All you need is data preparation: A systematic review of image harmonization techniques in Multi-center/device studies for medical support systems.

BACKGROUND AND OBJECTIVES: Artificial intelligence (AI) models trained on multi-centric and multi-device studies can provide more robust insights and research findings compared to single-center studies. However, variability in acquisition protocols and equipment can introduce inconsistencies that hamper the effective pooling of multi-source datasets. This systematic review evaluates strategies for image harmonization, which standardizes appearances to enable reliable AI analysis of multi-source medical imaging. METHODS: A literature search using PRISMA guidelines was conducted to identify relevant papers published between 2013 and 2023 analyzing multi-centric and multi-device medical imaging studies that utilized image harmonization approaches. RESULTS: Common image harmonization techniques included grayscale normalization (improving classification accuracy by up to 24.42 %), resampling (increasing the percentage of robust radiomics features from 59.5 % to 89.25 %), and color normalization (enhancing AUC by up to 0.25 in external test sets). Initially, mathematical and statistical methods dominated, but machine and deep learning adoption has risen recently. Color imaging modalities like digital pathology and dermatology have remained prominent application areas, though harmonization efforts have expanded to diverse fields including radiology, nuclear medicine, and ultrasound imaging. In all the modalities covered by this review, image harmonization improved AI performance, with increasing of up to 24.42 % in classification accuracy and 47 % in segmentation Dice scores. CONCLUSIONS: Continued progress in image harmonization represents a promising strategy for advancing healthcare by enabling large-scale, reliable analysis of integrated multi-source datasets using AI. Standardizing imaging data across clinical settings can help realize personalized, evidence-based care supported by data-driven technologies while mitigating biases associated with specific populations or acquisition protocols.

Automatic identification of hypertension and assessment of its secondary effects using artificial intelligence: A systematic review (2013-2023).

Artificial Intelligence (AI) techniques are increasingly used in computer-aided diagnostic tools in medicine. These techniques can also help to identify Hypertension (HTN) in its early stage, as it is a global health issue. Automated HTN detection uses socio-demographic, clinical data, and physiological signals. Additionally, signs of secondary HTN can also be identified using various imaging modalities. This systematic review examines related work on automated HTN detection. We identify datasets, techniques, and classifiers used to develop AI models from clinical data, physiological signals, and fused data (a combination of both). Image-based models for assessing secondary HTN are also reviewed. The majority of the studies have primarily utilized single-modality approaches, such as biological signals (e.g., electrocardiography, photoplethysmography), and medical imaging (e.g., magnetic resonance angiography, ultrasound). Surprisingly, only a small portion of the studies (22 out of 122) utilized a multi-modal fusion approach combining data from different sources. Even fewer investigated integrating clinical data, physiological signals, and medical imaging to understand the intricate relationships between these factors. Future research directions are discussed that could build better healthcare systems for early HTN detection through more integrated modeling of multi-modal data sources.

Automated anxiety detection using probabilistic binary pattern with ECG signals.

BACKGROUND AND AIM: Anxiety disorder is common; early diagnosis is crucial for management. Anxiety can induce physiological changes in the brain and heart. We aimed to develop an efficient and accurate handcrafted feature engineering model for automated anxiety detection using ECG signals. MATERIALS AND METHODS: We studied open-access electrocardiography (ECG) data of 19 subjects collected via wearable sensors while they were shown videos that might induce anxiety. Using the Hamilton Anxiety Rating Scale, subjects are categorized into normal, light anxiety, moderate anxiety, and severe anxiety groups. ECGs were divided into non-overlapping 4- (Case 1), 5- (Case 2), and 6-second (Case 3) segments for analysis. We proposed a self-organized dynamic pattern-based feature extraction function-probabilistic binary pattern (PBP)-in which patterns within the function were determined by the probabilities of the input signal-dependent values. This was combined with tunable q-factor wavelet transform to facilitate multileveled generation of feature vectors in both spatial and frequency domains. Neighborhood component analysis and Chi2 functions were used to select features and reduce data dimensionality. Shallow k-nearest neighbors and support vector machine classifiers were used to calculate four (=2 × 2) classifier-wise results per input signal. From the latter, novel self-organized combinational majority voting was applied to calculate an additional five voted results. The optimal final model outcome was chosen from among the nine (classifier-wise and voted) results using a greedy algorithm. RESULTS: Our model achieved classification accuracies of over 98.5 % for all three cases. Ablation studies confirmed the incremental accuracy of PBP-based feature engineering over traditional local binary pattern feature extraction. CONCLUSIONS: The results demonstrated the feasibility and accuracy of our PBP-based feature engineering model for anxiety classification using ECG signals.

