Digital transformation has reconfigured healthcare systems, by integrating digital technologies into clinical and administrative processes, driving improvements in the quality of services, patient safety and the organizational efficiency of institutions. In addition to the computerization of clinical records, the digitalization has driven the adoption of advanced clinical decision support systems, based on artificial intelligence, interoperability between information systems and automated analysis of large volumes of data in real time.
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FROM MAMMOGRAPHIC REPORTS IN PORTUGUESE TO STRUCTURED CLINICAL DATA: AN AUTOMATIC TRANSFORMATION
Publication type: Article Summary
Original title: Implementação de uma ferramenta de suporte à entrada de texto em Português, escrito e falado, para o sistema de apoio à decisão clínica MammoClass
Article publication date: October 2016
Source: Repositório Aberto da Universidade do Porto
Author: Ricardo Rocha
Supervisor: Inês Dutra
What is the goal, target audience, and areas of digital health it addresses?
The aim of the study is to improve the MammoClass tool, allowing information from mammograms dictated or written in Portuguese to be converted into standardized terms, facilitating clinical analysis and prediction of the risk of malignancy of breast lesions. The target audience includes health professionals, particularly radiologists and primary care doctors, as well as health technology researchers and decision-makers involved in the management and digital transformation of clinical services. This study falls within the areas of digital health, with relevance to artificial intelligence applied to health, voice-based interfaces, interoperability of clinical information systems and medical decision support tools.
What is the context?
Digital transformation has reconfigured healthcare systems, by integrating digital technologies into clinical and administrative processes, driving improvements in the quality of services, patient safety and the organizational efficiency of institutions. In addition to the computerization of clinical records, the digitalization has driven the adoption of advanced clinical decision support systems, based on artificial intelligence, interoperability between information systems and automated analysis of large volumes of data in real time.
In the field of breast imaging, these technologies are particularly important given the complexity of imaging data and the need for rapid and accurate decisions, especially in light of the high incidence of breast cancer. The disease is characterised by uncontrolled cell proliferation in breast tissue, with a high potential for local invasion and spread to other organs. Despite the increased screening efforts, indicators such as mortality rates remain worrying, underscoring the urgent need for digital solutions capable of promoting earlier detection, more accurate risk stratification and a reduction in the clinical and social impact of the disease.
What are the current approaches?
Currently, the approaches used to support clinical decision-making in breast imaging are largely based on standardising the interpretation of exams and using computer tools based on structured data. Mammography (X-ray of the breast) remains the main imaging test in screening programmes, playing a central role in the early detection of potentially malignant alterations. To standardise the description of results and facilitate communication between health professionals, the Breast Imaging Reporting and Data System (BI-RADS®) was developed, which establishes a standardised lexicon with 43 specific descriptors for characterising breast structures. These include the presence of dark margins or an irregular shape of the lesion. Based on the combination of these and other parameters, radiologists assign a category from 1 to 7, with higher values indicating a higher risk of malignancy.
This structured data is essential for feeding clinical decision support systems such as MammoClass, a platform that allows you to predict the likelihood of a mammographic alteration being benign or malignant based on an analysis of BI-RADS® descriptors. However, in clinical practice, most reports are still predominantly written in free text, through dictation or handwriting, which makes efficient conversion to structured formats difficult and limits data interoperability.
To mitigate this limitation, some institutions use voice recognition technologies, which allow clinical dictation to be transcribed automatically. However, when applied to the Portuguese language, these tools have high error rates, especially in environments with background noise or in the face of regional variations in pronunciation. In addition, these solutions are generally designed for full speech transcription and are not optimised for the selective identification and extraction of relevant clinical terminology. As a result, the quality and reliability of the extracted data is compromised.
What does innovation consist of? How is the impact of this study assessed?
The innovation of this study consisted of the development of MammoClass V2, an expanded and updated version of the original system, which incorporates new functionalities designed to facilitate the entry and structuring of clinical data in mammograms. This version introduces an approach centred on the automatic extraction of BI-RADS® descriptors from clinical text written in Portuguese, promoting the direct integration of this structured data into the clinical decision support system. Instead of transcribing the entire speech, the system focuses exclusively on identifying and extracting the relevant descriptors, ignoring the rest of the content.
