Artificial intelligence on the identification of risk groups for osteoporosis, a general review

Abstract

IntroductionThe goal of this paper is to present a critical review on the main systems that use artificial intelligence to identify groups at risk for osteoporosis or fractures. The systems considered for this study were those that fulfilled the following requirements: range of coverage in diagnosis, low cost and capability to identify more significant somatic factors.MethodsA bibliographic research was done in the databases, PubMed, IEEExplorer Latin American and Caribbean Center on Health Sciences Information (LILACS), Medical Literature Analysis and Retrieval System Online (MEDLINE), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Scopus, Web of Science, and Science Direct searching the terms “Neural Network”, “Osteoporosis Machine Learning” and “Osteoporosis Neural Network”. Studies with titles not directly related to the research topic and older data that reported repeated strategies were excluded. The search was carried out with the descriptors in German, Spanish, French, Italian, Mandarin, Portuguese and English; but only studies written in English were found to meet the established criteria. Articles covering the period 2000–2017 were selected; however, articles prior to this period with great relevance were included in this study.DiscussionBased on the collected research, it was identified that there are several methods in the use of artificial intelligence to help the screening of risk groups of osteoporosis or fractures. However, such systems were limited to a specific ethnic group, gender or age. For future research, new challenges are presented.ConclusionsIt is necessary to develop research with the unification of different databases and grouping of the various attributes and clinical factors, in order to reach a greater comprehensiveness in the identification of risk groups of osteoporosis. For this purpose, the use of any predictive tool should be performed in different populations with greater participation of male patients and inclusion of a larger age range for the ones involved. The biggest challenge is to deal with all the data complexity generated by this unification, developing evidence-based standards for the evaluation of the most significant risk factors.

Tekoälyn hyödyntäminen terveydenhuollossa terveysriskien ja riskitekijöiden tunnistamiseksi ja ennustamiseksi

“…Tekoälyn menetelmiä on hyödynnetty esimerkiksi aivoaneurysmaan [17], osteoporoosiin [18], mielenterveyden häiriöihin [19,20], dementiaan [21], runsaaseen virtsahappopitoisuuteen [22] ja sydänsairauksiin [23][24][25] liittyvien riskitekijöiden tunnistamiseksi. Lisäksi tekoälyn menetelmiä on hyödynnetty yleisemmin sairauksiin liittyvien riskitekijöiden [26] ja terveydentilojen tunnistamiseksi [27,28].…”

“…Potilastietojärjestelmissä ja muissa lähdejärjestelmissä olevaa tietoa on hyödynnetty mahdollisuuksien mukaan rakenteisessa muodossa. Rakenteisessa muodossa olevia tietoja on tutkimuksia varten poimittu erilaisista mittaustuloksista [18,22,24,26] ja kyselyistä [18,20]. Rakenteista tietoa on saatu myös lääkityksistä [23,27], diagnooseista [17,19,21,24,27] ja toimenpiteistä [17,27].…”

“…FinJeHeW 2019;11(3) 205 Laadittujen tunnistamisen ja ennustamisen mallien arviointituloksissa oli eroja. Sekä Fordin tutkimusryhmän [27], että Cruzin tutkimusryhmän [18] kirjallisuuskatsauksissa vertailtiin tarkastelun kohteena olevien riskitekijöiden tunnistamiseksi käytettyjen menetelmien välisiä eroja, ja jonkin verran eroavaisuuksia mallien välillä oli tunnistettavissa. Tutkimuksissa oli erilaisia menetelmiä käytetty myös rinnakkain, pyrkimyksenä vertailla tuloksia ja siten luoda mahdollisimman hyvä malli riskitekijöiden tai riskiryhmien tunnistamiseksi.…”

How Bioethics Can Shape Artificial Intelligence and Machine Learning

“…Preliminary investigations using AI and machine learning applications to supplement medical decision‐making have shown promising results. A recent literature review revealed that AI applications are able to correctly identify populations at risk for osteoporosis, and a study revealed that machine learning applications were able to outperform expert pathologists in detecting lymph node metastases in women who suffer from breast cancer . For the purpose of this study, the authors used a “deep‐learning” technique to construct these machine learning systems.…”

“…As Alex London recently noted in this journal, these systems confront us with "the prospect of machines encroaching on realms of decision-making revered as the province of expert professionals." 1 Designing machine learning-based decision support systems is not a merely technical challenge. It also requires attention to bioethical principles.…”