Clinical assessment of deep learning-based uncertainty maps in lung cancer segmentation.

Objective. Prior to radiation therapy planning, accurate delineation of gross tumour volume (GTVs) and organs at risk (OARs) is crucial. In the current clinical practice, tumour delineation is performed manually by radiation oncologists, which is time-consuming and prone to large inter-observer variability. With the advent of deep learning (DL) models, automated contouring has become possible, speeding up procedures and assisting clinicians. However, these tools are currently used in the clinic mostly for contouring OARs, since these systems are not reliable yet for contouring GTVs. To improve the reliability of these systems, researchers have started exploring the topic of probabilistic neural networks. However, there is still limited knowledge of the practical implementation of such networks in real clinical settings.Approach. In this work, we developed a 3D probabilistic system that generates DL-based uncertainty maps for lung cancer CT segmentations. We employed the Monte Carlo (MC) dropout technique to generate probabilistic and uncertainty maps, while the model calibration was evaluated by using reliability diagrams. A clinical validation was conducted in collaboration with a radiation oncologist to qualitatively assess the value of the uncertainty estimates. We also proposed two novel metrics, namely mean uncertainty (MU) and relative uncertainty volume (RUV), as potential indicators for clinicians to assess the need for independent visual checks of the DL-based segmentation. Main results. Our study showed that uncertainty mapping effectively identified cases of under or over-contouring. Although the overconfidence of the model, a strong correlation was observed between the clinical opinion and MU metric. Moreover, both MU and RUV revealed high AUC values in discretising between low and high uncertainty cases.Significance. Our study is one of the first attempts to clinically validate uncertainty estimates in DL-based contouring. The two proposed metrics exhibited promising potential as indicators for clinicians to independently assess the quality of tumour delineation.

The Benefit of Transvaginal Elastography in Detecting Deep Endometriosis: A Feasibility Study.

OBJECTIVES: This study aimed to evaluate elastography features of deep infiltrating endometriosis (DIE), and to define whether this technique may discriminate lesions from surrounding non-endometriotic tissue. METHODS: This was an exploratory observational study on women affected by DIE treated in a third-level academic hospital gynaecology outpatient facility between 2020 and 2021. Strain elastography (SE) was conducted via transvaginal probe. Tissue deformation of DIE and surrounding tissue was expressed as percentage tissue deformation or as subjective colour score (CS; from blue=stiff to red=soft, assigned numerical values from 0 to 3). Ratios of normal tissue/DIE were compared to ratio of normal tissue/stiffer normal tissue area. RESULTS: Evaluations were performed on 46 DIE nodules and surrounding tissue of the uterosacral ligaments (n=21), parametrium (n=7), rectum (n=14), and recto-vaginal septum (n =4). Irrespective of location, DIE strain ratio (3.09, IQR 2.38-4.14 vs. 1.25, IQR 1.11-1.48; p<0.001) and CS ratio (4.62, IQR 3.83-6.94 vs. 1.13, IQR 1.06-1.29; p<0.001) was significantly higher than that of normal tissue. ROC AUC of CS ratio was higher than ROC AUC of strain ratio (99.76%, CI.95 99.26-100% vs. 91.35%, CI.95 85.23-97.47%; p=0.007), and best ROC threshold for CS ratio was 1.82, with a sensitivity of 97.83% (CI.95 93.48-100%) and a specificity of 100% (CI.95 100-100%). CONCLUSIONS: Both strain and CS ratios accurately distinguish DIE nodules at various locations. Applications of elastography in improving the diagnosis DIE, in distinguishing different DIE lesions and in monitoring DIE evolution can be envisioned and are worthy of further evaluation.

Oncological Outcomes of Thulium-Yttrium-Aluminum-Garnet (Tm:YAG) Laser Ablation for Penile Cancer.

