrid slice sampling via spectral gap

Abstract It is known that the simple slice sampler has robust convergence properties; however, the class of problems where it can be implemented is limited. In contrast, we consider hybrid slice samplers which are easily implementable and where another Markov chain approximately samples the uniform distribution on each slice. Under appropriate assumptions on the Markov chain on the slice, we give a lower bound and an upper bound of the spectral gap of the hybrid slice sampler in terms of the spectral gap of the simple slice sampler. An immediate consequence of this is that the spectral gap and geometric ergodicity of the hybrid slice sampler can be concluded from the spectral gap and geometric ergodicity of the simple version, which is very well understood. These results indicate that robustness properties of the simple slice sampler are inherited by (appropriately designed) easily implementable hybrid versions. We apply the developed theory and analyze a number of specific algorithms, such as the stepping-out shrinkage slice sampling, hit-and-run slice sampling on a class of multivariate targets, and an easily implementable combination of both procedures on multidimensional bimodal densities.

Mid-Flexion Instability in Total Knee Arthroplasty: Insights from Robotic-Assisted Surgery

Despite technological advancements with robotic-assisted surgery, instability remains a challenge in total knee arthroplasty (TKA). Mid-flexion instability (MFI) has been reported to cause patient dissatisfaction. With no universal diagnostic criteria, the MFI concept is still ambiguous, and no specific treatment algorithm is defined. This study aims to analyze the MFI concept and risk factors and investigate how robotic surgery, compared to manual TKA, could impact the MFI concept. A comprehensive investigation of the current literature regarding MIF, focusing especially on its relationship with robotic surgery TKA, was conducted using the PubMed and Scopus databases. The MIF concept remains poorly understood, so it is crucial to prevent it by recognizing risk factors, which are technique-related, implant-related, and patient-related. Since robotics offers optimal balancing in TKA and reduces causes affecting MFI, it could indirectly impact and prevent this complication. This review suggests that robotics utilization improving TKA balancing has the potential to impact and reduce MFI. However, further research in this area is essential to provide insight regarding the role of robotics in mitigating the MFI risk.

Bounding tractogram redundancy.

In tractography, redundancy poses a significant challenge, often resulting in tractograms that include anatomically implausible streamlines or those that fail to represent the brain's white matter architecture accurately. Current filtering methods aim to refine tractograms by addressing these issues, but they lack a unified measure of redundancy and can be computationally demanding. We propose a novel framework to quantify tractogram redundancy based on filtering tractogram subsets without endorsing a specific filtering algorithm. Our approach defines redundancy based on the anatomical plausibility and diffusion signal representation of streamlines, establishing both lower and upper bounds for the number of false-positive streamlines and the tractogram redundancy. We applied this framework to tractograms from the Human Connectome Project, using geometrical plausibility and statistical methods informed by the streamlined attributes and ensemble consensus. Our results establish bounds for the tractogram redundancy and the false-discovery rate of the tractograms. This study advances the understanding of tractogram redundancy and supports the refinement of tractography methods. Future research will focus on further validating the proposed framework and exploring tractogram compression possibilities.

Method of Forming and Algorithm of Saving a Photo Map of the Seafloor during its Photography by an Unmanned Underwater Vehicle

The paper proposes a new method and algorithm for creating a photo map of the seabed from photographs obtained from on-board photo and video systems of autonomous uninhabited underwater vehicles during fulfillment of their various missions. This photo map is formed from photographs of the seabed by sequentially overlapping them. At the same time, data on the position of the underwater vehicle received from the on-board navigation system at the time of receipt of each photograph is used to position each photo image on the generated photo map. And to rotate and scale these photographic images, data on the orientation of the underwater vehicle in the global coordinate system and its height above the bottom, also obtained from the on-board navigation system, are used. To provide quick access to the photo map, it is proposed to store it on an on-board data storage device in the form of a tile map, widely used when creating interactive geographical maps, for example, Google Maps. However, the formation of a tile map is usually carried out on the basis of all the already received photographic images, as is done with satellite images, which cannot be applied on board underwater vehicles when data from the photo-video system is received sequentially. Therefore, a special algorithm has been developed and implemented that generates a tile map during the movement of the underwater vehicle and updates only the area of the tile map in which the photo is currently being taken. This minimizes the computational load and ensures the formation of a photo map of the bottom using standard on-board computing devices. The conducted semi-natural experiments with data obtained as a result of performing real missions using the MMT-3000 underwater vehicle confirmed the high efficiency of using the developed method and algorithm.

