Automatic Detection of Acromegaly From Facial Photographs Using Machine Learning Methods

Artificial intelligence facial recognition system for diagnosis of endocrine and metabolic syndromes based on a facial image database

AIM

To build a facial image database and to explore the diagnostic efficacy and influencing factors of the artificial intelligence-based facial recognition (AI-FR) system for multiple endocrine and metabolic syndromes.

METHODS

Individuals with multiple endocrine and metabolic syndromes and healthy controls were included from public literature and databases. In this facial image database, facial images and clinical data were collected for each participant and dFRI (disease facial recognition intensity) was calculated to quantify facial complexity of each syndrome. AI-FR diagnosis models were trained for each disease using three algorithms: support vector machine (SVM), principal component analysis k-nearest neighbor (PCA-KNN), and adaptive boosting (AdaBoost). Diagnostic performance was evaluated. Optimal efficacy was achieved as the best index among the three models. Effect factors of AI-FR diagnosis were explored with regression analysis.

RESULTS

462 cases of 10 endocrine and metabolic syndromes and 2310 controls were included into the facial image database. The AI-FR diagnostic models showed diagnostic accuracies of 0.827-0.920 with SVM, 0.766-0.890 with PCA-KNN, and 0.818-0.935 with AdaBoost. Higher dFRI was associated with higher optimal area under the curve (AUC) (P = 0.035). No significant correlation was observed between the sample size of the training set and diagnostic performance.

CONCLUSIONS

A multi-ethnic, multi-regional, and multi-disease facial database for 10 endocrine and metabolic syndromes was built. AI-FR models displayed ideal diagnostic performance. dFRI proved associated with the diagnostic performance, suggesting inherent facial features might contribute to the performance of AI-FR models.

Artificial intelligence in paediatric endocrinology: conflict or cooperation

Artificial intelligence (AI) in medicine is transforming healthcare by automating system tasks, assisting in diagnostics, predicting patient outcomes and personalising patient care, founded on the ability to analyse vast datasets. In paediatric endocrinology, AI has been developed for diabetes, for insulin dose adjustment, detection of hypoglycaemia and retinopathy screening; bone age assessment and thyroid nodule screening; the identification of growth disorders; the diagnosis of precocious puberty; and the use of facial recognition algorithms in conditions such as Cushing syndrome, acromegaly, congenital adrenal hyperplasia and Turner syndrome. AI can also predict those most at risk from childhood obesity by stratifying future interventions to modify lifestyle. AI will facilitate personalised healthcare by integrating data from 'omics' analysis, lifestyle tracking, medical history, laboratory and imaging, therapy response and treatment adherence from multiple sources. As data acquisition and processing becomes fundamental, data privacy and protecting children's health data is crucial. Minimising algorithmic bias generated by AI analysis for rare conditions seen in paediatric endocrinology is an important determinant of AI validity in clinical practice. AI cannot create the patient-doctor relationship or assess the wider holistic determinants of care. Children have individual needs and vulnerabilities and are considered in the context of family relationships and dynamics. Importantly, whilst AI provides value through augmenting efficiency and accuracy, it must not be used to replace clinical skills.

A novel multi-feature learning model for disease diagnosis using face skin images

BACKGROUND

Facial skin characteristics can provide valuable information about a patient's underlying health conditions.

OBJECTIVE

In practice, there are often samples with divergent characteristics (commonly known as divergent samples) that can be attributed to environmental factors, living conditions, or genetic elements. These divergent samples significantly degrade the accuracy of diagnoses.

METHODOLOGY

To tackle this problem, we propose a novel multi-feature learning method called Multi-Feature Learning with Centroid Matrix (MFLCM), which aims to mitigate the influence of divergent samples on the accurate classification of samples located on the boundary. In this approach, we introduce a novel discriminator that incorporates a centroid matrix strategy and simultaneously adapt it to a classifier in a unified model. We effectively apply the centroid matrix to the embedding feature spaces, which are transformed from the multi-feature observation space, by calculating a relaxed Hamming distance. The purpose of the centroid vectors for each category is to act as anchors, ensuring that samples from the same class are positioned close to their corresponding centroid vector while being pushed further away from the remaining centroids.

RESULTS

Validation of the proposed method with clinical facial skin dataset showed that the proposed method achieved F1 scores of 92.59%, 83.35%, 82.84% and 85.46%, respectively for the detection the Healthy, Diabetes Mellitus (DM), Fatty Liver (FL) and Chronic Renal Failure (CRF).

CONCLUSION

Experimental results demonstrate the superiority of the proposed method compared with typical classifiers single-view-based and state-of-the-art multi-feature approaches. To the best of our knowledge, this study represents the first to demonstrate concept of multi-feature learning using only facial skin images as an effective non-invasive approach for simultaneously identifying DM, FL and CRF in Han Chinese, the largest ethnic group in the world.

Artificial Intelligence (AI) in pediatric endocrinology

Artificial Intelligence (AI) is integrating itself throughout the medical community. AI's ability to analyze complex patterns and interpret large amounts of data will have considerable impact on all areas of medicine, including pediatric endocrinology. In this paper, we review and update the current studies of AI in pediatric endocrinology. Specific topics that are addressed include: diabetes management, bone growth, metabolism, obesity, and puberty. Becoming knowledgeable and comfortable with AI will assist pediatric endocrinologists, the goal of the paper.

Current and Future Advances in Surgical Therapy for Pituitary Adenoma

The vital physiological role of the pituitary gland, alongside its proximity to critical neurovascular structures, means that pituitary adenomas can cause significant morbidity or mortality. While enormous advancements have been made in the surgical care of pituitary adenomas, numerous challenges remain, such as treatment failure and recurrence. To meet these clinical challenges, there has been an enormous expansion of novel medical technologies (eg, endoscopy, advanced imaging, artificial intelligence). These innovations have the potential to benefit each step of the patient's journey, and ultimately, drive improved outcomes. Earlier and more accurate diagnosis addresses this in part. Analysis of novel patient data sets, such as automated facial analysis or natural language processing of medical records holds potential in achieving an earlier diagnosis. After diagnosis, treatment decision-making and planning will benefit from radiomics and multimodal machine learning models. Surgical safety and effectiveness will be transformed by smart simulation methods for trainees. Next-generation imaging techniques and augmented reality will enhance surgical planning and intraoperative navigation. Similarly, surgical abilities will be augmented by the future operative armamentarium, including advanced optical devices, smart instruments, and surgical robotics. Intraoperative support to surgical team members will benefit from a data science approach, utilizing machine learning analysis of operative videos to improve patient safety and orientate team members to a common workflow. Postoperatively, neural networks leveraging multimodal datasets will allow early detection of individuals at risk of complications and assist in the prediction of treatment failure, thus supporting patient-specific discharge and monitoring protocols. While these advancements in pituitary surgery hold promise to enhance the quality of care, clinicians must be the gatekeepers of the translation of such technologies, ensuring systematic assessment of risk and benefit prior to clinical implementation. In doing so, the synergy between these innovations can be leveraged to drive improved outcomes for patients of the future.