OBJECTIVE: To report oncological outcomes after thulium-yttrium-aluminum-garnet (Tm:YAG) laser ablation for penile cancer patients. MATERIALS AND METHODS: We retrospectively analyzed 71 patients with ≤cT1 penile cancer (2013-2022). All patients underwent Tm:YAG ablation with a RevoLix 200W continuous-wave laser. First, Kaplan-Meier plots and multivariable Cox regression models tested local tumor recurrence rates. Second, Kaplan-Meier plots tested progression-free survival (≥T3 and/or N1-3 and/or M1). RESULTS: Median (interquartile range) follow-up time was 38 (22-58) months. Overall, 33 (50.5%) patients experienced local tumor recurrence. Specifically, 19 (29%) vs 9 (14%) vs 5 (7.5%) patients had 1 vs 2 vs 3 recurrences over time. In multivariable Cox regression models, a trend for higher recurrence rates was observed for G3 tumors (hazard ratio:6.1; P = .05), relative to G1. During follow-up, 12 (18.5%) vs 4 (6.0%) vs 2 (3.0%) men were retreated with 1 vs 2 vs 3 Tm:YAG laser ablations. Moreover, 11 (17.0%) and 3 (4.5%) patients underwent glansectomy and partial/total penile amputation. Last, 5 (7.5%) patients experienced disease progression. Specifically, TNM stage at the time of disease progression was: (1) pT3N0; (2) pT2N2; (3) pTxN3; (4) pT1N1 and (5) pT3N3, respectively. CONCLUSION: Tm:YAG laser ablation provides similar oncological results as those observed by other penile-sparing surgery procedures. In consequence, Tm:YAG laser ablation should be considered a valid alternative for treating selected penile cancer patients.

Improved assessment of donor liver steatosis using Banff consensus recommendations and deep learning algorithms.

BACKGROUND & AIMS: The Banff Liver Working Group recently published consensus recommendations for steatosis assessment in donor liver biopsy, but few studies reported their use and no automated deep-learning algorithms based on the proposed criteria have been developed so far. We evaluated Banff recommendations on a large monocentric series of donor liver needle biopsies by comparing pathologists' scores with those generated by convolutional neural networks (CNNs) we specifically developed for automated steatosis assessment. METHODS: We retrospectively retrieved 292 allograft liver needle biopsies collected between January 2016 and January 2020 and performed steatosis assessment using a former intra-institution method (pre-Banff method) and the newly introduced Banff recommendations. Scores provided by pathologists and CNN models were then compared, and the degree of agreement was measured with the intraclass correlation coefficient (ICC). RESULTS: Regarding the pre-Banff method, poor agreement was observed between the pathologist and CNN models for small droplet macrovesicular steatosis (ICC: 0.38), large droplet macrovesicular steatosis (ICC: 0.08), and the final combined score (ICC: 0.16) evaluation, but none of these reached statistically significance. Interestingly, significantly improved agreement was observed using the Banff approach: ICC was 0.93 for the low-power score (p <0.001), 0.89 for the high-power score (p <0.001), and 0.93 for the final score (p <0.001). Comparing the pre-Banff method with the Banff approach on the same biopsy, pathologist and CNN model assessment showed a mean (±SD) percentage of discrepancy of 26.89 (±22.16) and 1.20 (±5.58), respectively. CONCLUSIONS: Our findings support the use of Banff recommendations in daily practice and highlight the need for a granular analysis of their effect on liver transplantation outcomes. IMPACT AND IMPLICATIONS: We developed and validated the first automated deep-learning algorithms for standardized steatosis assessment based on the Banff Liver Working Group consensus recommendations. Our algorithm provides an unbiased automated evaluation of steatosis, which will lay the groundwork for granular analysis of steatosis's short- and long-term effects on organ viability, enabling the identification of clinically relevant steatosis cut-offs for donor organ acceptance. Implementing our algorithm in daily clinical practice will allow for a more efficient and safe allocation of donor organs, improving the post-transplant outcomes of patients.

Penile-Sparing Surgery for Tumour Recurrence after Previous Glansectomy/Partial Penectomy: Treatment Feasibility and Oncological Outcomes.

We tested the feasibility and oncological outcomes after penile-sparing surgery (PSS) for local recurrent penile cancer after a previous glansectomy/partial penectomy. We retrospectively analysed 13 patients (1997-2022) with local recurrence of penile cancer after a previous glansectomy or partial penectomy. All patients underwent PSS: circumcision, excision, or laser ablation. First, technical feasibility, treatment setting, and complications (Clavien-Dindo) were recorded. Second, Kaplan-Meier plots depicted overall and local recurrences over time. Overall, 11 (84.5%) vs. 2 (15.5%) patients were previously treated with glansectomy vs. partial penectomy. The median (IQR) time to disease recurrence was 56 (13-88) months. Six (46%) vs. two (15.5%) vs. five (38.5%) patients were treated with, respectively, local excision vs. local excision + circumcision vs. laser ablation. All procedures, except one, were performed in an outpatient setting. Only one Clavien-Dindo 2 complication was recorded. The median follow-up time was 41 months. Overall, three (23%) vs. four (30.5%) patients experienced local vs. overall recurrence, respectively. All local recurrences were safely treated with salvage surgery. In conclusion, we reported the results of a preliminary analysis testing safety, feasibility, and early oncological outcomes of PSS procedures for patients with local recurrence after previous glansectomy or partial penectomy. Stronger oncological outcomes should be tested in other series to optimise patient selection.