Machine Learning Innovations for Precise Plant Disease Detection: A Review

The sustainable agriculture practices demands new innovations identifying plant diseases and instead of crop disease detection and precision and efficacy. An extensive review of the literature found through PubMed searches indicates a gap in the present approaches, which highlights the need for sophisticated machine learning solutions in the field of plant pathology. This study involves a comprehensive review of relevant publications collected via PubMed searches. The methodology involves the analysis of machine learning algorithms, datasets utilized and techniques applied for plant disease detection. Special attention is given to recent advancements in the field, focusing on the development and optimization of models tailored for precise and reliable disease identification. The study reveals compelling results, underscoring the transformative impact of machine learning innovations on plant disease detection accuracy. Specific algorithms exhibit superior performance, with implications for widespread applications in precision agriculture. These outcomes not only enhance current disease identification capabilities but also lay the groundwork for future advancements in automated and high-precision plant pathology diagnostics. The integration of machine learning emerges as a pivotal force in reshaping the landscape of plant disease detection.

Soft tissue tumor imaging in adults: whole-body staging in sarcoma, non-malignant entities requiring special algorithms, pitfalls and special imaging aspects. Guidelines 2024 from the European Society of Musculoskeletal Radiology (ESSR).

The revised European Society of Musculoskeletal Radiology (ESSR) consensus guidelines on soft tissue tumor imaging represent an update of 2015 after technical advancements, further insights into specific entities, and revised World Health Organization (2020) and AJCC (2017) classifications. This second of three papers covers algorithms once histology is confirmed: (1) standardized whole-body staging, (2) special algorithms for non-malignant entities, and (3) multiplicity, genetic tumor syndromes, and pitfalls. A validated Delphi method based on peer-reviewed literature was used to derive consensus among a panel of 46 specialized musculoskeletal radiologists from 12 European countries. Statements that had undergone interdisciplinary revision were scored online by the level of agreement (0 to 10) during two iterative rounds, that could result in 'group consensus', 'group agreement', or 'lack of agreement'. The three sections contain 24 statements with comments. Group consensus was reached in 95.8% and group agreement in 4.2%. For whole-body staging, pulmonary MDCT should be performed in all high-grade sarcomas. Whole-body MRI is preferred for staging bone metastasis, with [18F]FDG-PET/CT as an alternative modality in PET-avid tumors. Patients with alveolar soft part sarcoma, clear cell sarcoma, and angiosarcoma should be screened for brain metastases. Special algorithms are recommended for entities such as rhabdomyosarcoma, extraskeletal Ewing sarcoma, myxoid liposarcoma, and neurofibromatosis type 1 associated malignant peripheral nerve sheath tumors. Satisfaction of search should be avoided in potential multiplicity. Standardized whole-body staging includes pulmonary MDCT in all high-grade sarcomas; entity-dependent modifications and specific algorithms are recommended for sarcomas and non-malignant soft tissue tumors. These updated ESSR soft tissue tumor imaging guidelines aim to provide support in decision-making, helping to avoid common pitfalls, by providing general and entity-specific algorithms, techniques, and reporting recommendations for whole-body staging in sarcoma and non-malignant soft tissue tumors. An early, accurate, diagnosis is crucial for the prognosis of patients with soft tissue tumors. These updated guidelines provide best practice expert consensus for standardized imaging algorithms, techniques, and reporting. Standardization can improve the comparability examinations and provide databases for large data analysis.

Variational Feature Extraction in Scientific Visualization

Across many scientific disciplines, the pursuit of even higher grid resolutions leads to a severe scalability problem in scientific computing. Feature extraction is a commonly chosen approach to reduce the amount of information from dense fields down to geometric primitives that further enable a quantitative analysis. Examples of common features are isolines, extremal lines, or vortex corelines. Due to the rising complexity of the observed phenomena, or in the event of discretization issues with the data, a straightforward application of textbook feature definitions is unfortunately insufficient. Thus, feature extraction from spatial data often requires substantial pre- or post-processing to either clean up the results or to include additional domain knowledge about the feature in question. Such a separate pre- or post-processing of features not only leads to suboptimal and incomparable solutions, it also results in many specialized feature extraction algorithms arising in the different application domains. In this paper, we establish a mathematical language that not only encompasses commonly used feature definitions, it also provides a set of regularizers that can be applied across the bounds of individual application domains. By using the language of variational calculus, we treat features as variational minimizers, which can be combined and regularized as needed. Our formulation not only encompasses existing feature definitions as special case, it also opens the path to novel feature definitions. This work lays the foundations for many new research directions regarding formal definitions, data representations, and numerical extraction algorithms.