MLP-Like Model With Convolution Complex Transformation for Auxiliary Diagnosis Through Medical Images

Medical images such as facial and tongue images have been widely used for intelligence-assisted diagnosis, which can be regarded as the multi-label classification task for disease location (DL) and disease nature (DN) of biomedical images. Compared with complicated convolutional neural networks and Transformers for this task, recent MLP-like architectures are not only simple and less computationally expensive, but also have stronger generalization capabilities. However, MLP-like models require better input features from the image. Thus, this study proposes a novel convolution complex transformation MLP-like (CCT-MLP) model for the multi-label DL and DN recognition task for facial and tongue images. Notably, the convolutional Tokenizer and multiple convolutional layers are first used to extract the better shallow features from input biomedical images to make up for the loss of spatial information obtained by the simple MLP structure. Subsequently, the Channel-MLP architecture with complex transformations is used to extract deep-level contextual features. In this way, multi-channel features are extracted and mixed to perform the multi-label classification of the input biomedical images. Experimental results on our constructed multi-label facial and tongue image datasets demonstrate that our method outperforms existing methods in terms of both accuracy (Acc) and mean average precision (mAP).

The socioeconomic burden of acromegaly

Acromegaly is a rare and insidious disease characterized by chronic excess growth hormone, leading to various morphological changes and systemic complications. Despite its low prevalence, acromegaly poses a significant socioeconomic burden on patients and healthcare systems. This review synthesizes the current state of knowledge on the psychosocial burden, disability, impact on daily life, and cost of acromegaly disease, focusing on the quality of life, partnership, medical care and treatment afflictions, participation in daily activities, professional and leisure impairment, and cost of treatment for acromegaly and its comorbidities. It also examines management strategies, coping mechanisms, and interventions aimed at alleviating this burden. A comprehensive understanding of the extent of the socioeconomic burden in acromegaly is crucial to develop effective strategies to improve treatment and care. Further research is warranted to explore the myriad factors contributing to this burden, as well as the efficacy of interventions to alleviate it, ultimately enhancing the quality of life for patients with acromegaly.

Statistical local descriptors for face recognition: a comprehensive study

The use of local statistical descriptors for image representation has emerged and gained a reputation as a powerful approach in the last couple of decades. Many algorithms have been proposed and applied, since then, in various application areas employing different datasets, classifiers, and testing parameters. In this paper, we felt the need to make a comprehensive study of frequently-used statistical local descriptors. We investigate the effect of using different histogram-based local feature extraction algorithms on the performance of the face recognition problem. Comparisons are conducted among 18 different algorithms. These algorithms are used for the extraction of the local statistical feature descriptors of the face images. Moreover, feature fusion/concatenation of different combinations of generated feature descriptors is applied, and the relevant impact on the system performance is evaluated. Comprehensive experiments are carried out using two well-known face databases with identical experimental settings. The obtained results indicate that the fusion of the descriptors can significantly enhance the system's performance.

Multiple color representation and fusion for diabetes mellitus diagnosis based on back tongue images

Tongue images have been proved to be effective in diabetes mellitus (DM) diagnosis. Without requirement of collecting blood sample, tongue image based diagnosis approach is non-invasive and convenient for the patients. Meanwhile, the colors of tongues play an important in aiding accurate diagnosis. However, the tongues' colors fall on a small color gamut that makes it difficult for the existing color descripts to identify and distinguish the tiny difference of the tongues. To tackle this problem, we introduce a novel color descriptor by representing the colors with the clustering centers, namely color centroid points, of the color points sampled from tongue images. In order to boost the capacity of the descriptor, we extend it into three color spaces, i.e., RGB, HSV and LAB to mine a rich set of color information and exploit the complementary information among the three spaces. Since there exist correlation and complementarity among the features extracted from the three color spaces, we propose a novel multiple color features fusion method for DM diagnosis. Particularly, two projections are learned to project the multiple features to their corresponding shared and specific subspaces, in which their similarity and diversity are firstly measured by the Euclidean Distance and Hilbert Schmidt Independence Criterion (HSIC), respectively. To fully exploit the similar and complementary information, the two components are jointly transformed to their label vector, efficiently embedding the discriminant prior into the model, leading to significant improvement in the diagnosis outcomes. Experimental results on clinical tongue dataset substantiated the effectiveness of our proposed clustering-based color descriptor and the proposed multiple colors fusion approach. Overall, the proposed pipeline for the diagnosis of DM using back tongue images, achieved an average accuracy of up to 93.38%, indicating its potential toward realization of a clinical diagnostic tool for DM. Without loss generality, we also assessed the performance of the novel multiple features fusion method on two public datasets. The experiments prove the superiority of our multiple features learning model on general real-life application.

Real-time detection of acromegaly from facial images with artificial intelligence

OBJECTIVE

Despite improvements in diagnostic methods, acromegaly is still a late-diagnosed disease. In this study, it was aimed to automatically recognize acromegaly disease from facial images by using deep learning methods and to facilitate the detection of the disease.

DESIGN

Cross-sectional, single-centre study.

METHODS

The study included 77 acromegaly (52.56 ± 11.74, 34 males/43 females) patients and 71 healthy controls (48.47 ± 8.91, 39 males/32 females), considering gender and age compatibility. At the time of the photography, 56/77 (73%) of the acromegaly patients were in remission. Normalized images were obtained by scaling, aligning, and cropping video frames. Three architectures named ResNet50, DenseNet121, and InceptionV3 were used for the transfer learning-based convolutional neural network (CNN) model developed to classify face images as "Healthy" or "Acromegaly". Additionally, we trained and integrated these CNN machine learning methods to create an Ensemble Method (EM) for facial detection of acromegaly.

RESULTS

The positive predictive values obtained for acromegaly with the ResNet50, DenseNet121, InceptionV3, and EM were calculated as 0.958, 0.965, 0.962, and 0.997, respectively. The average sensitivity, specificity, precision, and correlation coefficient values calculated for each of the ResNet50, DenseNet121, and InceptionV3 models are quite close. On the other hand, EM outperformed these three CNN architectures and provided the best overall performance in terms of sensitivity, specificity, accuracy, and precision as 0.997, 0.997, 0.997, and 0.998, respectively.

CONCLUSIONS

The present study provided evidence that the proposed AcroEnsemble Model might detect acromegaly from facial images with high performance. This highlights that artificial intelligence programs are promising methods for detecting acromegaly in the future.