Reply to Taylor et al. Comment on "Manna et al. SARS-CoV-2 Inactivation in Aerosol by Means of Radiated Microwaves. Viruses 2023, 15, 1443".

SARS-CoV-2 is inactivated in aerosol (its primary mode of transmission) by means of radiated microwaves at frequencies that have been experimentally determined. Such frequencies are best predicted by the mathematical model suggested by Taylor, Margueritat and Saviot. The alignment between such mathematical prediction and the outcomes of our experiments serves to reinforce the efficacy of the radiated microwave technology and its promise in mitigating the transmission of SARS-CoV-2 in its naturally airborne state.

Quantitative analysis of prion disease using an AI-powered digital pathology framework.

Prion disease is a fatal neurodegenerative disorder characterized by accumulation of an abnormal prion protein (PrPSc) in the central nervous system. To identify PrPSc aggregates for diagnostic purposes, pathologists use immunohistochemical staining of prion protein antibodies on tissue samples. With digital pathology, artificial intelligence can now analyze stained slides. In this study, we developed an automated pipeline for the identification of PrPSc aggregates in tissue samples from the cerebellar and occipital cortex. To the best of our knowledge, this is the first framework to evaluate PrPSc deposition in digital images. We used two strategies: a deep learning segmentation approach using a vision transformer, and a machine learning classification approach with traditional classifiers. Our method was developed and tested on 64 whole slide images from 41 patients definitively diagnosed with prion disease. The results of our study demonstrated that our proposed framework can accurately classify WSIs from a blind test set. Moreover, it can quantify PrPSc distribution and localization throughout the brain. This could potentially be extended to evaluate protein expression in other neurodegenerative diseases like Alzheimer's and Parkinson's. Overall, our pipeline highlights the potential of AI-assisted pathology to provide valuable insights, leading to improved diagnostic accuracy and efficiency.

Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013-2023).

Uncertainty estimation in healthcare involves quantifying and understanding the inherent uncertainty or variability associated with medical predictions, diagnoses, and treatment outcomes. In this era of Artificial Intelligence (AI) models, uncertainty estimation becomes vital to ensure safe decision-making in the medical field. Therefore, this review focuses on the application of uncertainty techniques to machine and deep learning models in healthcare. A systematic literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our analysis revealed that Bayesian methods were the predominant technique for uncertainty quantification in machine learning models, with Fuzzy systems being the second most used approach. Regarding deep learning models, Bayesian methods emerged as the most prevalent approach, finding application in nearly all aspects of medical imaging. Most of the studies reported in this paper focused on medical images, highlighting the prevalent application of uncertainty quantification techniques using deep learning models compared to machine learning models. Interestingly, we observed a scarcity of studies applying uncertainty quantification to physiological signals. Thus, future research on uncertainty quantification should prioritize investigating the application of these techniques to physiological signals. Overall, our review highlights the significance of integrating uncertainty techniques in healthcare applications of machine learning and deep learning models. This can provide valuable insights and practical solutions to manage uncertainty in real-world medical data, ultimately improving the accuracy and reliability of medical diagnoses and treatment recommendations.

Deep learning approach for accurate prostate cancer identification and stratification using combined immunostaining of cytokeratin, p63, and racemase.

