Introduction

Suicide is a major health burden: the World Health Organisation (WHO) reports ~ 785,000 suicides annually, equivalent to one person dying every 40 s, with an incidence of 10.6 per 100,000 population (Naghavi 2019). Epidemiological studies show huge variations in suicides between different geographical areas: although suicides have fallen in China (all people) and India (young people), they are growing in many countries, including the USA, Brazil, and South Korea. The risk of suicide is complex to predict. It is understood to be influenced by the interaction of multiple factors. These include biological (for example, personality factors), clinical (psychiatric and physical health conditions), psychological and social, cultural, and environmental factors. For example, suicidal rates are correlated with economic recession, access to high lethality measures and media reporting of suicides.

The Centers for Disease Control and Prevention report that suicide is the third leading cause of death amongst teenagers and adolescents in the US (Health, United States 2004: with chartbook on trends in the health of Americans and updated tables 2005). Epidemiological studies further indicate that young adults between the ages of 15–21 represent the highest prevalence rates of mental illness at 39% (Eisenberg et al. 2007). Psychiatric conditions associated with suicidal ideation and behaviour include depression, anxiety, substance use disorders, and eating disorders (Brådvik 2018). Some indications of suicidal ideation include an earlier suicide attempt or intentional self-harm behaviour, just as cutting or burning oneself (Korczak et al. 2015). A recent review article reported that language barriers and separation from family were risk factors of suicide, as these factors can lead to a feeling of hopelessness, depression, and anxiety. Other risk factors for suicidality amongst adolescents are poor communication between adolescents and their parents, parental mental health conditions, and intra-family disputes (Korczak et al. 2015). A study by Izadinia et al. (2010) reported that while anxiety, depression, mental health, and everyday stresses were all correlated with suicidal ideations, depression, followed by anxiety, were the main contributors to suicidal ideation (Izadinia et al. 2010). Furthermore, Yeh et al. (2019) established that half of the people who died by suicide had at least one diagnosed mental health condition before the death. Therefore, screening for suicidal risks is a critical step in reducing suicides. This review article focuses on diagnosing depression and anxiety mental health disorders as part of screening for suicidal behaviours and suicide. “Introduction” section describes the background of suicidal ideation and its main contributors. “Traditional screening tools for suicide” section discusses the traditional screening methods used in clinical practice and their limitations. “Biological markers for suicide” section discusses biological markers. “Machine learning tools for diagnosis” section describes the possibility of employing machine learning methods to detect depression and/or anxiety disorder for suicidal ideation identification. In “Methodology for the study” section, the methodology of this review study is explained. In “Summarised studies” section, the summarised studies are described. In “Discussion” section, the findings of the review study are discussed. In “Future avenues for research” section, future avenues for research are proposed, and in “Conclusion” section, the study is concluded.

Traditional screening tools for suicide

Providing support to people who disclose their suicidal ideation is critical in suicide prevention. However, some individuals choose not to seek help for their suicidal ideation, which significantly impedes suicide prevention efforts (Brådvik 2018). For example, although suicidal ideation is generally higher in females (Nock et al. 2008), deaths by suicide are higher in males due to both choices of more lethal methods and reluctance to seek help (World Health Organization 2014). Studies have also shown that many individuals generally prefer consulting their primary care practitioners for emotional concerns than suicidal ideation, specifically due to the stigma associated with suicide (Calear and Batterham 2019). There are various screening tools such as the Columbia Suicide Screen, Risk of Suicide Questionnaire, Suicidal Ideation Questionnaire, Suicidal Ideation Questionnaire JR, Diagnostic Predictive Scales, Suicide Risk Screen and the Suicide Probability Scale (Joe and Bryant 2007) that are widely used by non-mental health professionals for suicide assessment. Additionally, screening programs are largely used in schools to assess suicide risks. For instance, in the first stage of the Teen Screen program, which is described as a model for early suicide prevention intervention, students are tasked to complete the Teen Screen screening questionnaire, after which those identified to be at a higher risk are further assessed through the use of the Diagnostic Interview Schedule for children. In the final stage, a clinician interviews the identified at-risk students (Calear and Batterham 2019). Hence, conventional diagnosis of suicide includes self-reports and clinical interviews.

