Diagnostic use of facial image analysis software in endocrine and genetic disorders: review, current results and future perspectives

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 cardiovascular medicine: An updated review of the literature

Screening and early detection of cardiovascular disease (CVD) are crucial for managing progress and preventing related morbidity. In recent years, several studies have reported the important role of Artificial intelligence (AI) technology and its integration into various medical sectors. AI applications are able to deal with the massive amounts of data (medical records, ultrasounds, medications, and experimental results) generated in medicine and identify novel details that would otherwise be forgotten in the mass of healthcare data sets. Nowadays, AI algorithms are currently used to improve diagnosis of some CVDs including heart failure, atrial fibrillation, hypertrophic cardiomyopathy and pulmonary hypertension. This review summarized some AI concepts, critical execution requirements, obstacles, and new applications for CVDs.

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.

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.

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.

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.

Towards an Earlier Diagnosis of Acromegaly and Gigantism

Acromegaly is a rare disease and the clinical features of acromegaly develop insidiously; its diagnosis is often significantly delayed. Therefore, earlier diagnosis will improve the quality of life of the patient and reduce the need for other therapies to control the initial and ongoing damage that acromegaly presents. In this chapter, we describe the view of the patient and the clinician on the importance of earlier diagnosis, as well as on what can be done to speed up this process. Earlier diagnosis will not only improve quality of life and the burden of disease in acromegaly patients, but it will also have a positive impact in the economic burden of this rare disease.

[Selective screening of patients with associated somatic diseases as a method of early detection of acromegaly]

Backgraund

Backgraund: Acromegaly is a multi-organ disabling disease, the effectiveness of treatment of which directly depends on timely diagnosis. Latent course and delayed diagnosis increase the exposure of pathological hypersecretion of growth hormone and insulin-like growth factor-1, contributing to the development of irreversible systemic and metabolic changes in the body that negatively affect survival.

Aims

Aims: The aim of the study was to clinically test a comprehensive diagnostic approach using selective screening to detect cases of acromegaly in patients with combined somatic diseases.

Materials and methods

Materials and methods: The diagnostic search algorithm included a 2-stage questionnaire, expert assessment of the clinical status, laboratory and instrumental examination. The inpatient examination included the use of additional laboratory and instrumental methods and expert evaluation of the results obtained by filling out a doctor’s questionnaire. When the score was higher than 18 points, a more specific examination was performed: double determination of the insulin-like growth factor-1 level, oral glucose tolerance test with determination of the nadir of growth hormone value, and MRI of the brain with contrast enhancement. The diagnosis of acromegaly was made on the basis of personal data, expert assessment of the clinical status, results of laboratory and instrumental examinations.

Results

Results: A survey of 1249 patients with combined systemic and metabolic disorders conducted using the point system allowed us to suspect acromegaly in 367 patients (29.4%), who were offered further examination. The majority of patients were previously seen by specialists for diabetes mellitus (79.3%) or thyroid pathology (10%). In the result of inpatient ­examination of 329 patients, 35 (10.6%) patients showed an increase in the blood level of IGF-I. In 19 patients, a persistent increase in the level of IGF-I was combined with the absence of GH suppression of less than 0.4 ng/ml against the background of glucose load. During MRI in 9 patients, pituitary adenoma was detected (in 2 — microadenoma and 7 — ­macroadenoma).

Conclusions

Conclusions: As a result of the study, among the group of 1249 patients (mean age 58±13 years) with the presence of concomitant diseases, 9 newly identified patients with acromegaly were found who were prescribed adequate treatment. The introduction of selective screening technology into the practice of an endocrinologist will improve the effectiveness of diagnostic search for patients with acromegaly, more accurately assess the prevalence of the disease in Russia and the need for specialized medical care.

A New Clinical Model to Estimate the Pre-Test Probability of Cushing's Syndrome: The Cushing Score

BACKGROUND

Hypercortisolism accounts for relevant morbidity and mortality and is often a diagnostic challenge for clinicians. A prompt diagnosis is necessary to treat Cushing's syndrome as early as possible.

OBJECTIVE

The aim of this study was to develop and validate a clinical model for the estimation of pre-test probability of hypercortisolism in an at-risk population.