An event-triggered background-oriented schlieren technique combined with dynamic projection using dynamic mirror device

Abstract This paper reports a high-frequency event-triggered background-oriented schlieren (BOS) technique using a combination of an event-triggered camera and dynamic projection. To combine the advantages of continuous and pulsed illumination for the event-triggered camera, a novel background pattern is first developed to incorporate static and dynamic textures generated through projection utilizing a dynamic mirror device (DMD). Then, a specific post-processing algorithm is proposed to reconstruct frames with high time accuracy from event data. This technique allows for the continuous observation and capturing of flows at 4000 frames per second (FPS) with a very low cost, breaking through the short operating times of current high-frame-rate BOS. Moreover, the proposed BOS technique can visualize the flow in real-time with high temporal accuracy, a capability that is challenging to achieve with traditional BOS. To examine the proposed technique, BOS experiments were conducted on a sweeping jet actuator with various inlet pressure. The sweeping dynamics and the start-up process of the sweeping jet at various inlet pressure were visualized and investigated. It is found that the proposed event-triggered BOS can continuously visualize and record the jet flow at a resolution of 1280 × 720 pixels with an equivalent frame rate of up to 4000 FPS. The oscillation frequency of the sweeping jet was found to increase linearly with increasing inlet pressure. It reaches 117.2 Hz at an inlet pressure of 0.5 Mpa. Within the first ten milliseconds or so of start-up, the shape of the sweep was found to be symmetrical. Within the next hundred milliseconds, the jet commences to sweep and saturates. The start-up time of the sweeping jet was quantitatively measured and was observed to decrease with increased inlet pressures.

An international survey on canine urinary incontinence: case frequency, diagnosis, treatment and follow-up.

Urinary incontinence (UI) consists of involuntary leakage of urine during the storage phase of urination. An anonymous survey was given to Spanish and Italian veterinarians about canine UI treated cases, diagnosis, treatment, follow-up, and professional interest. Most veterinarians treated ≤3 cases/quarter, resulting in the percentage of incontinence males being lower than that of females (1-4% vs 0-24%). The percentage of spayed incontinent females was lower in Spain (0-24%) than in Italy (75-100%). Most diagnoses were based on a diagnostic algorithm (Spain: 88.7%; Italy: 65.3%); patient report and history, blood work, urinalysis and abdominal ultrasound. Urethral/bladder pressure measurement was unusual (Spain: 0.2%; Italy: 2.4%). In Spain, radiology with contrast medium and CT urography (26.3% and 34.4%, respectively) were more frequent than in Italy (11.6% and 22.7%, respectively). When suspecting urethral sphincter mechanism incompetence pharmacological trial (Spain: 93.2%; Italy: 78.9%). The first-choice medical treatment was Phenylpropanolamine, followed by Ephedrine and Deslorelin. When pharmacotherapy failed, the most frequent option was drug change, followed by increased drug dosage/frequency of administration, surgical therapy and colposuspension. A review was completed after the first week of treatment followed by periodic reviews. Most of the respondents participated in continuing education only if UI occurred in their everyday practice (Spain: 63.0%; Italy: 55.4%) and about 30% responders did it regardless of the number of UI cases treated (Spain: 30.5%; Italy: 37.4%). Some recommendations in clinical practice were made. UI can be underestimated by owners; therefore, a complete history should be obtained by veterinarians. Veterinarians should carefully evaluate if spaying is advisable considering it could increase UI risk. A step-by-step approach is recommended and a specific diagnostic-therapeutic algorithm for UI in dogs is provided. Conservative approaches (regular exercise, weight loss in overweight dogs and observing an "incontinence diary" to identify abnormal patterns of urination) are advisable.