Limitations of traditional screening methods

However, the afore-mentioned screening tools exhibit some limitations. Studies have shown that a lack of resources due to scarce funding for the assessment programs hinder the implementation of such programs in schools. Furthermore, as educators and school councilors are overwhelmed with the demands of each school day, they are reluctant to implement such risk assessment programs in schools (Mazza 1997). Additionally, several studies that were conducted on the effectiveness of school-based screenings reported a high incidence of false positives (Thompson and Eggert 1999). Also, since most of the suicide screening tools were developed using individuals who were identified as being of white ethnicity (Manetta and Ormand 2005), the tools may not be effective in identifying at-risk adolescents of different ethnicities.

Biological markers for suicide

There are no proven accurate biological markers of suicide that can be integrated into clinical practice. Some demographic and behavioural markers exist (Heeringen and Mann 2014). Forecasting and averting suicidal behaviour is still a budding research field, and several markers have been identified for future study (Nugent et al. 2019). For instance, Niculesu et al. (2017) reported that Apolipoprotein E and interleukin-6 were promising biomarkers for suicidal prediction. Kaminsky et al. (2015) reported epigenetic and genetic markers, including SKA2, as potential markers for suicidality, but further study replication was recommended. Many studies recognized changed sleep architecture to be a biomarker of suicidal thoughts and behaviour (Malik et al. 2014; Bernert et al. 2017; Ballard et al. 2016). The lack of an animal model for suicide (Gould et al. 2017) is a major constraint on basic scientific research, although developing such models is underway. An inadequate number of post-mortem brains available for research poses another challenge (Costanza et al. 2014).

Machine learning tools for diagnosis

The need for AI tools for the diagnosis of suicide risk

The limitations of clinical screening methods for suicide and the frequency of non-disclosure of suicidal ideation mean that 60–70% of individuals who commit suicide are not known to be at risk by their primary care practitioners (Ahmedani et al. 2014). Pourmand et al. (2018) reported in a recent review that adolescents often divulge risk factors for suicide on social media like Facebook and Twitter, even though they don’t disclose them to doctors. Of all the mental health-related tweets shared by large media outlets, around 30% had reference to suicide (Calear and Batterham 2019). Hence researchers have explored information from social media which overtly mentions suicidal thoughts or attempts. In another recent review by Franco et al. (2018), about 3.23% of studies exploring such data were related to machine learning algorithms, highlighting the potential of cutting-edge technological tools to predict suicide risk (Calear and Batterham 2019).

Traditional machine learning models

Machine learning is a sub-field of artificial intelligence (AI). Training of the machine learning model involves a sequence of steps; the input data is usually pre-processed to remove any noise, after which significant features are extracted and selected before the classification process. This workflow is presented in Fig. 1. In conventional machine learning, the system learns from its experience, wherein the system learns the pattern of the input data and responds from its learning, at the output (Voulodimos et al. 2018). At this juncture, the system becomes smarter as it learns the data automatically, without any human intervention (Voulodimos et al. 2018). However, machine learning works well with small data, but the extraction and selection of significant features are manual processes that require human intervention. Some examples of machine learning models commonly used for the classification of diseases include the support vector machine (Cristianini and Shawe-Taylor 2000), decision tree (Kingsford and Salzberg 2008), probabilistic neural networks (Specht 1990), k-nearest neighbour (Hu et al. 2016), and artificial neural networks (Grossi and Buscema 2007). Such conventional machine learning models have been fervently used in the classification of some mental illnesses such as schizophrenia (Sharma and Acharya 2021; Jahmunah et al. 2019), depression (Sharma et al. 2018a), Parkinson’s disease (Tuncer et al. 2020), and Alzheimer’s disease (Wei et al. 2020).

Fig. 1
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Workflow required for training a machine learning model

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Advanced deep learning models

On the contrary, in more advanced machine learning, deep learning models with several layers between the input and output layers are used for classification purposes (Sharma et al. 2021a). Unlike conventional models, these models learn large input data before predicting a classification outcome. Furthermore, in contrast to traditional classifiers in deep models, the feature extraction and selection processes are automatically done by the model, without requiring human aid. Some examples of deep models commonly used for the classification of diseases include the convolutional neural network (CNN) (Sharma et al. 2018b), long short-term memory (LSTM) (Houdt et al. 2020), and autoencoders (Lopez Pinaya et al. 2019).