Endocrine disorders in obstructive sleep apnoea syndrome: A bidirectional relationship

Obstructive sleep apnoea (OSA) is a common disorder characterized by recurrent episodes of apnoea or hypopnea due to total or partial pharyngeal collapse and temporary upper airway obstruction during sleep. The prevalence of OSA is increasing and currently affects about 30% of men and 13% of women in Europe. Intermittent hypoxia, oxidative stress, systemic inflammation, and sleep fragmentation resulting from OSA can provoke subsequent cardiometabolic disorders. The relationships between endocrine disorders and OSA are complex and bidirectional. Indeed, several endocrine disorders are risk factors for OSA. Compared with the general population, the prevalence of OSA is increased in patients with obesity, hypothyroidism, acromegaly, Cushing syndrome, and type 1 and 2 diabetes. In some cases, treatment of the underlying endocrine disorder can improve, and occasionally cure, OSA. On the other hand, OSA can also induce endocrine disorders, particularly glucose metabolism abnormalities. Whether continuous positive airway pressure (CPAP) treatment for OSA can improve these endocrine disturbances remains unclear due to the presence of several confounding factors. In this review, we discuss the current state-of-the-art based on the review of the current medical literature for key articles focusing on the bidirectional relationship between endocrine disorders and OSA and the effects of treatment. Screening of OSA in endocrine patients is also discussed, as it remains a subject of debate.

Review on Facial-Recognition-Based Applications in Disease Diagnosis

Diseases not only manifest as internal structural and functional abnormalities, but also have facial characteristics and appearance deformities. Specific facial phenotypes are potential diagnostic markers, especially for endocrine and metabolic syndromes, genetic disorders, facial neuromuscular diseases, etc. The technology of facial recognition (FR) has been developed for more than a half century, but research in automated identification applied in clinical medicine has exploded only in the last decade. Artificial-intelligence-based FR has been found to have superior performance in diagnosis of diseases. This interdisciplinary field is promising for the optimization of the screening and diagnosis process and assisting in clinical evaluation and decision-making. However, only a few instances have been translated to practical use, and there is need of an overview for integration and future perspectives. This review mainly focuses on the leading edge of technology and applications in varieties of disease, and discusses implications for further exploration.

Non-invasive health prediction from visually observable features

Background: The unprecedented development of Artificial Intelligence has revolutionised the healthcare industry. In the next generation of healthcare systems, self-diagnosis will be pivotal to personalised healthcare services. During the COVID-19 pandemic, new screening and diagnostic approaches like mobile health are well-positioned to reduce disease spread and overcome geographical barriers. This paper presents a non-invasive screening approach to predict the health of a person from visually observable features using machine learning techniques. Images like face and skin surface of the patients are acquired using camera or mobile devices and analysed to derive clinical reasoning and prediction of the person’s health.

Methods: In specific, a two-level classification approach is presented. The proposed hierarchical model chooses a class by training a binary classifier at the node of the hierarchy. Prediction is then made using a set of class-specific reduced feature set.

Results: Testing accuracies of 86.87% and 76.84% are reported for the first and second-level classification. Empirical results demonstrate that the proposed approach yields favourable prediction results while greatly reduces the computational time.

Conclusions: The study suggests that it is possible to predict the health condition of a person based on his/her face appearance using cost-effective machine learning approaches.

Explainable health prediction from facial features with transfer learning

In the recent years, Artificial Intelligence (AI) has been widely deployed in the healthcare industry. The new AI technology enables efficient and personalized healthcare systems for the public. In this paper, transfer learning with pre-trained VGGFace model is applied to identify sick symptoms based on the facial features of a person. As the deep learning model’s operation is unknown for making a decision, this paper investigates the use of Explainable AI (XAI) techniques for soliciting explanations for the predictions made by the model. Various XAI techniques including Integrated Gradient, Explainable region-based AI (XRAI) and Local Interpretable Model-Agnostic Explanations (LIME) are studied. XAI is crucial to increase the model’s transparency and reliability for practical deployment. Experimental results demonstrate that the attribution method can give proper explanations for the decisions made by highlighting important attributes in the images. The facial features that account for positive and negative classes predictions are highlighted appropriately for effective visualization. XAI can help to increase accountability and trustworthiness of the healthcare system as it provides insights for understanding how a conclusion is derived from the AI model.

Diagnosis and Treatment of Acromegaly: An Update

Acromegaly is typically caused by a growth hormone-secreting pituitary adenoma, driving excess secretion of insulin-like growth factor 1. Acromegaly may result in a variety of cardiovascular, respiratory, endocrine, metabolic, musculoskeletal, and neoplastic comorbidities. Early diagnosis and adequate treatment are essential to mitigate excess mortality associated with acromegaly. PubMed searches were conducted using the keywords growth hormone, acromegaly, pituitary adenoma, diagnosis, treatment, pituitary surgery, medical therapy, and radiation therapy (between 1981 and 2021). The diagnosis of acromegaly is confirmed on biochemical grounds, including elevated serum insulin-like growth factor 1 and lack of growth hormone suppression after glucose administration. Pituitary magnetic resonance imaging is advised in patients with acromegaly to identify an underlying pituitary adenoma. Transsphenoidal pituitary surgery is generally first-line therapy for patients with acromegaly. However, patients with larger and invasive tumors (macroadenomas) are often not in remission postoperatively. Medical therapies, including somatostatin receptor ligands, cabergoline, and pegvisomant, can be recommended to patients with persistent disease after surgery. Select patients may also be candidates for preoperative medical therapy. In addition, primary medical therapy has a role for patients without mass effect on the optic chiasm who are unlikely to be cured by surgery. Clinical, endocrine, imaging, histologic, and molecular markers may help predict the response to medical therapy; however, confirmation in prospective studies is needed. Radiation therapy is usually a third-line option and is increasingly administered by a variety of stereotactic techniques. An improved understanding of the pathogenesis of acromegaly may ultimately lead to the design of novel, efficacious therapies for this serious condition.

The role of the dentist and orthodontist in recognizing oro-facial manifestations of acromegaly: a questionnaire-based study

PURPOSE

Oro-facial manifestations of acromegaly are among the earliest signs of the disease and are reported by a significant number of patients at diagnosis. Despite this high prevalence of acromegaly oral manifestation, dentists do not play a pivotal role in acromegaly identification and diagnosis. The aim of our study was to evaluate the ability of dentists and orthodontists in the early recognition of the oro-facial manifestations of acromegaly.

METHODS

A telematic questionnaire was administered to dentists and orthodontists. The questionnaire included photos with facial and oral-dental details and lateral teleradiography of acromegaly patients (ACRO).

RESULTS

The study included 426 participants: 220 dentists and 206 orthodontists. Upon reviewing the photos, dentists most often observed mandibular prognathism and lips projection, while orthodontists also reported the impairment of relative soft tissue. Orthodontists, who usually use photos to document patients' oral-facial characteristics, paid more attention to oral-facial impairment than dentists. During dental assessment, 90% of the participants usually evaluated tongue size and appearance, diastemas presence, and signs of sleep impairment (mainly orthodontists). Orthodontists were also more able to identify sella turcica enlargement at teleradiography. A total of 10.8% of the participants had ACRO as patients and 11.3% referred at least one patient for acromegaly suspicion.

CONCLUSION

The study highlighted dentists' strategic role in identifying ACRO. Increasing dentists' awareness about acromegaly clinical issues may improve early diagnosis, potentially resulting in an increased quality of life and decreased mortality among ACRO.