DESIGN

We conducted a retrospective multicenter case-control study, involving five Italian referral centers for Endocrinology (Turin, Messina, Naples, Padua and Rome). One hundred and fifty patients affected by Cushing's syndrome and 300 patients in which hypercortisolism was excluded were enrolled. All patients were evaluated, according to current guidelines, for the suspicion of hypercortisolism.

RESULTS

The Cushing score was built by multivariable logistic regression, considering all main features associated with a clinical suspicion of hypercortisolism as possible predictors. A stepwise backward selection algorithm was used (final model AUC=0.873), then an internal validation was performed through ten-fold cross-validation. Final estimation of the model performance showed an average AUC=0.841, thus reassuring about a small overfitting effect. The retrieved score was structured on a 17.5-point scale: low-risk class (score value: ≤5.5, probability of disease=0.8%); intermediate-low-risk class (score value: 6-8.5, probability of disease=2.7%); intermediate-high-risk class (score value: 9-11.5, probability of disease=18.5%) and finally, high-risk class (score value: ≥12, probability of disease=72.5%).

CONCLUSIONS

We developed and internally validated a simple tool to determine pre-test probability of hypercortisolism, the Cushing score, that showed a remarkable predictive power for the discrimination between subjects with and without a final diagnosis of Cushing's syndrome.

Feasibility of using deep learning to detect coronary artery disease based on facial photo

Aims

Facial features were associated with increased risk of coronary artery disease (CAD). We developed and validated a deep learning algorithm for detecting CAD based on facial photos.

Methods and results

We conducted a multicentre cross-sectional study of patients undergoing coronary angiography or computed tomography angiography at nine Chinese sites to train and validate a deep convolutional neural network for the detection of CAD (at least one ≥50% stenosis) from patient facial photos. Between July 2017 and March 2019, 5796 patients from eight sites were consecutively enrolled and randomly divided into training (90%, n = 5216) and validation (10%, n = 580) groups for algorithm development. Between April 2019 and July 2019, 1013 patients from nine sites were enrolled in test group for algorithm test. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated using radiologist diagnosis as the reference standard. Using an operating cut point with high sensitivity, the CAD detection algorithm had sensitivity of 0.80 and specificity of 0.54 in the test group; the AUC was 0.730 (95% confidence interval, 0.699–0.761). The AUC for the algorithm was higher than that for the Diamond–Forrester model (0.730 vs. 0.623, P < 0.001) and the CAD consortium clinical score (0.730 vs. 0.652, P < 0.001).

Conclusion

Our results suggested that a deep learning algorithm based on facial photos can assist in CAD detection in this Chinese cohort. This technique may hold promise for pre-test CAD probability assessment in outpatient clinics or CAD screening in community. Further studies to develop a clinical available tool are warranted.

Time to Diagnosis in Cushing's Syndrome: A Meta-Analysis Based on 5367 Patients

CONTEXT

Signs and symptoms of Cushing's syndrome (CS) overlap with common diseases, such as the metabolic syndrome, obesity, osteoporosis, and depression. Therefore, it can take years to finally diagnose CS, although early diagnosis is important for prevention of complications.

OBJECTIVE

The aim of this study was to assess the time span between first symptoms and diagnosis of CS in different populations to identify factors associated with an early diagnosis.

DATA SOURCES

A systematic literature search via PubMed was performed to identify studies reporting on time to diagnosis in CS. In addition, unpublished data from patients of our tertiary care center and 4 other centers were included.

STUDY SELECTION

Clinical studies reporting on the time to diagnosis of CS were eligible. Corresponding authors were contacted to obtain additional information relevant to the research question.

DATA EXTRACTION

Data were extracted from the text of the retrieved articles and from additional information provided by authors contacted successfully. From initially 3326 screened studies 44 were included.

DATA SYNTHESIS

Mean time to diagnosis for patients with CS was 34 months (ectopic CS: 14 months; adrenal CS: 30 months; and pituitary CS: 38 months; P < .001). No difference was found for gender, age (<18 and ≥18 years), and year of diagnosis (before and after 2000). Patients with pituitary CS had a longer time to diagnosis in Germany than elsewhere.

CONCLUSIONS

Time to diagnosis differs for subtypes of CS but not for gender and age. Time to diagnosis remains to be long and requires to be improved.