To this end, a multi-platform web interface was developed (accessible by computer, tablet and smartphone), which allows clinical text to be entered — in Portuguese — using three different formats: free writing, form filling and dictation with support for voice recognition technologies (Speech-to-Text). The extraction of the terms is carried out by a specialised linguistic parser, designed to recognise clinical terminology even when expressed with grammatical variations or in different linguistic sequences, ensuring greater robustness and flexibility in textual interpretation.
To evaluate this system, a methodological approach was designed that combined quantitative tests. The quantitative component included two data input scenarios: (i) lists of BI-RADS® descriptors previously defined and individually dictated and (ii) complete clinical reports written in natural language from mammograms performed at Centro Hospitalar São João, in Porto. At the same time, two different speech recognition technologies were compared: the Web Speech API, based on Google cloud services, and the Julius/Coruja system, an open-source tool with local execution, guaranteeing greater control and privacy of the processed data.
What are the main results? What is the future of this approach?
The results of this study showed that the approach developed enabled the automatic extraction of BI-RADS® descriptors from clinical text in Portuguese, with consistent performance. In quantitative tests, the system achieved an average accuracy of 81 percent and a sensitivity of over 85 percent, both on lists of individually dictated terms and on complete clinical reports written in natural language. These figures showed the tool’s ability to correctly identify relevant clinical terminology and reduce omissions. The linguistic parser proved to be effective in detecting descriptors expressed with grammatical variations, different verb tenses or different syntactic orders, demonstrating flexibility in natural language processing. This performance was particularly evident in texts written or voice-dictated, often found in clinical practice.
As far as voice recognition is concerned, the Web Speech API performed better in terms of transcription fidelity, especially in quiet environments and with speakers with neutral diction. On the other hand, the Julius/Coruja system, although with a slight reduction in the recognition rate, proved to be advantageous as it operates locally, offering greater privacy and independence from external connectivity – characteristics that are relevant to sensitive clinical contexts.
Despite these advances, the solution presents opportunities for further development, particularly in terms of expanding the recognised clinical vocabulary, resilience to less controlled contexts and the integration of more advanced language models. In the short term, pilot studies are planned in healthcare institutions to validate the tool’s impact on clinical practice, especially in the production and standardisation of reports. In the medium term, it is anticipated that the approach will be extended to other areas of imaging and that language models based on machine learning will be incorporated, with a view to improving sensitivity without compromising clinical specificity.
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Home / Publications / Publication
FROM MAMMOGRAPHIC REPORTS IN PORTUGUESE TO STRUCTURED CLINICAL DATA: AN AUTOMATIC TRANSFORMATION
Publication type: Article Summary
Original title: Implementação de uma ferramenta de suporte à entrada de texto em Português, escrito e falado, para o sistema de apoio à decisão clínica MammoClass
Article publication date: October 2016
Source: Repositório Aberto da Universidade do Porto
Author: Ricardo Rocha
Supervisor: Inês Dutra
What is the goal, target audience, and areas of digital health it addresses?
The aim of the study is to improve the MammoClass tool, allowing information from mammograms dictated or written in Portuguese to be converted into standardized terms, facilitating clinical analysis and prediction of the risk of malignancy of breast lesions. The target audience includes health professionals, particularly radiologists and primary care doctors, as well as health technology researchers and decision-makers involved in the management and digital transformation of clinical services. This study falls within the areas of digital health, with relevance to artificial intelligence applied to health, voice-based interfaces, interoperability of clinical information systems and medical decision support tools.
What is the context?
Digital transformation has reconfigured healthcare systems, by integrating digital technologies into clinical and administrative processes, driving improvements in the quality of services, patient safety and the organizational efficiency of institutions. In addition to the computerization of clinical records, the digitalization has driven the adoption of advanced clinical decision support systems, based on artificial intelligence, interoperability between information systems and automated analysis of large volumes of data in real time.
In the field of breast imaging, these technologies are particularly important given the complexity of imaging data and the need for rapid and accurate decisions, especially in light of the high incidence of breast cancer. The disease is characterised by uncontrolled cell proliferation in breast tissue, with a high potential for local invasion and spread to other organs. Despite the increased screening efforts, indicators such as mortality rates remain worrying, underscoring the urgent need for digital solutions capable of promoting earlier detection, more accurate risk stratification and a reduction in the clinical and social impact of the disease.
What are the current approaches?