Machine intelligence in non-invasive endocrine cancer diagnostics

Artificial intelligence (AI) has illuminated a clear path towards an evolving health-care system replete with enhanced precision and computing capabilities. Medical imaging analysis can be strengthened by machine learning as the multidimensional data generated by imaging naturally lends itself to hierarchical classification. In this Review, we describe the role of machine intelligence in image-based endocrine cancer diagnostics. We first provide a brief overview of AI and consider its intuitive incorporation into the clinical workflow. We then discuss how AI can be applied for the characterization of adrenal, pancreatic, pituitary and thyroid masses in order to support clinicians in their diagnostic interpretations. This Review also puts forth a number of key evaluation criteria for machine learning in medicine that physicians can use in their appraisals of these algorithms. We identify mitigation strategies to address ongoing challenges around data availability and model interpretability in the context of endocrine cancer diagnosis. Finally, we delve into frontiers in systems integration for AI, discussing automated pipelines and evolving computing platforms that leverage distributed, decentralized and quantum techniques.

[Artificial intelligence technologies in clinical neurooncology]

Neurooncology in the 21st century is a complex discipline integrating achievements of fundamental and applied neurosciences. Complex processes and data in clinical neurooncology determine the necessity for advanced methods of mathematical modeling and predictive analytics to obtain new scientific knowledge. Such methods are currently being developed in computer science (artificial intelligence). This review is devoted to potential and range of possible applications of artificial intelligence technologies in neurooncology with a special emphasis on glial tumors. Our conclusions may be valid for other areas of clinical medicine.

A deep learning-based diagnostic tool for identifying various diseases via facial images

With the current health crisis caused by the COVID-19 pandemic, patients have become more anxious about infection, so they prefer not to have direct contact with doctors or clinicians. Lately, medical scientists have confirmed that several diseases exhibit corresponding specific features on the face the face. Recent studies have indicated that computer-aided facial diagnosis can be a promising tool for the automatic diagnosis and screening of diseases from facial images. However, few of these studies used deep learning (DL) techniques. Most of them focused on detecting a single disease, using handcrafted feature extraction methods and conventional machine learning techniques based on individual classifiers trained on small and private datasets using images taken from a controlled environment. This study proposes a novel computer-aided facial diagnosis system called FaceDisNet that uses a new public dataset based on images taken from an unconstrained environment and could be employed for forthcoming comparisons. It detects single and multiple diseases. FaceDisNet is constructed by integrating several spatial deep features from convolutional neural networks of various architectures. It does not depend only on spatial features but also extracts spatial-spectral features. FaceDisNet searches for the fused spatial-spectral feature set that has the greatest impact on the classification. It employs two feature selection techniques to reduce the large dimension of features resulting from feature fusion. Finally, it builds an ensemble classifier based on stacking to perform classification. The performance of FaceDisNet verifies its ability to diagnose single and multiple diseases. FaceDisNet achieved a maximum accuracy of 98.57% and 98% after the ensemble classification and feature selection steps for binary and multiclass classification categories. These results prove that FaceDisNet is a reliable tool and could be employed to avoid the difficulties and complications of manual diagnosis. Also, it can help physicians achieve accurate diagnoses without the need for physical contact with the patients.

Machine Learning in Pituitary Surgery

Machine learning applications in neurosurgery are increasingly reported for diverse tasks such as faster and more accurate preoperative diagnosis, enhanced lesion characterization, as well as surgical outcome, complications and healthcare cost prediction. Even though the pertinent literature in pituitary surgery is less extensive with respect to other neurosurgical diseases, past research attempted to answer clinically relevant questions to better assist surgeons and clinicians. In the present chapter we review reported ML applications in pituitary surgery including differential diagnosis, preoperative lesion characterization (immunohistochemistry, cavernous sinus invasion, tumor consistency), surgical outcome and complication predictions (gross total resection, tumor recurrence, and endocrinological remission, cerebrospinal fluid leak, postoperative hyponatremia). Moreover, we briefly discuss from a practical standpoint the current barriers to clinical translation of machine learning research. On the topic of pituitary surgery, published reports can be considered mostly preliminary, requiring larger training populations and strong external validation. Thoughtful selection of clinically relevant outcomes of interest and transversal application of model development pipeline-together with accurate methodological planning and multicenter collaborations-have the potential to overcome current limitations and ultimately provide additional tools for more informed patient management.

Advancing health equity with artificial intelligence

Population and public health are in the midst of an artificial intelligence revolution capable of radically altering existing models of care delivery and practice. Just as AI seeks to mirror human cognition through its data-driven analytics, it can also reflect the biases present in our collective conscience. In this Viewpoint, we use past and counterfactual examples to illustrate the sequelae of unmitigated bias in healthcare artificial intelligence. Past examples indicate that if the benefits of emerging AI technologies are to be realized, consensus around the regulation of algorithmic bias at the policy level is needed to ensure their ethical integration into the health system. This paper puts forth regulatory strategies for uprooting bias in healthcare AI that can inform ongoing efforts to establish a framework for federal oversight. We highlight three overarching oversight principles in bias mitigation that maps to each phase of the algorithm life cycle.

Application of artificial intelligence and radiomics in pituitary neuroendocrine and sellar tumors: a quantitative and qualitative synthesis

PURPOSE

To systematically review the literature regarding the application of machine learning (ML) of magnetic resonance imaging (MRI) radiomics in common sellar tumors. To identify future directions for application of ML in sellar tumor MRI.

METHODS

PubMed, Medline, Embase, Google Scholar, Scopus, ArxIV, and bioRxiv were searched to identify relevant studies published between 2010 and September 2021. Studies were included if they specifically involved ML of MRI radiomics in the analysis of sellar masses. Risk of bias assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) Tool.

RESULTS

Fifty-eight articles were identified for review. All papers utilized retrospective data, and a quantitative systematic review was performed for thirty-one studies utilizing a public dataset which compared pituitary adenomas, meningiomas, and gliomas. One of the analyzed architectures yielded the highest classification accuracy of 0.996. The remaining twenty-seven articles were qualitatively reviewed and showed promising findings in predicting specific tumor characteristics such as tumor consistency, Ki-67 proliferative index, and post-surgical recurrence.

CONCLUSION

This review highlights the potential clinical application of ML using MRI radiomic data of the sellar region in diagnosis and predicting treatment outcomes. We describe future directions for practical application in the clinical care of patients with pituitary neuroendocrine and other sellar tumors.

Deep learning-based facial image analysis in medical research: a systematic review protocol

INTRODUCTION

Deep learning techniques are gaining momentum in medical research. Evidence shows that deep learning has advantages over humans in image identification and classification, such as facial image analysis in detecting people's medical conditions. While positive findings are available, little is known about the state-of-the-art of deep learning-based facial image analysis in the medical context. For the consideration of patients' welfare and the development of the practice, a timely understanding of the challenges and opportunities faced by research on deep-learning-based facial image analysis is needed. To address this gap, we aim to conduct a systematic review to identify the characteristics and effects of deep learning-based facial image analysis in medical research. Insights gained from this systematic review will provide a much-needed understanding of the characteristics, challenges, as well as opportunities in deep learning-based facial image analysis applied in the contexts of disease detection, diagnosis and prognosis.