A CNN model comprises three main layers; convolution, pooling, and fully connected layers. The convolution and pooling layers aid in creating new feature maps in each succeeding layer, enabling the extraction of more complex features from the input data deeper into the network. The fully connected layers provide the output of the classification (Sharma et al. 2018b). Autoencoders comprise two main components known as the encoder, in which the model reduces the feature size and presents the input data into an encoded representation, and the decoder, wherein the model re-creates the data from the encoded version, such that it represents the original data very closely (Lopez Pinaya et al. 2019). The LSTM mainly comprises the input, forget, and output gates that control the information stored, read, and written on the cell, respectively, with the onset of input data. The model works by recollecting crucial information from previous states and building on them (Houdt et al. 2020). These models have been successfully employed in the automated detection of mental health conditions such as Parkinson’s disease (Oh et al. 2018), depression (Ay et al. 2019), and schizophrenia (Oh et al. 2020). Hence, machine learning techniques have also been employed efficaciously to detect mental health disorders. Figures 2a–c illustrate the architectures of the CNN, autoencoder, and LSTM deep models, respectively. The figures depict the workings of the models when the input data is fed to them.

Fig. 2
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Architectures of the CNN (a), autoencoder (b), and LSTM (c) models

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Methodology for the study

This review study was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to select the most relevant studies on AI tools developed for depression and/or anxiety diagnosis. To analyze more recent articles, the search was conducted between the years 2013 and 2022. The appropriate journal articles were searched through the Institute of Electrical and Electronics Engineers (IEEE), Google Scholar, PubMed, Science Direct, and Springer Link scientific repositories. The Boolean search strings such as “Machine Learning”, “Deep Learning”, “Artificial intelligence tools”, “Depression”, “Anxiety disorders” and “Suicidal ideation” were used in various combinations as perceived in Table 1. Three key processes were involved in the retrieval of articles based on the PRISMA guidelines. Initially, a total of 48,407 articles were identified based on the Boolean search strings for depression and anxiety disorder detection, wherein, for depression detection, 119,17 801, 8, 4771, and 3963 articles were retrieved from the IEEE, Google Scholar, PubMed, Science Direct and Springer link repositories respectively. For anxiety disorder detection, 16, 17,400, 3, 2211, and 2116 articles were retrieved from the aforementioned repositories. Then, articles were screened to eradicate duplicate and irrelevant articles, based on the inclusion and exclusion criteria, wherein articles on ‘mental health’, ‘stress’, ‘treatment’ or ‘treatment response’, ‘monitoring’, and ‘management’ were all excluded. Furthermore, theses, books, and abstracts were also omitted. The final number of articles relevant to this review were selected and set to 76, as seen in Tables 2, 3, 4 and 5. Figure 3 details how the PRISMA guideline was used to select the most relevant articles in this review.

Fig. 3
figure 3

Selection of relevant articles based on PRISMA guidelines

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The search was conducted between May to June 2021. Studies were included if they met the following criteria:

  1. (i)

    They described the use of AI tools to diagnose depression and/or anxiety

  2. (ii)

    They were published between the years 2013 and 2022,

  3. (iii)

    They were published in a peer-reviewed journal,

  4. (iv)

    They were published in English.

Studies were excluded if:

  1. (i)

    They described the use of AI tools to diagnose depression and/or anxiety together with other conditions or disorders

  2. (ii)

    The article was not published in English

  3. (iii)

    The article was not published in a peer-reviewed journal

  4. (iv)

    The article was published before 2013

Table 1 Results of the Boolean search string for the respective repositories
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Summarised studies