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.

Deep-Learning Approach to Automatic Identification of Facial Anomalies in Endocrine Disorders

BACKGROUND

Deep learning has the potential to assist the medical diagnostic process. We aimed to identify facial anomalies associated with endocrinal disorders using a deep-learning approach to facilitate the process of diagnosis and follow-up.

METHODS

We collected facial images of patients with hypercortisolism and acromegaly, and we augmented these images with additional negative samples from public databases. A model with a pretrained deep-learning network was constructed to automatically identify these hypersecretion statuses based on characteristic facial changes. We compared its performance to that of endocrine experts and further investigated key factors upon which the best performing model focused.

FINDINGS

The model achieved areas under the receiver operating characteristic curve of 0.9647 (Cushing's syndrome) and 0.9556 (acromegaly), accuracies of 0.9593 (Cushing's syndrome) and 0.9479 (acromegaly), and recalls of 0.7593 (Cushing's syndrome) and 0.8089 (acromegaly). It performed better than any level of our endocrine experts. Furthermore, the regions of interest on the part of the machine were primarily the same as those upon which the humans focused.

INTERPRETATION

Our findings suggest that the deep-learning model learned the facial characters based merely on labeled data without learning prerequisite medical knowledge, and its performance was comparable with professional medical practitioners. The model has the potential to assist in the diagnosis and follow-up of these hypersecretion statuses.

Toward a Diagnostic Score in Cushing's Syndrome

Cushing's syndrome (CS) is a classical rare disease: it is often suspected in patients who do not have the disease; at the same time, it takes a mean of 3 years to diagnose CS in affected individuals. The main reason is the extreme rarity (1-3/million/year) in combination with the lack of a single lead symptom. CS has to be suspected when a combination of signs and symptoms is present, which together make up the characteristic phenotype of cortisol excess. Unusual fat distribution affecting the face, neck, and trunk; skin changes including plethora, acne, hirsutism, livid striae, and easy bruising; and signs of protein catabolism such as thinned and vulnerable skin, osteoporotic fractures, and proximal myopathy indicate the need for biochemical screening for CS. In contrast, common symptoms like hypertension, weight gain, or diabetes also occur quite frequently in the general population and per se do not justify biochemical testing. First-line screening tests include urinary free cortisol excretion, dexamethasone suppression testing, and late-night salivary cortisol measurements. All three tests have overall reasonable sensitivity and specificity, and first-line testing should be selected on the basis of the physiologic conditions of the patient, drug intake, and available laboratory quality control measures. Two normal test results usually exclude the presence of CS. Other tests and laboratory parameters like the high-dose dexamethasone suppression test, plasma ACTH, the CRH test, and the bilateral inferior petrosal sinus sampling are not part of the initial biochemical screening. As a general rule, biochemical screening should only be performed if the pre-test probability for CS is reasonably high. This article provides an overview about the current standard in the diagnosis of CS starting with clinical scores and screenings, the clinical signs, relevant differential diagnoses, the first-line biochemical screening, and ending with a few exceptional cases.

Predicting Affect Classification in Mental Status Examination Using Machine Learning Face Action Recognition System: A Pilot Study in Schizophrenia Patients

Classifying patients' affect is a pivotal part of the mental status examination. However, this common practice is often widely inconsistent between raters. Recent advances in the field of Facial Action Recognition (FAR) have enabled the development of tools that can act to identify facial expressions from videos. In this study, we aimed to explore the potential of using machine learning techniques on FAR features extracted from videotaped semi-structured psychiatric interviews of 25 male schizophrenia inpatients (mean age 41.2 years, STD = 11.4). Five senior psychiatrists rated patients' affect based on the videos. Then, a novel computer vision algorithm and a machine learning method were used to predict affect classification based on each psychiatrist affect rating. The algorithm is shown to have a significant predictive power for each of the human raters. We also found that the eyes facial area contributed the most to the psychiatrists' evaluation of the patients' affect. This study serves as a proof-of-concept for the potential of using the machine learning FAR system as a clinician-supporting tool, in an attempt to improve the consistency and reliability of mental status examination.