METHODS

Databases including PubMed, PsycINFO, CINAHL, IEEEXplore and Scopus will be searched for relevant studies published in English in September, 2021. Titles, abstracts and full-text articles will be screened to identify eligible articles. A manual search of the reference lists of the included articles will also be conducted. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework was adopted to guide the systematic review process. Two reviewers will independently examine the citations and select studies for inclusion. Discrepancies will be resolved by group discussions till a consensus is reached. Data will be extracted based on the research objective and selection criteria adopted in this study.

ETHICS AND DISSEMINATION

As the study is a protocol for a systematic review, ethical approval is not required. The study findings will be disseminated via peer-reviewed publications and conference presentations.

PROSPERO REGISTRATION NUMBER

CRD42020196473.

Facial Recognition Intensity in Disease Diagnosis Using Automatic Facial Recognition

Artificial intelligence (AI) technology is widely applied in different medical fields, including the diagnosis of various diseases on the basis of facial phenotypes, but there is no evaluation or quantitative synthesis regarding the performance of artificial intelligence. Here, for the first time, we summarized and quantitatively analyzed studies on the diagnosis of heterogeneous diseases on the basis on facial features. In pooled data from 20 systematically identified studies involving 7 single diseases and 12,557 subjects, quantitative random-effects models revealed a pooled sensitivity of 89% (95% CI 82% to 93%) and a pooled specificity of 92% (95% CI 87% to 95%). A new index, the facial recognition intensity (FRI), was established to describe the complexity of the association of diseases with facial phenotypes. Meta-regression revealed the important contribution of FRI to heterogeneous diagnostic accuracy (p = 0.021), and a similar result was found in subgroup analyses (p = 0.003). An appropriate increase in the training size and the use of deep learning models helped to improve the diagnostic accuracy for diseases with low FRI, although no statistically significant association was found between accuracy and photographic resolution, training size, AI architecture, and number of diseases. In addition, a novel hypothesis is proposed for universal rules in AI performance, providing a new idea that could be explored in other AI applications.

Machine Learning-based Prediction Model for Treatment of Acromegaly With First-generation Somatostatin Receptor Ligands

CONTEXT

Artificial intelligence (AI), in particular machine learning (ML), may be used to deeply analyze biomarkers of response to first-generation somatostatin receptor ligands (fg-SRLs) in the treatment of acromegaly.

OBJECTIVE

To develop a prediction model of therapeutic response of acromegaly to fg-SRL.

METHODS

Patients with acromegaly not cured by primary surgical treatment and who had adjuvant therapy with fg-SRL for at least 6 months after surgery were included. Patients were considered controlled if they presented growth hormone (GH) <1.0 ng/mL and normal age-adjusted insulin-like growth factor (IGF)-I levels. Six AI models were evaluated: logistic regression, k-nearest neighbor classifier, support vector machine, gradient-boosted classifier, random forest, and multilayer perceptron. The features included in the analysis were age at diagnosis, sex, GH, and IGF-I levels at diagnosis and at pretreatment, somatostatin receptor subtype 2 and 5 (SST2 and SST5) protein expression and cytokeratin granulation pattern (GP).

RESULTS

A total of 153 patients were analyzed. Controlled patients were older (P = .002), had lower GH at diagnosis (P = .01), had lower pretreatment GH and IGF-I (P < .001), and more frequently harbored tumors that were densely granulated (P = .014) or highly expressed SST2 (P < .001). The model that performed best was the support vector machine with the features SST2, SST5, GP, sex, age, and pretreatment GH and IGF-I levels. It had an accuracy of 86.3%, positive predictive value of 83.3% and negative predictive value of 87.5%.

CONCLUSION

We developed a ML-based prediction model with high accuracy that has the potential to improve medical management of acromegaly, optimize biochemical control, decrease long-term morbidities and mortality, and reduce health services costs.

Pituitary Adenomas: From Diagnosis to Therapeutics

Pituitary adenomas are tumors that arise in the anterior pituitary gland. They are the third most common cause of central nervous system (CNS) tumors among adults. Most adenomas are benign and exert their effect via excess hormone secretion or mass effect. Clinical presentation of pituitary adenoma varies based on their size and hormone secreted. Here, we review some of the most common types of pituitary adenomas, their clinical presentation, and current diagnostic and therapeutic strategies.

Computational diagnostic methods on 2D photographs: A review of the literature

Here we provide a literature review of all the methods reported to date for analyzing 2D pictures for diagnostic purposes. Pubmed was used to screen the MEDLINE database using MeSH (Medical Subject Heading) terms and keyworks. The different recognition steps and the main results were reported. All human studies involving 2D facial photographs used to diagnose one or several conditions in healthy populations or in patients were included. We included 1515 articles and 27 publications were finally retained. 67% of the articles aimed at diagnosing one particular syndrome versus healthy controls and 33% aimed at performing multi-class syndrome recognition. Data volume varied from 15 to 17,106 patient pictures. Manual or automatic landmarks were one of the most commonly used tools in order to extract morphological information from images, in 22/27 (81%) publications. Geometrical features were extracted from landmarks based on Procrustes superimposition in 4/27 (15%). Textural features were extracted in 19/27 (70%) publications. Features were then classified using machine learning methods in 89% of publications, while deep learning methods were used in 11%. Facial recognition tools were generally successful in identifying rare conditions in dysmorphic patients, with comparable or higher recognition accuracy than clinical experts.

[AI-assisted Prediction of Lymph Node Metastasis of Breast Cancer: Current and Prospective Research]

One of the most important application of artificial intelligence (AI) in pathology is prediction, using morphological features, of patient prognosis and response to specific treatments. As one of the most common kinds of malignancies in the world and the crucial important cause of death due to malignant tumor among women, breast cancer has become the center of attention in clinical services. Axillary lymph node metastasis is an important prognostic factor in breast cancer. The accuracy of the assessment of axillary lymph node metastasis bears heavily on clinical diagnosis and treatment. At present, based on the principle of non-invasive procedures, many studies have been done to develop models that can be used to predict sentinel lymph node metastasis of breast cancer. However, different clinical and pathological parameters are used in these predictive models. How to analyze the clinical and pathological data of breast cancer patients in a more comprehensive way and how to establish a prediction model with better precision have become the future direction of development. In this paper, we describe the research progress of AI in pathology and the current status of its use in breast cancer research. We have conducted in-depth reflection and looked into the future of ways to predict effectively breast cancer lymph node metastasis and to establish more accurate and effective deep-learning algorithm based on AI assistance so as to continuously improve the diagnosis and treatment of breast cancer.