Evaluation of a prototype metal artifact reduction algorithm for cone beam CT in patients undergoing radioembolization

Metal artifacts notoriously pose significant challenge in computed tomography (CT), leading to inaccuracies in image formation and interpretation. Artifact reduction tools have been designed to improve cone beam computed tomography (CBCT) image quality by reducing artifacts caused by certain high-density materials. Metal artifact reduction (MAR) tools are specific algorithms that are applied during image reconstruction to minimize or eliminate artifacts degrading CBCT images. The purpose of the study is to evaluate the effect of a MAR algorithm on image quality in CBCT performed for evaluating patients before transarterial radioembolization (TARE). We retrospectively included 40 consecutive patients (aged 65 ± 13 years; 23 males) who underwent 45 CBCT examinations (Allura FD 20, XperCT Roll protocol, Philips Healthcare, Best, The Netherlands) in the setting of evaluation for TARE between January 2017 and December 2018. Artifacts caused by coils, catheters, and surgical clips were scored subjectively by four readers on a 5-point scale (1 = artifacts affecting diagnostic information to 5 = no artifacts) using a side-by-side display of uncorrected and MAR-corrected images. In addition, readers scored tumor visibility and vessel discrimination. MAR-corrected images were assigned higher scores, indicating better image quality. The differences between the measurements with and without MAR were most impressive for coils with a mean improvement of 1.6 points (95%CI [1.5 1.8]) on the 5-point likert scale, followed by catheters 1.4 points (95%CI [1.3 1.5]) and clips 0.7 points (95%CI [0.3 1.1]). Improvements for other artifact sources were consistent but relatively small (below 0.25 points on average). Interrater agreement was good to perfect (Kendall’s W coefficient = 0.68–0.95) and was higher for MAR-corrected images, indicating that MAR improves diagnostic accuracy. A metal artifact reduction algorithm can improve diagnostic and interventional accuracy of cone beam CT in patients undergoing radioembolization by reducing artifacts caused by diagnostic catheters and coils, lowering interference of metal artifacts with adjacent major structures, and improving tumor visibility.

Clinical performance of human papillomavirus based cervical cancer screening algorithm: The result of a large Danish implementation study.

In Denmark, where human papillomavirus (HPV) -based cervical cancer screening is being implemented, the aim of this pilot implementation study was to test a specific screening algorithm, assess follow-up examination attendance, and measure the proportion of precancer lesions found in relation to the number of women referred for colposcopy. From May 2017 to December 2020, 36 417 women in the uptake area of the Department of Pathology, Vejle Hospital, Region of Southern Denmark, were included in the HPV group. Women positive for HPV16/18 irrespective of cytology and women positive for other high-risk HPV (hrHPV) types having concomitant abnormal cytology were referred directly to colposcopy. Women positive for other hrHPV types and normal cytology were referred to repeat screening after 12 months, and hrHPV negative to routine screening after three years. We obtained information on screening results and subsequent histological diagnosis from the Danish Pathology Databank through September 2022. 3.6% of the women were referred to colposcopy after primary screening, 5% to repeat screening after 12 months, and 91.4% back to routine screening. High follow-up rates were observed: 96% attended colposcopy after primary screening, with 91% attending colposcopy after repeat screening. CIN3+ was detected at colposcopy following the primary screening in 28.1% of HPV16/18-positive women and 18.2% of those positive for other hrHPV types with concomitant abnormal cytology. Of the women with other hrHPV and simultaneous ASCUS/LSIL, 8% had CIN3+. At the repeat screening, 43% had become hrHPV negative, 55% were persistently positive for other hrHPV, and 2% had turned positive for HPV16/18. At the colposcopy following repeat screening, 10.1% of the women positive for other hrHPV were diagnosed with CIN3+, in comparison with 11.1% of the HPV16/18-positive women. In this pilot implementation study, an algorithm for HPV-based screening was evaluated in a Danish setting. The results demonstrated high attendance at follow-up examinations and provided insights into the number of colposcopy referrals and the detection of CIN2 and CIN3+ cases. The results suggest that women testing positive for other hrHPV in combination with ASCUS/LSIL at primary screening could potentially be referred to repeat screening instead of an immediate colposcopy.

Machine Vision Analysis of Ujumqin Sheep's Walking Posture and Body Size.