3D Facial Analysis in Acromegaly: Gender-Specific Features and Clinical Correlations

Objective: Quantitative investigations of facial changes in acromegaly are rare. A new imaging technique, three-dimensional (3D) stereophotography, can accurately quantify whole facial changes. We aimed to measure facial characteristics in acromegaly patients using 3D stereophotography, analyze gender-specific features, and explore clinical influencing factors. Design: Single-center case-control study. Methods: Thirty-nine acromegaly patients and 39 age- and gender-matched healthy subjects were prospectively enrolled. 3D stereophotography was performed, and facial lines and angles were quantified for each subject. Clinical information for each acromegaly patient was acquired. Results: The nose width, length, height and depth were longer; the upper and lower lips were thicker; the face length, face width and gonion-gnathion distances were longer; and the nasofrontal and columella-labial angles were smaller in the acromegaly patients, especially in males, than in the healthy controls, with statistical significance (p < 0.05). No differences were found in the face breadth, columella-labial angle, or nose length, height or depth between the female patient and healthy control groups. The insulin-like growth factor 1 (IGF-1) levels in the acromegaly patients were linearly and positively correlated with the nose width (p = 0.006) and gonion-gnathion distance (p = 0.029) and linearly and negatively correlated with the nasofrontal angle (p = 0.026). Conclusions: The acromegaly patients' facial changes exhibit a unique trend, and the characteristics are not identical between genders. 3D stereophotography is an accurate and reliable tool for investigating facial characteristics. Recognizing the above facial features might be potential to assist in the early diagnosis and timely treatment of acromegaly and aid in predicting the severity of systemic complications.

A Survey on Computer Vision for Assistive Medical Diagnosis From Faces

Automatic medical diagnosis is an emerging center of interest in computer vision as it provides unobtrusive objective information on a patient's condition. The face, as a mirror of health status, can reveal symptomatic indications of specific diseases. Thus, the detection of facial abnormalities or atypical features is at upmost importance when it comes to medical diagnostics. This survey aims to give an overview of the recent developments in medical diagnostics from facial images based on computer vision methods. Various approaches have been considered to assess facial symptoms and to eventually provide further help to the practitioners. However, the developed tools are still seldom used in clinical practice, since their reliability is still a concern due to the lack of clinical validation of the methodologies and their inadequate applicability. Nonetheless, efforts are being made to provide robust solutions suitable for healthcare environments, by dealing with practical issues such as real-time assessment or patients positioning. This survey provides an updated collection of the most relevant and innovative solutions in facial images analysis. The findings show that with the help of computer vision methods, over 30 medical conditions can be preliminarily diagnosed from the automatic detection of some of their symptoms. Furthermore, future perspectives, such as the need for interdisciplinary collaboration and collecting publicly available databases, are highlighted.

The risks of overlooking the diagnosis of secreting pituitary adenomas

Secreting pituitary adenomas that cause acromegaly and Cushing’s disease, as well as prolactinomas and thyrotroph adenomas, are uncommon, usually benign, slow-growing tumours. The rarity of these conditions means that their diagnosis is not familiar to most non-specialist physicians. Consequently, pituitary adenomas may be overlooked and remain untreated, and affected individuals may develop serious comorbidities that reduce their quality of life and life expectancy. Because many signs and symptoms of pituitary adenomas overlap with those of other, more common disorders, general practitioners and non-endocrinology specialists need to be aware of the “red flags” suggestive of these conditions. A long duration of active disease in patients with secreting pituitary adenomas is associated with an increased risk of comorbidities and reduced quality of life. Appropriate treatment can lead to disease remission, and, although some symptoms may persist in some patients, treatment usually reduces the incidence and severity of comorbidities and improves quality of life. Therefore, correct, early diagnosis and characterization of a pituitary adenoma is crucial for patients, to trigger timely, appropriate treatment and to optimize outcome. This article provides an overview of the epidemiology of hormonal syndromes associated with pituitary adenomas, discusses the difficulties of and considerations for their diagnosis, and reviews the comorbidities that may develop, but can be prevented, by accurate diagnosis and appropriate treatment. We hope this review will help general practitioners and non-endocrinology specialists to suspect secreting pituitary adenomas and refer patients to an endocrinologist for confirmation of the diagnosis and treatment.