Forensic human image identification using medical indicators

Diseases not only bring troubles to people's body functions and mind but also influence the appearances and behaviours of human beings. Similarly, we can analyse the diseases from people's appearances and behaviours and use the personal medical history for human identification. In this article, medical indicators presented in abnormal changes of human appearances and behaviours caused by physiological or psychological diseases were introduced, and were applied in the field of forensic identification of human images, which we called medical forensic identification of human images (mFIHI). The proposed method analysed the people's medical signs by studying the appearance and behaviour characteristics depicted in images or videos, and made a comparative examination between the medical indicators of the questioned human images and the corresponding signs or medical history of suspects. Through a conformity and difference analysis on medical indicators and their indicated diseases, it would provide an important information for human identification from images or videos. A case study was carried out to demonstrate and verify the feasibility of the proposed method of mFIHI, and our results showed that it would be important contents and angles for forensic expert manual examination in forensic human image identification.

Patient-Identified Problems and Influences Associated With Diagnostic Delay of Acromegaly: A Nationwide Cross-Sectional Study

BACKGROUND

Insidious-onset acromegaly may easily be overlooked by non-specialists of acromegaly and cause diagnostic delay. This study aims to examine the association between diagnostic delay and advice from doctors before any confirmed diagnosis and subsequent comorbidities, and elicit patient-perceived reasons for misdiagnoses.

METHODS

An online nationwide cross-sectional study was conducted through China Acromegaly Patient Association. Growth Hormone (GH) and Insulin-like Growth Factor 1 (IGF-1) levels at diagnosis and cancerous, endocrine-metabolic, musculoskeletal, cardiovascular, respiratory, and psychiatric comorbidities were reported by patients. The association between diagnostic delay and pre-diagnostic advice from doctors as well as subsequent comorbidities after diagnosis were examined.

RESULTS

In total, 447 valid responses were collected. Overall, 58.8% patients experienced misdiagnoses, and 22.6% had diagnostic delay. Before arriving at any diagnosis, patients without treatment (adjusted odds ratio [AOR]: 3.66, 95% confidence interval [CI]: 1.30-10.33) or receiving treatment to symptoms only (AOR: 7.05, 95%CI: 4.09-12.17) had greater chance of being misdiagnosed, and hence had diagnostic delay. Patients believed insufficient specialists, limited awareness of acromegaly of non-specialists and poor doctor-patient communications were major reasons of misdiagnosis. Diagnostic delay were associated with higher GH level at diagnosis and endocrine-metabolic, musculoskeletal and cardiovascular comorbidities (all P<0.05).

CONCLUSIONS

Suboptimal pre-diagnostic advice for patients, reflecting limited awareness of acromegaly among non-specialists, may delay the diagnosis and increase comorbidities. Feedbacks on the patients' final diagnosis from specialists to non-specialists should be considered, and doctor-patient communication and clinical decision-making process should be improved. Comorbidities should be screened and monitored particularly for patients with diagnostic delay.

Constructing an automatic diagnosis and severity-classification model for acromegaly using facial photographs by deep learning

Due to acromegaly’s insidious onset and slow progression, its diagnosis is usually delayed, thus causing severe complications and treatment difficulty. A convenient screening method is imperative. Based on our previous work, we herein developed a new automatic diagnosis and severity-classification model for acromegaly using facial photographs by deep learning on the data of 2148 photographs at different severity levels. Each photograph was given a score reflecting its severity (range 1~3). Our developed model achieved a prediction accuracy of 90.7% on the internal test dataset and outperformed the performance of ten junior internal medicine physicians (89.0%). The prospect of applying this model to real clinical practices is promising due to its potential health economic benefits.

Facial plastic surgery and face recognition algorithms: Interaction and challenges. A scoping review and future directions

INTRODUCTION

Face recognition (FR) technology can be used in wide range of applications such as identity authentication, access control, and surveillance. Interests and research activities in face recognition have increased significantly over the past twenty years. Plastic surgery procedures can significantly alter facial appearance, thereby posing a serious challenge even to the state-of-the-art face matching algorithms. The purpose of this work was to detail the interaction between facial plastic surgery and facial recognition software and discuss the new challenges of this interaction.

MATERIAL AND METHODS

The authors critically reviewed the literature from January 2000 to September 2019, to identify articles reporting interactions between facial plastic surgery and facial recognition algorithms and discuss the new challenges of these interactions. Controlled vocabulary terms and keywords were used in the search strategy and two authors independently analyzed data. Factors included in the analysis were: Author, Journal, Year, Scope, Study design, Plastic surgery, Data (volume, origin and processing), Identification accuracy and Conclusion (interaction/challenge).

RESULTS

Forty-three research articles underwent data extraction and 28 articles were included in quantitative synthesis. Of the 28 articles, the most common study designs were experimental evaluation (n=15, 53,5%), Evaluation Study studies (n=7, 25%) and Review studies (n=4, 14,3%). Fifty percent of the articles have been published in the last 4 years (14 articles, 50%). Most of the study scope was informatics (64,3%). Only 10 articles were published in medical journals. Rhytidectomy (face lift) is the most challenging procedure for the FR algorithms. Data volume varied from 4 to 2878 subjects. The proposed algorithms provide at least 15 to 99% better identification performance. Among these, only two papers discuss the new challenges of the interaction between facial plastic surgery and Face Recognition Algorithms.

CONCLUSION

In the context of advances in artificial intelligence, Internet connectivity and data integration, the purpose of this review is, to look forward to analyze the new interactions of facial plastic surgery and facial recognition algorithms, and to suggest avenues for future research and clinical application of this technology. Furthermore, to evaluate if plastic surgeons are prepared to discuss this technology with their patients. Plastic surgeons should be prepared to answer questions from patients about the fundamentals of facial recognition technology, and the potential effects of plastic surgery on facial recognition technology performance. Continued efforts are needed to provide scientifically rigorous data of facial biometric identification after facial plastic surgery and to include these notions in the routine consultation or consent process for patients seeking aesthetic facial surgery.

Machine learning applications in imaging analysis for patients with pituitary tumors: a review of the current literature and future directions

PURPOSE

To provide an overview of fundamental concepts in machine learning (ML), review the literature on ML applications in imaging analysis of pituitary tumors for the last 10 years, and highlight the future directions on potential applications of ML for pituitary tumor patients.

METHOD

We presented an overview of the fundamental concepts in ML, its various stages used in healthcare, and highlighted the key components typically present in an imaging-based tumor analysis pipeline. A search was conducted across four databases (PubMed, Ovid, Embase, and Google Scholar) to gather research articles from the past 10 years (2009-2019) involving imaging related to pituitary tumor and ML. We grouped the studies by imaging modalities and analyzed the ML tasks in terms of the data inputs, reference standards, methodologies, and limitations.

RESULTS

Of the 16 studies included in our analysis, 10 appeared in 2018-2019. Most of the studies utilized retrospective data and followed a semi-automatic ML pipeline. The studies included use of magnetic resonance imaging (MRI), facial photographs, surgical microscopic video, spectrometry, and spectroscopy imaging. The objectives of the studies covered 14 distinct applications and majority of the studies addressed a binary classification problem. Only five of the 11 MRI-based studies had an external validation or a holdout set to test the performance of a final trained model.

CONCLUSION

Through our concise evaluation and comparison of the studies using the concepts presented, we highlight future directions so that potential ML applications using different imaging modalities can be developed to benefit the clinical care of pituitary tumor patients.