The ability to recognize the body sizes of sheep is significantly influenced by posture, especially without artificial fixation, leading to more noticeable changes. This study presents a recognition model using the Mask R-CNN convolutional neural network to identify the sides and backs of sheep. The proposed approach includes an algorithm for extracting key frames through mask calculation and specific algorithms for head-down, head-up, and jumping postures of Ujumqin sheep. The study reported an accuracy of 94.70% in posture classification. We measured the body size parameters of Ujumqin sheep of different sexes and in different walking states, including observations of head-down and head-up. The errors for the head-down position of rams, in terms of body slanting length, withers height, hip height, and chest depth, were recorded as 0.08 ± 0.06, 0.09 ± 0.07, 0.07 ± 0.05, and 0.12 ± 0.09, respectively. For rams in the head-up position, the corresponding errors were 0.06 ± 0.05, 0.06 ± 0.05, 0.07 ± 0.05, and 0.13 ± 0.07, respectively. The errors for the head-down position of ewes, in terms of body slanting length, withers height, hip height, and chest depth, were recorded as 0.06 ± 0.05, 0.09 ± 0.08, 0.07 ± 0.06, and 0.13 ± 0.10, respectively. For ewes in the head-up position, the corresponding errors were 0.06 ± 0.05, 0.08 ± 0.06, 0.06 ± 0.04, and 0.16 ± 0.12, respectively. The study observed that sheep walking through a passage exhibited a more curved knee posture compared to normal measurements, often with a lowered head. This research presents a cost-effective data collection scheme for studying multiple postures in animal husbandry.

USteg-DSE: Universal quantitative Steganalysis framework using Densenet merged with Squeeze & Excitation net

Carrying concealed communication via media is termed as steganography and unraveling details of such covert transmission is known as steganalysis. Extracting details of hidden message like length, position, embedding algorithm etc. forms part of forensic steganalysis. Predicting length of payload in camouflaged interchange is termed as quantitative steganalysis and is an indispensable tool for forensic investigators. When payload length is estimated without any prior knowledge about cover media or used steganography algorithm, it is termed as universal quantitative steganalysis.Most of existing frameworks on quantitative steganalysis available in literature, work for a specific embedding algorithm or are domain specific. In this paper we propose and present USteg-DSE, a deep learning framework for performing universal quantitative image steganalysis using DenseNet with Squeeze & Excitation module (SEM). In deep learning techniques, deeper networks easily capture complex statistical properties. But as depth increases, networks suffer from vanishing gradient problem. In classic architectures, all channels are equally weighted to produce feature maps. Presented USteg-DSE framework overcomes these problems by using DenseNet and SEM. In DenseNet, each layer is directly connected with every other layer. DenseNet makes information and gradient flow easier with fewer feature maps. SEM incorporates content aware mechanism to adaptively regulate weight for every feature map. Presented framework has been compared with existing state-of-the-art techniques for spatial domain as well as transform domain and show better results in terms of Mean Absolute Error (MAE) and Mean Square Error (MSE).

Hidradenitis suppurativa: review of international clinical guidelines for the diagnosis and treatment of the disease

Background. Hidradenitis suppurativa hidradenitis is a chronic recurrent inflammatory skin disease that develops after puberty and is characterized by the appearance of recurrent painful nodes, abscesses, the formation of fistula passages and scars on skin areas rich in apocrine sweat glands. Treatment of purulent hidradenitis is aimed at suppressing inflammation, relieving pain, preventing the formation of fistulas and scars. The objectives of this review. Summarize the information in the published international clinical guidelines for the diagnosis and treatment of purulent hidradenitis, their comprehensive assessment and comparison with each other. Methods. In the period from December 2022 to February 2023, scientific articles were searched in the PubMed database of the National Center for Biotechnological Information. Inclusion criteria: scientific articles in English, without date restrictions; interdisciplinary publications of specialists in which dermatovenereologists participated. Results. The analysis of the existing relevant international clinical recommendations for the diagnosis and treatment of purulent hidradenitis indicates the absence of specific treatment schemes and algorithms, criteria for evaluating the effectiveness of therapy. In the treatment of purulent hidradenitis, the use of external keratolytics, antiseptics and antibiotics is recommended. Among systemic drugs, antibiotics, retinoids, immunosuppressive agents, hormonal drugs are used. The highest therapeutic efficacy in patients with purulent hidradenitis was shown by genetically engineered drugs that inhibit TNF-α and IL-17A. Conclusions. Patients with purulent hidradenitis require various treatment approaches, including a variety of surgical interventions, depending on the stage, severity, prescription of the disease and the general condition of the patient. The basic principle is the individual selection of the treatment method for a particular patient. In this regard, there is a need to develop domestic clinical guidelines for the management of patients with purulent hidradenitis.