Currently, the approaches used to support clinical decision-making in breast imaging are largely based on standardising the interpretation of exams and using computer tools based on structured data. Mammography (X-ray of the breast) remains the main imaging test in screening programmes, playing a central role in the early detection of potentially malignant alterations. To standardise the description of results and facilitate communication between health professionals, the Breast Imaging Reporting and Data System (BI-RADS®) was developed, which establishes a standardised lexicon with 43 specific descriptors for characterising breast structures. These include the presence of dark margins or an irregular shape of the lesion. Based on the combination of these and other parameters, radiologists assign a category from 1 to 7, with higher values indicating a higher risk of malignancy.
This structured data is essential for feeding clinical decision support systems such as MammoClass, a platform that allows you to predict the likelihood of a mammographic alteration being benign or malignant based on an analysis of BI-RADS® descriptors. However, in clinical practice, most reports are still predominantly written in free text, through dictation or handwriting, which makes efficient conversion to structured formats difficult and limits data interoperability.
To mitigate this limitation, some institutions use voice recognition technologies, which allow clinical dictation to be transcribed automatically. However, when applied to the Portuguese language, these tools have high error rates, especially in environments with background noise or in the face of regional variations in pronunciation. In addition, these solutions are generally designed for full speech transcription and are not optimised for the selective identification and extraction of relevant clinical terminology. As a result, the quality and reliability of the extracted data is compromised.
What does innovation consist of? How is the impact of this study assessed?
The innovation of this study consisted of the development of MammoClass V2, an expanded and updated version of the original system, which incorporates new functionalities designed to facilitate the entry and structuring of clinical data in mammograms. This version introduces an approach centred on the automatic extraction of BI-RADS® descriptors from clinical text written in Portuguese, promoting the direct integration of this structured data into the clinical decision support system. Instead of transcribing the entire speech, the system focuses exclusively on identifying and extracting the relevant descriptors, ignoring the rest of the content.
To this end, a multi-platform web interface was developed (accessible by computer, tablet and smartphone), which allows clinical text to be entered — in Portuguese — using three different formats: free writing, form filling and dictation with support for voice recognition technologies (Speech-to-Text). The extraction of the terms is carried out by a specialised linguistic parser, designed to recognise clinical terminology even when expressed with grammatical variations or in different linguistic sequences, ensuring greater robustness and flexibility in textual interpretation.
To evaluate this system, a methodological approach was designed that combined quantitative tests. The quantitative component included two data input scenarios: (i) lists of BI-RADS® descriptors previously defined and individually dictated and (ii) complete clinical reports written in natural language from mammograms performed at Centro Hospitalar São João, in Porto. At the same time, two different speech recognition technologies were compared: the Web Speech API, based on Google cloud services, and the Julius/Coruja system, an open-source tool with local execution, guaranteeing greater control and privacy of the processed data.
What are the main results? What is the future of this approach?
The results of this study showed that the approach developed enabled the automatic extraction of BI-RADS® descriptors from clinical text in Portuguese, with consistent performance. In quantitative tests, the system achieved an average accuracy of 81 percent and a sensitivity of over 85 percent, both on lists of individually dictated terms and on complete clinical reports written in natural language. These figures showed the tool’s ability to correctly identify relevant clinical terminology and reduce omissions. The linguistic parser proved to be effective in detecting descriptors expressed with grammatical variations, different verb tenses or different syntactic orders, demonstrating flexibility in natural language processing. This performance was particularly evident in texts written or voice-dictated, often found in clinical practice.
As far as voice recognition is concerned, the Web Speech API performed better in terms of transcription fidelity, especially in quiet environments and with speakers with neutral diction. On the other hand, the Julius/Coruja system, although with a slight reduction in the recognition rate, proved to be advantageous as it operates locally, offering greater privacy and independence from external connectivity – characteristics that are relevant to sensitive clinical contexts.
Despite these advances, the solution presents opportunities for further development, particularly in terms of expanding the recognised clinical vocabulary, resilience to less controlled contexts and the integration of more advanced language models. In the short term, pilot studies are planned in healthcare institutions to validate the tool’s impact on clinical practice, especially in the production and standardisation of reports. In the medium term, it is anticipated that the approach will be extended to other areas of imaging and that language models based on machine learning will be incorporated, with a view to improving sensitivity without compromising clinical specificity.