BACKGROUND: Prostate cancer (PCa) is the most frequently diagnosed cancer in men worldwide, affecting around 1.4 million individuals. Current PCa diagnosis relies on histological analysis of prostate biopsy samples, an activity that is both time-consuming and prone to observer bias. Previous studies have demonstrated that immunostaining of cytokeratin, p63, and racemase can significantly improve the sensitivity and the specificity of PCa detection compared to traditional H&E staining. METHODS: This study introduces a novel approach that combines diagnosis-specific immunohistochemical (IHC) staining and deep learning techniques to provide reliable stratification of prostate glands. Our approach leverages a customized segmentation network, called K-PPM, that incorporates adaptive kernels and multiscale feature integration to enhance the functional information of IHC. To address the high class-imbalance problem in the dataset, we propose a weighted adaptive patch-extraction and specific-class kernel update. RESULTS: Our system achieved noteworthy results, with a mean Dice Score Coefficient of 90.36% and a mean absolute error of 1.64 % in specific-class gland quantification on whole slides. These findings demonstrate the potential of our system as a valuable support tool for pathologists, reducing workload and decreasing diagnostic inter-observer variability. CONCLUSIONS: Our study presents innovative approaches that have broad applicability to other digital pathology areas beyond PCa diagnosis. As a fully automated system, this model can serve as a framework for improving the histological and IHC diagnosis of other types of cancer.

EpilepsyNet: Novel automated detection of epilepsy using transformer model with EEG signals from 121 patient population.

BACKGROUND: Epilepsy is one of the most common neurological conditions globally, and the fourth most common in the United States. Recurrent non-provoked seizures characterize it and have huge impacts on the quality of life and financial impacts for affected individuals. A rapid and accurate diagnosis is essential in order to instigate and monitor optimal treatments. There is also a compelling need for the accurate interpretation of epilepsy due to the current scarcity in neurologist diagnosticians and a global inequity in access and outcomes. Furthermore, the existing clinical and traditional machine learning diagnostic methods exhibit limitations, warranting the need to create an automated system using deep learning model for epilepsy detection and monitoring using a huge database. METHOD: The EEG signals from 35 channels were used to train the deep learning-based transformer model named (EpilepsyNet). For each training iteration, 1-min-long data were randomly sampled from each participant. Thereafter, each 5-s epoch was mapped to a matrix using the Pearson Correlation Coefficient (PCC), such that the bottom part of the triangle was discarded and only the upper triangle of the matrix was vectorized as input data. PCC is a reliable method used to measure the statistical relationship between two variables. Based on the 5 s of data, single embedding was performed thereafter to generate a 1-dimensional array of signals. In the final stage, a positional encoding with learnable parameters was added to each correlation coefficient's embedding before being fed to the developed EpilepsyNet as input data to epilepsy EEG signals. The ten-fold cross-validation technique was used to generate the model. RESULTS: Our transformer-based model (EpilepsyNet) yielded high classification accuracy, sensitivity, specificity and positive predictive values of 85%, 82%, 87%, and 82%, respectively. CONCLUSION: The proposed method is both accurate and robust since ten-fold cross-validation was employed to evaluate the performance of the model. Compared to the deep models used in existing studies for epilepsy diagnosis, our proposed method is simple and less computationally intensive. This is the earliest study to have uniquely employed the positional encoding with learnable parameters to each correlation coefficient's embedding together with the deep transformer model, using a huge database of 121 participants for epilepsy detection. With the training and validation of the model using a larger dataset, the same study approach can be extended for the detection of other neurological conditions, with a transformative impact on neurological diagnostics worldwide.

Deep neural network technique for automated detection of ADHD and CD using ECG signal.

BACKGROUND AND OBJECTIVE: Attention Deficit Hyperactivity problem (ADHD) is a common neurodevelopment problem in children and adolescents that can lead to long-term challenges in life outcomes if left untreated. Also, ADHD is frequently associated with Conduct Disorder (CD), and multiple research have found similarities in clinical signs and behavioral symptoms between both diseases, making differentiation between ADHD, ADHD comorbid with CD (ADHD+CD), and CD a subjective diagnosis. Therefore, the goal of this pilot study is to create the first explainable deep learning (DL) model for objective ECG-based ADHD/CD diagnosis as having an objective biomarker may improve diagnostic accuracy. METHODS: The dataset used in this study consist of ECG data collected from 45 ADHD, 62 ADHD+CD, and 16 CD patients at the Child Guidance Clinic in Singapore. The ECG data were segmented into 2 s epochs and directly used to train our 1-dimensional (1D) convolutional neural network (CNN) model. RESULTS: The proposed model yielded 96.04% classification accuracy, 96.26% precision, 95.99% sensitivity, and 96.11% F1-score. The Gradient-weighted class activation mapping (Grad-CAM) function was also used to highlight the important ECG characteristics at specific time points that most impact the classification score. CONCLUSION: In addition to achieving model performance results with our suggested DL method, Grad-CAM's implementation also offers vital temporal data that clinicians and other mental healthcare professionals can use to make wise medical judgments. We hope that by conducting this pilot study, we will be able to encourage larger-scale research with a larger biosignal dataset. Hence allowing biosignal-based computer-aided diagnostic (CAD) tools to be implemented in healthcare and ambulatory settings, as ECG can be easily obtained via wearable devices such as smartwatches.