Identification of the Facial Features of Patients With Cancer: A Deep Learning-Based Pilot Study

BACKGROUND

Cancer has become the second leading cause of death globally. Most cancer cases are due to genetic mutations, which affect metabolism and result in facial changes.

OBJECTIVE

In this study, we aimed to identify the facial features of patients with cancer using the deep learning technique.

METHODS

Images of faces of patients with cancer were collected to build the cancer face image data set. A face image data set of people without cancer was built by randomly selecting images from the publicly available MegaAge data set according to the sex and age distribution of the cancer face image data set. Each face image was preprocessed to obtain an upright centered face chip, following which the background was filtered out to exclude the effects of nonrelative factors. A residual neural network was constructed to classify cancer and noncancer cases. Transfer learning, minibatches, few epochs, L2 regulation, and random dropout training strategies were used to prevent overfitting. Moreover, guided gradient-weighted class activation mapping was used to reveal the relevant features.

RESULTS

A total of 8124 face images of patients with cancer (men: n=3851, 47.4%; women: n=4273, 52.6%) were collected from January 2018 to January 2019. The ages of the patients ranged from 1 year to 70 years (median age 52 years). The average faces of both male and female patients with cancer displayed more obvious facial adiposity than the average faces of people without cancer, which was supported by a landmark comparison. When testing the data set, the training process was terminated after 5 epochs. The area under the receiver operating characteristic curve was 0.94, and the accuracy rate was 0.82. The main relative feature of cancer cases was facial skin, while the relative features of noncancer cases were extracted from the complementary face region.

CONCLUSIONS

In this study, we built a face data set of patients with cancer and constructed a deep learning model to classify the faces of people with and those without cancer. We found that facial skin and adiposity were closely related to the presence of cancer.

Machine Learning Applications in Endocrinology and Metabolism Research: An Overview

Machine learning (ML) applications have received extensive attention in endocrinology research during the last decade. This review summarizes the basic concepts of ML and certain research topics in endocrinology and metabolism where ML principles have been actively deployed. Relevant studies are discussed to provide an overview of the methodology, main findings, and limitations of ML, with the goal of stimulating insights into future research directions. Clear, testable study hypotheses stem from unmet clinical needs, and the management of data quality (beyond a focus on quantity alone), open collaboration between clinical experts and ML engineers, the development of interpretable high-performance ML models beyond the black-box nature of some algorithms, and a creative environment are the core prerequisites for the foreseeable changes expected to be brought about by ML and artificial intelligence in the field of endocrinology and metabolism, with actual improvements in clinical practice beyond hype. Of note, endocrinologists will continue to play a central role in these developments as domain experts who can properly generate, refine, analyze, and interpret data with a combination of clinical expertise and scientific rigor.

Identifying Facial Features and Predicting Patients of Acromegaly Using Three-Dimensional Imaging Techniques and Machine Learning

Objective: Facial changes are common among nearly all acromegalic patients. As they develop slowly, patients often fail to notice such changes before they become obvious. Consequently, diagnosis and treatment are often delayed. So far, convenient and accurate early detection of this disease is still unavailable. This study is designed to combine the use of 3D imaging and machine learning techniques in facial feature analysis and identification of acromegalic patients, in an effort to ascertain how both techniques performed in terms of applicability and value in the early detection of the disease. Methods: One hundred and twenty-four participants including 62 patients with acromegaly and 62 matched controls were enrolled. Using three-dimensional imaging techniques, 58 facial parameters were measured on each face. A two-way analysis of variance (ANOVA) and a post-hoc t-tests were conducted to examine the variations of these parameters with disease status and gender. Using linear discriminant analysis (LDA), we further distinguished patients from controls, characterized what combinations of the parameters could best predict disease state and their relative contributions. Results: Patients are significantly different from normal subjects in many variables, and facial changes of male patients are more significant than female ones. Both male and female patients present following major changes: the increase of facial length and breadth, the widening and elevation of the nose, the thickening of vermilion and the enlargement of the mandible. Facial variables which strongly related to the pathological states can be used to predict the morbid state with high accuracy (prediction accuracies 92.86% in females, p < 0.0001 and 75% in males, p < 0.001). We have further testified that only a few variables play a vital role in disease prediction and the vital combination of variables vary with gender. Conclusions: Three-dimensional imaging enables comprehensive and accurate quantification of facial characteristics, which makes it a promising technique to investigate facial features of acromegalic patients. In combination with machine learning technique, patients can be accurately identified and predicted by their facial variables. This approach might be beneficial for the early detection of acromegalic patients and timely consultation to improve their outcomes.

Italian Association of Clinical Endocrinologists (AME) and Italian AACE Chapter Position Statement for Clinical Practice: Acromegaly - Part 2: Therapeutic Issues

Any newly diagnosed patient should be referred to a multidisciplinary team experienced in the treatment of pituitary adenomas. The therapeutic management of acromegaly always requires a personalized strategy. Normal age-matched IGF-I values are the treatment goal. Transsphenoidal surgery by an expert neurosurgeon is the primary treatment modality for most patients, especially if there are neurological complications. In patients with poor clinical conditions or who refuse surgery, primary medical treatment should be offered, firstly with somatostatin analogs (SSAs). In patients who do not reach hormonal targets with first-generation depot SSAs, a second pharmacological option with pasireotide LAR or pegvisomant (alone or combined with SSA) should be offered. Irradiation could be proposed to patients with surgical remnants who would like to be free from long-term medical therapies or those with persistent disease activity or tumor growth despite surgery or medical therapy. Since the therapeutic tools available enable therapeutic targets to be achieved in most cases, the challenge is to focus more on the quality of life.

Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics

PURPOSE

The aim of this systematic review was to analyse literature on artificial intelligence (AI) and radiomics, including all medical imaging modalities, for oncological and non-oncological applications, in order to assess how far the image mining research stands from routine medical application. To do this, we applied a trial phases classification inspired from the drug development process.

METHODS

Among the articles we considered for inclusion from PubMed were multimodality AI and radiomics investigations, with a validation analysis aimed at relevant clinical objectives. Quality assessment of selected papers was performed according to the QUADAS-2 criteria. We developed the phases classification criteria for image mining studies.

RESULTS

Overall 34,626 articles were retrieved, 300 were selected applying the inclusion/exclusion criteria, and 171 high-quality papers (QUADAS-2 ≥ 7) were identified and analysed. In 27/171 (16%), 141/171 (82%), and 3/171 (2%) studies the development of an AI-based algorithm, radiomics model, and a combined radiomics/AI approach, respectively, was described. A total of 26/27(96%) and 1/27 (4%) AI studies were classified as phase II and III, respectively. Consequently, 13/141 (9%), 10/141 (7%), 111/141 (79%), and 7/141 (5%) radiomics studies were classified as phase 0, I, II, and III, respectively. All three radiomics/AI studies were categorised as phase II trials.