Would you like to know all the details?
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With more than half a million hospital medical consultations carried out in the first half of 2024, the ULS of Coimbra stands out as an institution dedicated to integrated, high-quality and patient-centered healthcare, with 8…
Elderly Care Ecosystem: an Innovative Platform for Personalized and Efficient Services
The Elderly Care Ecosystem (ECE) is an integration of various digital health technologies, exploring the areas of telehealth and predictive analytics. The goal of this ecosystem is to improve the quality of life for elderly…
Innovative technology that subconsciously relieves anxiety through a scarf
The SCAARF technology aims to offer an alternative method to alleviate anxiety symptoms in a non-intrusive and subconscious way. This technology is an innovative idea in the field of digital health and wearable technology, designed…
Digital Health Interventions: Equity in Hypertension Care for Everyone
Nearly half of all adults in the United States have hypertension, one of the leading risk factors for cardiovascular disease, and only about a quarter (24%) of those people have their hypertension under control. Studies…
Personalization and Technology in Diabetes Management
IPDM has significant potential to improve diabetes management and drive health system reforms to become high-performing, effective, equitable, accessible, and sustainable. Evidence and good practices inspire health system transformation. Adopting person-centred approaches like co-creation and…
Negotiations on the European Health Data Space Advance With the Participation of the SPMS
The European Health Data Space will be a common health data sharing system across the European Union. It foresees the use of data for purposes that benefit people and society. It will ensure citizens have…
Secretary of State Margarida Tavares Emphasizes Digital Innovation in Health Promotion
Margarida Tavares spoke at the opening of the conference ” O Digital na promoção contínua da saúde e do bem-estar”, organized by the Associação para a Promoção e Desenvolvimento da Sociedade da Informação (APDSI) and…
ARS Algarve Modernizes Radiology With AI and New Data Center
The radiology service of ARS Algarve has already performed nearly 29,000 exams using Artificial Intelligence (AI) technology. In recent years, there has been a significant investment in image digitization and data storage, as well as…
European Health Data Space: Unified Access To Health Data In The EU
The COVID-19 pandemic highlighted the importance of digital services in health, but complex rules and increasing cyberattacks make it difficult to share data across Member States; the EHDS, based on several regulations, provides tailor-made rules…
European Commission Amends Digital Europe Programme With an Investment of €762.7 Million
The European Commission has amended the Digital Europe Programme work programmes 2023-2024, investing an additional €762.7 million in Europe’s digital transition and cybersecurity. The digital transition is the main work programme with a budget of…
SPMS Integrates the TEF-Health Initiative
SPMS participates in the TEF-Health initiative as a partner in a consortium composed of 51 entities from 9 European Union countries. This action is co-financed by the European Commission and has a duration of five…
FMUP Creates Inhealth Junior Academy for High School Students
The InHealth Junior Academy — Academia Júnior de Inovação em Saúde is an initiative of the Departamento de Medicina da Comunidade, Informação e Decisão em Saúde da Faculdade de Medicina da Universidade do Porto (FMUP)….
SPMS Represents Portugal as Vice-president of GDHP
The GDHP is an intergovernmental organization in the digital health sector that facilitates cooperation and collaboration between government representatives and the World Health Organization (WHO). Its purpose is to foster policymaking that promote the digitalization…
Digital Transformation of Health at INCoDe.2030 in Tomar
The “National Digital Skills Initiative e.2030, Portugal” (INCoDe.2030) is an initiative that aims to improve the Portuguese population’s level of digital skills, placing Portugal at the level of the most advanced European countries in this…
Braga Hospital Evaluates Memory With Interactive Game in Patients With Multiple Sclerosis
Multiple Sclerosis is known as a chronic disease of the central nervous system, with a wide variety of motor and sensory symptoms that can lead to work disability, socioeconomic burden, and reduced quality of life…
Neurosurgery Teleconsultation Wins Innovation Award
The aim of the BI Award for Innovation in Healthcare is to recognize innovative projects in the healthcare sector that improve the quality of life for the Portuguese people. In 2021, the specific theme was…
HealthData@PT: New SPMS Initiative for Health Data
Action HealthData@PT is launched in the context of the implementation of the European Health Data Space, and is an initiative approved by the European Commission under the EU4Health 2021-2027 programme. This initiative contributes to the…
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