SARS-CoV-2 Inactivation in Aerosol by Means of Radiated Microwaves.

Coronaviruses are a family of viruses that cause disease in mammals and birds. In humans, coronaviruses cause infections on the respiratory tract that can be fatal. These viruses can cause both mild illnesses such as the common cold and lethal illnesses such as SARS, MERS, and COVID-19. Air transmission represents the principal mode by which people become infected by SARS-CoV-2. To reduce the risks of air transmission of this powerful pathogen, we devised a method of inactivation based on the propagation of electromagnetic waves in the area to be sanitized. We optimized the conditions in a controlled laboratory environment mimicking a natural airborne virus transmission and consistently achieved a 90% (tenfold) reduction of infectivity after a short treatment using a Radio Frequency (RF) wave emission with a power level that is safe for people according to most regulatory agencies, including those in Europe, USA, and Japan. To the best of our knowledge, this is the first time that SARS-CoV-2 has been shown to be inactivated through RF wave emission under conditions compatible with the presence of human beings and animals. Additional in-depth studies are warranted to extend the results to other viruses and to explore the potential implementation of this technology in different environmental conditions.

Brain tumor detection and screening using artificial intelligence techniques: Current trends and future perspectives.

A brain tumor is an abnormal mass of tissue located inside the skull. In addition to putting pressure on the healthy parts of the brain, it can lead to significant health problems. Depending on the region of the brain tumor, it can cause a wide range of health issues. As malignant brain tumors grow rapidly, the mortality rate of individuals with this cancer can increase substantially with each passing week. Hence it is vital to detect these tumors early so that preventive measures can be taken at the initial stages. Computer-aided diagnostic (CAD) systems, in coordination with artificial intelligence (AI) techniques, have a vital role in the early detection of this disorder. In this review, we studied 124 research articles published from 2000 to 2022. Here, the challenges faced by CAD systems based on different modalities are highlighted along with the current requirements of this domain and future prospects in this area of research.

Correction to "Whole-Cell Bioconversion of Renewable Biomasses-Related Aromatics to cis,cis-Muconic Acid".

[This corrects the article DOI: 10.1021/acssuschemeng.2c06534.].

Durability of neutralizing antibodies against yellow fever virus after vaccination in healthy adults.

In accordance with the World Health Organization, one dose of yellow fever vaccine may guarantee protection lifelong in healthy adults. However, relatively little information is still available from ad hoc studies. We evaluated the persistence of neutralizing antibodies, which are considered to be an immune correlate of protection, in a large number of military personnel vaccinated up to 47 years before. Overall, 322 individuals were studied. The median time from vaccination to blood collection for neutralizing antibody evaluation was 9 years, ranging from <1 to 47 years. Of the 322 participants, 319 had neutralizing antibodies (99.1 %). The highest median PRNT50 value was observed in those vaccinated ≤1 year before (median PRNT50 = 320). In conclusion, our study confirms on a larger scale that, in healthy adults, neutralizing antibodies may persist as long as 47 years after a single yellow fever vaccines dose.

Impact of Stain Normalization on Pathologist Assessment of Prostate Cancer: A Comparative Study.

In clinical routine, the quality of whole-slide images plays a key role in the pathologist's diagnosis, and suboptimal staining may be a limiting factor. The stain normalization process helps to solve this problem through the standardization of color appearance of a source image with respect to a target image with optimal chromatic features. The analysis is focused on the evaluation of the following parameters assessed by two experts on original and normalized slides: (i) perceived color quality, (ii) diagnosis for the patient, (iii) diagnostic confidence and (iv) time required for diagnosis. Results show a statistically significant increase in color quality in the normalized images for both experts (p < 0.0001). Regarding prostate cancer assessment, the average times for diagnosis are significantly lower for normalized images than original ones (first expert: 69.9 s vs. 77.9 s with p < 0.0001; second expert: 37.4 s vs. 52.7 s with p < 0.0001), and at the same time, a statistically significant increase in diagnostic confidence is proven. The improvement of poor-quality images and greater clarity of diagnostically important details in normalized slides demonstrate the potential of stain normalization in the routine practice of prostate cancer assessment.