Tables 2, 3, 4 and 5 summarize studies for detecting depression, anxiety disorder, suicidal ideation, and depression and anxiety disorder using AI tools, respectively. Comparing the tables, it can be deduced that most of the studies focus on detecting depression followed by depression and anxiety disorder as comorbid conditions. From the tables, it is also notable that most authors have successfully investigated audio and/or facial features for the detection of depression and/or anxiety disorder (Ooi et al. 2013; Zhou et al. 2015; Williamson et al. 2016; Pampouchidou et al. 2015; Pampouchidou et al. 2020; Yang et al. 2016, 2017; Dham et al. 2017; Alhanai et al. 2018; He and Cao 2018; Afshan et al. 2018; Zhu et al. 2018; Venkataraman 2018; Gavrilescu and Vizireanu Aug. 2019; Melo et al. 2019; Victor et al. 2019; Chlasta et al. 2019; Guntuku et al. 2019; Detecting Depression Using a Framework Combining Deep Multimodal Neural Networks with a Purpose-Built Automated Evaluation xxxx; Vázquez-Romero and Gallardo-Antolín 2020; Quatieri et al. 2020; Shinde et al. 2020; Zhang et al. Jul. 2020; Espinola et al. 2021; Matteo et al. 2021; Guo et al. 2021; Albuquerque et al. 2021). Additionally, some authors have widely analysed the brain signals (Sharma et al. 2018a, 2021b; Ay et al. 2019; Faust et al. 2014; Acharya et al. 2015; Bairy et al. 2016; Liao et al. 2017; Cai 2018; Uyulan et al. 2020; Qiao et al. 2020; Thoduparambil et al. 2020; Saeedi et al. 2020, 2021; Xie, et al. 2020; Qayyum et al. 2020; Khan et al. 2021; Seal et al. 2021; Bai et al. 2021). Some authors have also scoured and analysed texts from social media such as Twitter, Facebook and Reddit (Thoduparambil 2020; Saeedi 2020; Xie et al. 2020; Islam et al. 2018; Eichstaedt et al. 2018; Cacheda et al. 2019; Trotzek et al. 2020; Owen et al. 2020; Ramírez-Cifuentes et al. 2020; Safa et al. 2021; Tong et al. 2022; Gupta et al. 2022; Stankevich et al. 2018, 2020; Hussain et al. 2019; Alsagri and Ykhlef 2020). A few authors have explored the combination of audio and textural features (Alhanai et al. 2018; Park and Moon 2022), audio and visual recordings (Yang et al. 2017; Mallol-Ragolta et al. 2020; Saidi et al. 2020) while some others have used unique methods such as a combination of time series signal features (Zhou et al. 2015), measurement of electrodermal activity (Kim et al. 2018), magnetic resonance imaging (Kipli et al. 2013; Yamashita et al. 2020; Boeke et al. 2020), kinematic skeleton data (Li et al. 2021), photo-plethysmogram(PPG) signal features extraction (Khandoker 2017), gait characteristics (Wang et al. 2021) and optical flow visual-based method (Zhu et al. 2018). Haritha et al. (2017) explored respiratory signals for anxiety detection. However, apart from Ramirez et al. (2020) all these studies had only investigated the detection of depression and/or anxiety disorder alone, without linking to suicidal ideation. Ramirez et al. (2020) had uniquely assessed the suicide risk among social media users using text-based, statistical, behavioural, and image features. The study concludes that textural and behavioural features are the most promising for suicide risk assessment.

Table 2 Summary of studies for detecting depression using AI tools
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Table 3 Summary of study for the detection of anxiety disorder using AI tools
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Table 4 Summary of study for the detection of suicidal ideation using AI tools
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Table 5 Summary of studies for the detection of depression and anxiety disorder using AI tools
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Discussion

Figure 4 details the different sources of data utilized by authors for the detection of depression and/or anxiety disorder. The figure shows that publicly available databases were used most widely, followed by data obtained from hospitals or research centres. The recording or construction of data were probably less commonly considered as these are often time-consuming and tedious, as compared to obtaining data from publicly available databases effortlessly. From Fig. 5, it is observable that mostly audio and/or facial video features, followed by EEG signals and texts from social media, were analyzed for the detection of depression and/or anxiety disorder. Also, from Fig. 6, it is evident that there are more studies on the detection of depression as compared to the detection of anxiety disorder or depression and anxiety disorder jointly. Furthermore, it can be reckoned from Fig. 6 that the trend for depression detection has been increasing from 2013 to 2022. It is also noticeable that more studies have been conducted for depression and/or anxiety disorder detection from 2019 to 2022. Within the same year range, a study on suicidal risk assessment was conducted in 2020. These could possibly be due to the hike in depression and anxiety disorders during the COVID-19 pandemic in children and adolescents (Śniadach et al. 2021). Figure 7 shows the number of studies that employed conventional machine learning and advanced deep learning models. From the figure, it is comprehendible that conventional machine learning techniques have been most commonly developed for the detection of depression and/or anxiety disorders. Furthermore, deep learning models have been explored since 2017, peaking in 2020, contending that these models have been gaining popularity in recent years.

Fig. 4
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Different sources of data

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Fig. 5
figure 5

Pie chart representing the different types of input features used to detect depression and/or anxiety disorder

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Fig. 6
figure 6

Bar graph detailing the number of studies conducted to detect depression and/or anxiety disorder from 2013 to 2022

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Fig. 7
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Bar graph showing number of studies that employed traditional machine learning and advanced deep learning models in Tables 2, 3, 4 and 5