CONCLUSIONS

The results of the studies are promising but still not mature enough for image mining tools to be implemented in the clinical setting and be widely used. The transfer learning from the well-known drug development process, with some specific adaptations to the image mining discipline could represent the most effective way for radiomics and AI algorithms to become the standard of care tools.

Facial Recognition Technology: A Primer for Plastic Surgeons

The face is arguably the most unique and defining feature of the human body. From birth, humans are conditioned to perceive, interpret, and react to information conveyed by faces both familiar and unfamiliar. Although face recognition is routine for humans, only recently has it become possible for a computer to accurately recognize a human face in an image or video. With advances in artificial intelligence, image capture technology, and Internet connectivity, facial recognition technology has entered the forefront of personal and commercial technology. Plastic surgeons should be prepared to answer questions from patients about the fundamentals of facial recognition technology, and the potential effects of plastic surgery on facial recognition technology performance. This article provides an overview of facial recognition technology, describes its present applications, discusses its relevance within the field of plastic surgery, and provides recommendations for plastic surgeons to consider during preoperative discussions with patients.

Computer vision for total laparoscopic hysterectomy

INTRODUCTION

Laparoscopic surgery is widely performed in various surgical fields, but this technique requires time for surgeons to master. However, at the same time, there are many advantages in visualizing the operative field through a camera. In other words, we can visualize what we cannot see with our own eyes by using augmented reality and computer vision. Therefore, we investigated the possibilities and usefulness of computer vision in total laparoscopic hysterectomy.

METHODS

This study was approved by the Mitsui Memorial Hospital ethics committee. Patients who underwent total laparoscopic hysterectomy at Mitsui Memorial Hospital from January 2015 to December 2015 were enrolled. We evaluated 19 cases in which total laparoscopic hysterectomy was performed by the same operator and assistant. We used the Open Source Computer Vision Library for computer vision analysis. The development platform used in this study was a computer operating on Mac OS X 10.11.3.

RESULTS

We created panoramic images by matching features with the AKAZE algorithm. Noise reduction methods improved haziness caused by using energy devices. By abstracting the color of the suture string, we succeeded in abstracting the suture string from movies. We could not achieve satisfactory results in detecting ureters, and we expect that creative ideas for ureter detection may arise from collaborations between surgeons and medical engineers.

CONCLUSIONS

Although this was a preliminary study, the results suggest the utility of computer vision in assisting laparoscopic surgery.

A systematic review on machine learning in sellar region diseases: quality and reporting items

INTRODUCTION

Machine learning methods in sellar region diseases present a particular challenge because of the complexity and the necessity for reproducibility. This systematic review aims to compile the current literature on sellar region diseases that utilized machine learning methods and to propose a quality assessment tool and reporting checklist for future studies.

METHODS

PubMed and Web of Science were searched to identify relevant studies. The quality assessment included five categories: unmet needs, reproducibility, robustness, generalizability and clinical significance.

RESULTS

Seventeen studies were included with the diagnosis of general pituitary neoplasms, acromegaly, Cushing's disease, craniopharyngioma and growth hormone deficiency. 87.5% of the studies arbitrarily chose one or two machine learning models. One study chose ensemble models, and one study compared several models. 43.8% of studies did not provide the platform for model training, and roughly half did not offer parameters or hyperparameters. 62.5% of the studies provided a valid method to avoid over-fitting, but only five reported variations in the validation statistics. Only one study validated the algorithm in a different external database. Four studies reported how to interpret the predictors, and most studies (68.8%) suggested possible clinical applications of the developed algorithm. The workflow of a machine-learning study and the recommended reporting items were also provided based on the results.

CONCLUSIONS

Machine learning methods were used to predict diagnosis and posttreatment outcomes in sellar region diseases. Though most studies had substantial unmet need and proposed possible clinical application, replicability, robustness and generalizability were major limits in current studies.

Patients' perspectives on acromegaly diagnostic delay: a qualitative study

Context Acromegaly has a substantial diagnostic delay associated with an increased risk of comorbidities and psychosocial deterioration. Qualitative methods which focus on the ways that individuals understand and relate to what they are experiencing are the best methods for exploring patients' perspectives. To the best of our knowledge, they have not been developed in the context of acromegaly. Objectives This study aimed to explore the experience of the diagnostic pathway of patients with acromegaly. Design We conducted a qualitative study, based on 20 face-to-face unstructured interviews in a third referral Endocrinology center. Participants, purposively selected until data saturation, were patients with acromegaly with diverse disease durations, types of treatment or associated comorbidities. The data were examined by thematic analysis. Results Our analysis found four themes: (i) what happened for patients before the diagnosis; (ii) what happened after; (iii) the style or type of doctor involved and (iv) patients' suggestions for limiting diagnostic delay. Our findings underlined the direct associations between diagnostic delay and the doctor-patient encounter, and the truly catastrophic experience of this disease, both before and after the diagnosis. Conclusions Diagnosis of acromegaly requires active medical involvement and awareness. Intervention of patient-experts in medical schools may help to be more aware of this disease. Endocrinologists caring for patients with acromegaly should also address the catastrophic dimension of the patient's experience and initiate the narrative to help them to put it into words for preventing harmful consequences such as social isolation and QoL impairment, but also anxiety or depression.

Pragmatic considerations for fostering reproducible research in artificial intelligence

Artificial intelligence and deep learning methods hold great promise in the medical sciences in areas such as enhanced tumor identification from radiographic images, and natural language processing to extract complex information from electronic health records. Scientific review of AI algorithms has involved reproducibility, in which investigators share protocols, raw data, and programming codes. Within the realm of medicine, reproducibility introduces important challenges, including risk to patient privacy, challenges in reproducing results, and questions regarding ownership and financial value of large medical datasets. Scientific review, however, mandates some form of resolution of these inherent conflicts. We propose several approaches to permit scientific review while maintaining patient privacy and data confidentiality.

Acromegaly

Acromegaly is characterized by increased release of growth hormone and, consequently, insulin-like growth factor I (IGF1), most often by a pituitary adenoma. Prolonged exposure to excess hormone leads to progressive somatic disfigurement and a wide range of systemic manifestations that are associated with increased mortality. Although considered a rare disease, recent studies have reported an increased incidence of acromegaly owing to better disease awareness, improved diagnostic tools and perhaps a real increase in prevalence. Acromegaly treatment approaches, which include surgery, radiotherapy and medical therapy, have changed considerably over time owing to improved surgical procedures, development of new radiotherapy techniques and availability of new medical therapies. The optimal use of these treatments will reduce mortality in patients with acromegaly to levels in the general population. Medical therapy is currently an important treatment option and can even be the first-line treatment in patients with acromegaly who will not benefit from or are not suitable for first-line neurosurgical treatment. Pharmacological treatments include somatostatin receptor ligands (such as octreotide, lanreotide and pasireotide), dopamine agonists and the growth hormone receptor antagonist pegvisomant. In this Primer, we review the main aspects of acromegaly, including scientific advances that underlie expanding knowledge of disease pathogenesis, improvements in disease management and new medical therapies that are available and in development to improve disease control.