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Rejaibi et al. (2022) discussed that their recommended method of employing the recurrent neural network on audio features has some limitations wherein the generalization ability of this method to other datasets may be weaker as the shift from ideal circumstances of speech acquisition to speech in the rough conditions will increase the error rate of the method. They also compared the findings from other multimodal experiments and discussed that adding more features to the deep model enabled it to gain more knowledge about the identification of depression and was hence able to detect depression with higher accuracy. However, the computational complexity increases. They also stated that while adding visual features to the model increased the model performance by 20%, it appeared to be intrusive and invasive to patients. Park et al. (2022) conferred that their proposed method has limitations such as the inability to subdivide depression into mild or severe due to the way the model was developed in their study. They also discussed that the multimodal analysis of depression using text and voice data enhanced the classification accuracy as compared to using single data. Nasir et al. (2016) discussed that using geometric together with facial marker features improved the F1 score of their i-vector model compared to using the features separately. Adding polynomial feature sets to this resulted in a decline in model performance due to overfitting. They asserted that the i-vector model performed the best for audio features and polynomial parameterization of facial and geometrical features acted as the best video feature set. Yang et al. (2017) discussed that their developed hybrid DCNN-DNN model performed better than existing models, wherein text and semantic features had also been fused for depression detection besides audio and visual features.

Ramirez et al. (2020) debated that their observational study presents some limitations such as having no access to personal and medical information. Hence, the study lacks representativeness wherein analyses on gender, age, and location of users were not performed due to the lack of such information on Twitter. They also reported that while they had used text-based, statistical, behavioural, and image features for suicide risk assessment, the results could be improved by increasing textual and relational features.

Some authors also conducted a review study on depression detection. For instance, Wu et al. (2022) conducted a similar review study on depression detection using speech signals. While the authors reported that there has been a shift from exploring auditory features to deep model for speech depression recognition, they recommended overcoming depression detection challenges by collecting clinical information on depression to explore the core mechanism of speech in depression. They also concluded that combining multiple modalities for accurate and effective depression analysis is a possible trend in future research. While the review study by Wu et al. (2022) is also on depression detection, the authors focus more on employing deep learning methods with speech signals for depression detection, a contrast to this review study, which focuses on machine learning and deep learning models using various types of datasets for depression detection. We also quite differently propose the feature fusion method for future work. Nasser et al. (2020) conducted a review study on depression detection based on traditional machine learning methods using visual facial cues. The authors concluded that the Support Vector Machine technique is recommended for visual feature extraction methods for depression detection, due to the high accuracy obtained with a large number of subjects and with the usage of action units of full face. This review deviates from ours, as it only focuses on traditional machine learning techniques and visual facial cues as features for depression detection. In contrast to our study, William et al. (2021) conducted a review study on depression detection based on texts from social media. Based on the findings from the review, it has been established that the use of classifiers, support vector machines and probabilistic classifiers is the most common approach for depression detection using text analysis and that the BiLSTM combined with attention method generated the best results.

Salas-Zarate et al. (2022) also conducted a review study on detecting depression signs using social media. While the focus of this study is different from ours, the findings also vary. It has been established from the study that Twitter was the most studied social media, and word embedding was the most commonly employed linguistic feature extraction method and the support vector machine was the most prominent classifier that was used, for depression detection. In contrast to our study, Liu et al. (2022) reviewed studies that focused on a machine learning to determine depressive symptoms based on text mining for sentiment identification using social media data. The authors concluded that machine learning techniques could be effective in depression detection using text data from social media. In a different review study conducted by Guntuku et al. (2017), the authors analyzed the diverse approaches that were used to collate social media data comprising information regarding the users’ mental health. This varies from our study, which focuses on detecting depression and anxiety. The authors concluded that while depression and other mental illnesses are identifiable in many online environments, the generalizability of these studies to wider samples and benchmark for clinical criteria has not been determined.

Zhang et al. (2021) had conducted a review study on depression detection using virtual reality. The authors construed that while using virtual reality for depression detection has been increasingly acknowledged and virtual reality games have the potential to be designed for depression detection, they need to be improved. In another review study, Joshi et al. (2022) analysed how facial expressions, images, texts on social media, and emotional chatbots can effectively detect an individual’s emotions and depression. Various AI methods that have been employed for the analysis were discussed. From the findings, the authors established that depression, mood, and emotion could be detected by analyzing texts, videos, speech, gestures, or images through the employment of various machine learning and artificial intelligence-based models. In the review study by Aleem et al. (2022), the authors described various machine learning algorithms, along with their objectives and drawbacks, used for depression detection. They concluded that the support vector machine was the most prevalently used model, yielding high accuracies of above 75% for depression detection.

Hence, from the discussions above, it is clear that our review study differs from existing reviews in terms of focus and findings. Furthermore, based on the limitations and effects of modalities collated from some studies, it can be elucidated that using a fusion of features such as audio, visual, textual, and so on generally increases the classification accuracy of a deep model for depression detection, as the model gains more knowledge about depression identification. For instance, combining text and voice data improved the model’s performance as compared to using just a single data in the study by Park et al. (2022). However, one needs to be cautious about the type of features being analysed for different types of deep models. For instance, while adding visual features on top of speech signals increased the model’s performance in the study by Rejaibi et al. (2022), the computational complexity of the model increased. Furthermore, obtaining visual features can also appear as being intrusive and invasive to patients. Also, while the combination of the geometric and facial marker features improved the performance of the i-vector model, the addition of other features, like the polynomial, caused a decline in the model’s performance due to overfitting. Thus, while feature fusion is recommended, the types and number of features used depend on the type of deep model being developed, as quick depression detection is imperative in real-time settings.

From Tables 2, 3, 4 and 5, it is also notable that most authors had employed AI techniques for the studies. Some studies report high classification accuracies (95% and above) (Sharma et al. 2018a, 2021b; Ay et al. 2019; Afshan et al. 2018; Faust et al. 2014; Acharya et al. 2015; Uyulan et al. Jun. 2020; Thoduparambil et al. 2020; Saeedi et al. 2020, 2021; Qayyum et al. 2020; Khan et al. 2021; Seal et al. 2021; Khandoker 2017; Lech 2018; Tao et al. 2021) asserting the efficacy of using AI techniques for the detection of depression and/or anxiety disorder. There are advantages of using AI techniques for depression and/or anxiety detection, but there are pitfalls. Hence, the advantages and disadvantages of this review study are discussed below.

Advantages

  1. 1.

    Detection of depression and/or anxiety using AI can be more rapid hence individuals with suicidal ideation can be identified faster.

  2. 2.

    Depression and/or anxiety can be more accurately detected using AI tools, enabling suicidal ideation to be identified accurately.

  3. 3.

    Using AI tools is cost-effective in diagnosing anxiety and/or depression, hence identifying suicidal ideation.

Limitations

  1. 1.

    Using AI techniques for suicidal ideation may be presented with ethical (McKernan et al. 2018) and privacy issues (Gomes de Andrade et al. 2018).

  2. 2.

    This study has not identified large databases of various ethnicities to better represent the global population.

  3. 3.

    While this review discusses AI techniques for detecting depression and/or anxiety disorder, the features that best determine suicidal ideation in patients with depression and/or anxiety disorder have not been discovered.

Future avenues for research

Our review study underscores the significance of depression and/or anxiety disorder detection for identifying suicidal ideation, especially due to the unprecedented challenges brought about by the current COVID-19 pandemic. Findings from our review study demonstrate that audio and/or facial video features have been analyzed predominantly to detect depression and/or anxiety disorder. Furthermore, that machine learning techniques effectively detect depression and/or anxiety disorder. However, the features that best determine suicidal ideation in patients with depression and/or anxiety disorder have not been identified. Hence in our future work, we plan to gather large data from various ethnicities and propose to extract features such as facial images, EEG signals, speech signals, visual and clinical history features from huge population. The combination of features that best reduce the computational complexity of model, will be selected. The deep learning model which would train this feature set, would be kept in a secured cloud server. The classification result of the model (suicidal ideation is present/not present) would then be sent instantly to the mobile phones of involved clinicians, such as a psychiatrist, to assist in the stratification of clinical care. This idea is elucidated in Fig. 8.

Fig. 8
figure 8

Proposed scheme for the detection of suicidal ideation in individuals

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Conclusion

Suicidal rates have been increasing globally, with adolescents having the highest prevalence rates of mental illness at 39%, wherein depression and anxiety mental health disorders have been identified as the main contributors to suicidal ideation. Traditional diagnostic methods such as self-report questionnaires and clinical interviews exhibit several limitations. AI tools have been employed to diagnose various diseases, including neurological illnesses. Hence this review paper summarizes studies that employed AI tools for the automated detection of depression and/or anxiety disorders. From the summary, it is apparent that AI tools are promising and can overcome the limitations of traditional depression and/or anxiety diagnostic methods. This study has also established that audio and/or facial video features have been most commonly analysed, followed by EEG signals, to detect depression and/or anxiety disorder. However, one main limitation of this review study is that the features that best determine suicidal ideation in patients with depression and/or anxiety disorder have not been discovered. Leveraging the recommended features for suicidal risk assessment by Ramirez et al. (2020), we hope to address this limitation in our future studies.